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\end{verbatim} \subsubsection{Arguments} \begin{itemize} \item \texttt{object}: the output from {\tt matchit()}. \item \texttt{interactions}: an option to calculate summary statistics in \texttt{sum.all} and \texttt{sum.matched} for all covariates, their squares, and two-way interactions when \texttt{interactions = TRUE} and only the covariates themselves when \texttt{interactions = FALSE}, (DEFAULT = {\tt FALSE}). \item \texttt{addlvariables}: additional variables on which to calculate the diagnostic statistics (in addition to the variables included in the matching procedure) (DEFAULT = {\tt NULL}). \texttt{addlvariables}: a data frame containing additional variables whose balance is examined. The data should come with the same number of units and units in the same order as in the data set used for {\tt matchit()}. \item \texttt{standardize}: a logical variable indicating whether to standardize balance measures, i.e., whether the difference in means should be divided by the standard deviation in the original treated group. (DEFAULT = {\tt FALSE}) \end{itemize} \subsubsection{Output Values} The output from the \texttt{summary()} command includes the following elements, when applicable: \begin{itemize} \item The original assignment model call. \item \texttt{sum.all}: a data frame that contains variable names and interactions down the row names, and summary statistics on \emph{all observations} in each of the columns. The columns in \texttt{sum.all} contain: %\footnote{The output for full matching is % slightly different from that described here; see Section % \ref{subsubsec:full} for details.} \begin{itemize} \item means of all covariates $X$ for treated and control units, where \texttt{Means Treated}$= \mu_{X|T=1} = \frac{1}{n_1} \sum_{T=1} X_i$ and \texttt{Means Control}$= \mu_{X|T=0} = \frac{1}{n_0} \sum_{T=0} X_i$, \item standard deviation in the control group for all covariates $X$, where applicable, $$\quad s_{x|T=0} = \sqrt{\frac{\sum_{i \in \{i: T_i=0\}} (X_i - \mu_{X|T=0})^2}{n_0-1} }.$$ \item balance statistics of the original data (before matching), which compare treated and control covariate distributions. If {\tt standardize = FALSE}, balance measures will be presented on the original scale. Specifically, mean differences (\texttt{Mean Diff.}) as well as the median, mean, and maximum value of differences in empirical quantile functions for each covariate will be given (\texttt{eQQ Med}, \texttt{eQQ Mean}, and \texttt{eQQ Max}, respectively). If {\tt standardize = TRUE}, the balance measures will be standardized. Standardized mean differences (\texttt{Std.\ Mean Diff.}), defined as $\frac{\mu_{X|T=1} - \mu_{X|T=0}}{s_{x|T=1}}$, as well as the median, mean, and maximum value of differences in empirical cumulative distribution functions for each covariate will be given (\texttt{eCDF Med}, \texttt{eCDF Mean}, and \texttt{eCDF Max}, respectively). \end{itemize} \item \texttt{sum.matched}: a data frame which contains variable names down the row names, and summary statistics on only the \emph{matched observations} in each of the columns. Specifically, the columns in \texttt{sum.matched} contain the following elements: %\footnote{The % values output for full matching are slightly different from that % described here; see Section \ref{subsubsec:full} for details}: \begin{itemize} \item weighted means for matched treatment units and matched control units of all covariates $X$ and their interactions, where \texttt{Means Treated}$= \mu_{wX|T=1} = \frac{1}{n_1} \sum_{T=1} w_iX_i$ and \texttt{Means Control}$=\mu_{wX|T=0} = \frac{1}{n_0} \sum_{T=0} w_iX_i$, \item weighted standard deviations in the matched control group for all covariates $X$, where applicable, where \texttt{SD} $= s_{wX} = \sqrt{\frac{1}{n} \sum_{i} (w_iX_i - \overline{X}^*)^2}$, where $\overline{X}^*$ is the weighted mean of $X$ in the matched control group, and \item balance statistics of the matched data (after matching), which compare treated and control covariate distributions. If {\tt standardize = FALSE}, balance measures will be presented on the original scale. Specifically, mean differences (\texttt{Mean Diff.}) as well as the median, mean, and maximum value of differences in empirical quantile functions for each covariate will be given (\texttt{eQQ Med}, \texttt{eQQ Mean}, and \texttt{eQQ Max}, respectively). If {\tt standardize = TRUE}, the balance measures will be standardized. Standardized mean differences (\texttt{Std.\ Mean Diff.}), defined as $\frac{\mu_{wX|T=1} - \mu_{wX|T=0}}{s_{x|T=1}}$, as well as the median, mean, and maximum value of differences in empirical cumulative distribution functions for each covariate will be given (\texttt{eCDF Med}, \texttt{eCDF Mean}, and \texttt{eCDF Max}, respectively). \end{itemize} where $w$ represents the vector of \texttt{weights}. \item \texttt{reduction}: the percent reduction in the difference in means achieved in each of the balance measures in \texttt{sum.all} and \texttt{sum.matched}, defined as $100(|a|-|b|)/|a|$, where $a$ was the value of the balance measure before matching and $b$ is the value of the balance measure after matching. \item \texttt{nn}: the sample sizes in the full and matched samples and the number of discarded units, by treatment and control. \item \texttt{q.table}: an array that contains the same information as \texttt{sum.matched} by subclass. \item \texttt{qn}: the sample sizes in the full and matched samples and the number of discarded units, by subclass and by treatment and control. \item \texttt{match.matrix}: the same object is contained in the output of {\tt matchit()}. \end{itemize} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/preprocess.tex0000644000176200001440000002520212162551623016526 0ustar liggesusers\section{Preprocessing via Matching} \label{sec:matching} \subsection{Quick Overview} The main command \texttt{matchit()} implements the matching procedures. A general syntax is: \begin{verbatim} > m.out <- matchit(treat ~ x1 + x2, data = mydata) \end{verbatim} where {\tt treat} is the dichotomous treatment variable, and {\tt x1} and {\tt x2} are pre-treatment covariates, all of which are contained in the data frame {\tt mydata}. The dependent variable (or variables) may be included in \texttt{mydata} for convenience but is never used by \MatchIt\ or included in the formula. This command creates the \MatchIt\ object called \texttt{m.out}. Name the output object to see a quick summary of the results: \begin{verbatim} > m.out \end{verbatim} \subsection{Examples} To run any of the examples below, you first must load the library and and data: \begin{verbatim} > library(MatchIt) > data(lalonde) \end{verbatim} Our example data set is a subset of the job training program analyzed in \citet{lalonde86} and \citet{DehWah99}. \MatchIt\ includes a subsample of the original data consisting of the National Supported Work Demonstration (NSW) treated group and the comparison sample from the Population Survey of Income Dynamics (PSID).\footnote{This data set, \texttt{lalonde}, was created using NSWRE74$\_$TREATED.TXT and CPS3$\_$CONTROLS.TXT from http://www.columbia.edu/$\sim$rd247/nswdata.} The variables in this data set include participation in the job training program (\texttt{treat}, which is equal to 1 if participated in the program, and 0 otherwise), age ({\tt age}), years of education ({\tt educ}), race (\texttt{black} which is equal to 1 if black, and 0 otherwise; \texttt{hispan} which is equal to 1 if hispanic, and 0 otherwise), marital status (\texttt{married}, which is equal to 1 if married, 0 otherwise), high school degree (\texttt{nodegree}, which is equal to 1 if no degree, 0 otherwise), 1974 real earnings (\texttt{re74}), 1975 real earnings (\texttt{re75}), and the main outcome variable, 1978 real earnings (\texttt{re78}). \subsubsection{Exact Matching} \label{subsubsec:exact} The simplest version of matching is exact. This technique matches \emph{each} treated unit to \emph{all} possible control units with exactly the same values on all the covariates, forming subclasses such that within each subclass all units (treatment and control) have the same covariate values. Exact matching is implemented in \MatchIt\ using \texttt{method = "exact"}. Exact matching will be done on all covariates included on the right-hand side of the \texttt{formula} specified in the \MatchIt\ call. There are no additional options for exact matching. (Exact restrictions on a subset of covariates can also be specified in nearest neighbor matching; see Section~\ref{subsubsec:nearest}.) The following example can be run by typing {\tt demo(exact)} at the R prompt, \begin{verbatim} > m.out <- matchit(treat ~ educ + black + hispan, data = lalonde, method = "exact") \end{verbatim} \subsubsection{Subclassification} \label{subsubsec:subclass} When there are many covariates (or some covariates can take a large number of values), finding sufficient exact matches will often be impossible. The goal of subclassification is to form subclasses, such that in each the distribution (rather than the exact values) of covariates for the treated and control groups are as similar as possible. Various subclassification schemes exist, including the one based on a scalar distance measure such as the propensity score estimated using the \texttt{distance} option (see Section~\ref{subsubsec:inputs-all}). Subclassification is implemented in \MatchIt\ using \texttt{method = "subclass"}. The following example script can be run by typing {\tt demo(subclass)} at the R prompt, \begin{verbatim} > m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "subclass") \end{verbatim} The above syntax forms 6 subclasses, which is the default number of subclasses, based on a distance measure (the propensity score) estimated using logistic regression. By default, each subclass will have approximately the same number of treated units. Subclassification may also be used in conjunction with nearest neighbor matching described below, by leaving the default of \texttt{method = "nearest"} but adding the option \texttt{subclass}. When you choose this option, \MatchIt\ selects matches using nearest neighbor matching, but after the nearest neighbor matches are chosen it places them into subclasses, and adds a variable to the output object indicating subclass membership. \subsubsection{Nearest Neighbor Matching} \label{subsubsec:nearest} Nearest neighbor matching selects the $r$ (default=1) best control matches for each individual in the treatment group (excluding those discarded using the \texttt{discard} option). Matching is done using a distance measure specified by the {\tt distance} option (default=logit). Matches are chosen for each treated unit one at a time, with the order specified by the \texttt{m.order} command (default=largest to smallest). At each matching step we choose the control unit that is not yet matched but is closest to the treated unit on the distance measure. Nearest neighbor matching is implemented in \MatchIt\ using the \texttt{method = "nearest"} option. The following example script can be run by typing {\tt demo(nearest)}: \begin{verbatim} > m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "nearest") \end{verbatim} \subsubsection{Optimal Matching} \label{subsubsec:optimal} The default nearest neighbor matching method in \MatchIt\ is ``greedy'' matching, where the closest control match for each treated unit is chosen one at a time, without trying to minimize a global distance measure. In contrast, ``optimal'' matching finds the matched samples with the smallest average absolute distance across all the matched pairs. \citet{GuRos93} find that greedy and optimal matching approaches generally choose the same sets of controls for the overall matched samples, but optimal matching does a better job of minimizing the distance within each pair. In addition, optimal matching can be helpful when there are not many appropriate control matches for the treated units. Optimal matching is performed with \MatchIt\ by setting \texttt{method = "optimal"}, which automatically loads an add-on package called \texttt{optmatch} \citep{Hansen04}. The following example can also be run by typing {\tt demo(optimal)} at the R prompt. We conduct 2:1 optimal ratio matching based on the propensity score from the logistic regression. \begin{verbatim} > m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "optimal", ratio = 2) \end{verbatim} \subsubsection{Full Matching} \label{subsubsec:full} Full matching is a particular type of subclassification that forms the subclasses in an optimal way \citep{Rosenbaum02, Hansen04}. A fully matched sample is composed of matched sets, where each matched set contains one treated unit and one or more controls (or one control unit and one or more treated units). As with subclassification, the only units not placed into a subclass will be those discarded (if a \texttt{discard} option is specified) because they are outside the range of common support. Full matching is optimal in terms of minimizing a weighted average of the estimated distance measure between each treated subject and each control subject within each subclass. Full matching can be performed with \MatchIt\ by setting \texttt{method = "full"}. Just as with optimal matching, we use the \texttt{optmatch} package \citep{Hansen04}, which automatically loads when needed. The following example with full matching (using the default propensity score based on logistic regression) can also be run by typing {\tt demo(full)} at the R prompt: \begin{verbatim} > m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "full") \end{verbatim} \subsubsection{Genetic Matching} \label{subsub:genetic} Genetic matching automates the process of finding a good matching solution \citep{DiaSek05}. The idea is to use a genetic search algorithm to find a set of weights for each covariate such that the a version of optimal balance is achieved after matching. As currently implemented, matching is done with replacement using the matching method of \citet{AbaImb07} and balance is determined by two univariate tests, paired t-tests for dichotomous variables and a Kolmogorov-Smirnov test for multinomial and continuous variables, but these options can be changed. Genetic matching can be performed with \MatchIt\ by setting \texttt{method = "genetic"}, which automatically loads the \texttt{Matching} \citep{Sekhon04} package. The following example of genetic matching (using the estimated propensity score based on logistic regression as one of the covariates) can also be run by typing {\tt demo(genetic)}: \begin{verbatim} > m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "genetic") \end{verbatim} \subsubsection{Coarsened Exact Matching} \label{subsub:cem} Coarsened Exact Matching (CEM) is a Monotonoic Imbalance Bounding (MIB) matching method --- which means that the balance between the treated and control groups is chosen by the user ex ante rather than discovered through the usual laborious process of checking after the fact and repeatedly reestimating, and so that adjusting the imbalance on one variable has no effect on the maximum imbalance of any other. CEM also strictly bounds through ex ante user choice both the degree of model dependence and the average treatment effect estimation error, eliminates the need for a separate procedure to restrict data to common empirical support, meets the congruence principle, is robust to measurement error, works well with multiple imputation methods for missing data, and is extremely fast computationally even with very large data sets. CEM also works well for multicategory treatments, determining blocks in experimental designs, and evaluating extreme counterfactuals \citep{IacKinPor08}. CEM can be performed with \MatchIt\ by setting \texttt{method = "cem"}, which automatically loads the \texttt{cem} package. The following examples of CEM (with automatic coarsening) can also be run by typing \texttt{demo(cem)}: \begin{verbatim} m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "cem") \end{verbatim} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/plotref.tex0000644000176200001440000000762012162551623016020 0ustar liggesusers\section{\texttt{plot()}: Graphical Summaries of Balance} \subsection{Plot options for the matchit object} The \texttt{plot()} command allows you to check the distributions of propensity scores and covariates in the assignment model, squares, and interactions, and within each subclasses if specified. \subsubsection{Syntax} \begin{verbatim} > plot(m.out, discrete.cutoff = 5, type = "QQ", numdraws = 5000, interactive = TRUE, which.xs = NULL, ...) \end{verbatim} \subsubsection{Arguments} \begin{itemize} \item {\tt type}: type of output graph. \texttt{type = "QQ"} (default) outputs empirical quantile-quantile plots of each covariate to check balance of marginal distributions. Alternatively, \texttt{type = "jitter"} outputs jitter plots of the propensity score for treated and control units. Finally, \texttt{type="hist"} outputs histograms of the propensity score in the original treated and control groups and weighted histograms of the propensity score in the matched treated and control groups. \item {\tt discrete.cutoff}: For quantile-quantile plots, discrete covariates that take 5 or fewer values are jittered for visibility. This may be changed by setting this argument to any other positive integer. \item {\tt interactive}: If \texttt{TRUE} (default), users can identify individual units by clicking on the graph with the left mouse button, and (when applicable) choose subclasses to plot. \item {\tt which.xs}: For quantitle-quantile plots, specifies particular covariate names in a character vector to plot only a subset of the covariates. \item {\tt subclass}: If \texttt{interactive = FALSE}, users can specify which subclass to plot. \end{itemize} \subsubsection{Output Values} \begin{itemize} \item Empirical quantile-quantile plot: This graph plots covariate values that fall in (approximately) the same quantile of treated and control distributions. Control unit quantile values are plotted on the x-axis, and treated unit quantile values are plotted on the y-axis. If values fall below the 45 degree line, control units generally take lower values of the covariate. Data points that fall exactly on the 45 degree line indicate that the marginal distributions are identical. \item Jitter plots: This graph plots jittered estimated propensity scores of treated and control units. Dark diamonds indicate matched units and grey diamonds indicate unmatched or discarded units. The area of the diamond is proportional to the weights. Vertical lines are plotted if subclassification is used. \item Histograms: This graph plots histograms of the estimated propensity scores in the original treated and control groups and weighted histograms of the estimated propensity scores in the matched treated and control groups. Plots can be compared vertically to quickly check the balance before and after matching. \end{itemize} \subsection{Plot options for the matchit summary object} You can also send a matchit summary object to the \texttt{plot()} command, to obtain a summary of the balance on each covariate before and after matching. The summary() object must have been created using the option \texttt{standardize=TRUE}. The idea for this plot came from the ``twang" package by McCaffrey, Ridgeway, and Morral. \subsubsection{Syntax} \begin{verbatim} > s.out <- summary(object, standardize=TRUE, ...) > plot(s.out, ...) \end{verbatim} \subsubsection{Arguments} \begin{itemize} \item {\tt interactive}: If \texttt{TRUE} (default), users can identify individual variables by clicking on the graph with the left mouse button. \end{itemize} \subsubsection{Output Values} \begin{itemize} \item Line plot of standardized differences in means before and after matching. Numbers plotted are those output by the summary() command in the sum.all and sum.matched objects. \end{itemize} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/overview.tex0000644000176200001440000002603312162551623016212 0ustar liggesusers \MatchIt\ is designed for causal inference with a dichotomous treatment variable and a set of pretreatment control variables. Any number or type of dependent variables can be used. (If you are interested in the causal effect of more than one variable in your data set, run \MatchIt\ separately for each one; it is unlikely in any event that any one parametric model will produce valid causal inferences for more than one treatment variable at a time.) \MatchIt\ can be used for other types of causal variables by dichotomizing them, perhaps in multiple ways \citep[see also][]{ImaDyk04}. \MatchIt\ works for experimental data, but is usually used for observational studies where the treatment variable is not randomly assigned by the investigator, or the random assignment goes awry. We adopt the same notation as in \citet*{HoImaKin07}. Unless otherwise noted, let $i$ index the $n$ units in the data set, $n_1$ denote the number of treated units, $n_0$ denote the number of control units (such that $n=n_0+n_1$), and $x_i$ indicate a vector of pretreatment (or control) variables for unit $i$. Let $t_i=1$ when unit $i$ is assigned treatment, and $t_i=0$ when unit $i$ is assigned control. (The labels ``treatment'' and ``control'' and values 1 and 0 respectively are arbitrary and can be switched for convenience, except that some methods of matching are keyed to the definition of the treated group.) Denote $y_i(1)$ as the potential outcome of unit $i$ under treatment --- the value the outcome variable would take if $t_i$ were equal to 1, whether or not $t_i$ in fact is 0 or 1 -- and $y_i(0)$ the potential outcome of unit $i$ under control --- the value the outcome variable would take if $t_i$ were equal to 0, regardless of its value in fact. The variables $y_i(1)$ and $y_i(0)$ are jointly unobservable, and for each $i$, we observe one $y_i=t_iy_i(1)+(1-t_i)y_i(0)$, and not the other. Also denote a fixed vector of exogenous, pretreatment measured confounders as $X_i$. These variables are defined in the hope or under the assumption that conditioning on them appropriately will make inferences ignorable. Measures of balance should be computed with respect to all of $X$, even if some methods of matching only use some components. \section{Preprocessing via Matching} If $t_i$ and $X_i$ were independent, we would not need to control for $X_i$, and any parametric analysis would effectively reduce to a difference in means of $Y$ for the treated and control groups. The goal of matching is to preprocess the data prior to the parametric analysis so that the actual relationship between $t_i$ and $X_i$ is eliminated or reduced without introducing bias and or increasing inefficiency too much. When matching we select, duplicate, or selectively drop observations from our data, and we do so without inducing bias as long as we use a rule that is a function only of $t_i$ and $X_i$ and does not depend on the outcome variable $Y_i$. Many methods that offer this preprocessing are included here, including exact, subclassification, nearest neighbor, optimal, and genetic matching. For many of these methods the propensity score--defined as the probability of receiving the treatment given the covariates--is a key tool. In order to avoid changing the quantity of interest, most \MatchIt\ routines work by retaining all treated units and selecting (or weighting) control units to include in the final data set; this enables one to estimate the average treatment effect on the treated (the purpose of which is described in Section \ref{s:qoi}). \MatchIt\ implements and evaluates the choice of the rules for matching. Matching sometimes increases efficiency by eliminating heterogeneity or deleting observations outside of an area where a model can reasonably be used to extrapolate, but one needs to be careful not to lose too many observations in matching or efficiency will drop more than the reduction in bias that is achieved. The simplest way to obtain good matches (as defined above) is to use one-to-one exact matching, which pairs each treated unit with one control unit for which the values of $X_i$ are identical. However, with many covariates and finite numbers of potential matches, sufficient exact matches often cannot be found. Indeed, many of the other methods implemented in \MatchIt\ attempt to balance the overall covariate distributions as much as possible, when sufficient one-to-one exact matches are not available. A key point in \citet*{HoImaKin07} is that matching methods by themselves are not methods of estimation: Every use of matching in the literature involves an analysis step following the matching procedure, but almost all analyses use a simple difference in means. This procedure is appropriate only if exact matching was conducted. In almost all other cases, some adjustment is required, and there is no reason to degrade your inferences by using an inferior method of analysis such as a difference in means even when improving your inferences via preprocessing. Thus, with \MatchIt, you can improve your analyses in two ways. \MatchIt\ analyses are ``doubly robust'' in that if \emph{either} the matching analysis \emph{or} the analysis model is correct (but not necessarily both) your inferences will be statistically consistent. In practice, the modeling choices you make at the analysis stage will be much less consequential if you match first. \section{Checking Balance} \label{sec:balance-sum} The goal of matching is to create a data set that looks closer to one that would result from a perfectly blocked (and possibly randomized) experiment. When we get close, we break the link between the treatment variable and the pretreatment controls, which makes the parametric form of the analysis model less relevant or irrelevant entirely. To break this link, we need the distribution of covariates to be the same within the matched treated and control groups. A crucial part of any matching procedure is, therefore, to assess how close the (empirical) covariate distributions are in the two groups, which is known as ``balance.'' Because the outcome variable is not used in the matching procedure, any number of matching methods can be tried and evaluated, and the one matching procedure that leads to the best balance can be chosen. \MatchIt\ provides a number of ways to assess the balance of covariates after matching, including numerical summaries such as the ``mean Diff.'' (difference in means) or the difference in means divided by the treated group standard deviation, and summaries based on quantile-quantile plots that compare the empirical distributions of each covariate. The widely used procedure of doing t-tests of the difference in means is highly misleading and should never be used to assess balance; see \citet{ImaKinStu08}. These balance diagnostics should be performed on all variables in $X$, even if some are excluded from one of the matching procedures. \section{Conducting Analyses after Matching}\label{s:qoi} The most common way that parametric analyses are used to compute quantities of interest (without matching) is by (statistically) holding constant some explanatory variables, changing others, and computing predicted or expected values and taking the difference or ratio, all by using the parametric functional form. In the case of causal inference, this would mean looking at the effect on the expected value of the outcome variable when changing $T$ from 0 to 1, while holding constant the pretreatment control variables $X$ at their means or medians. This, and indeed any other appropriate analysis procedure, would be a perfectly reasonable way to proceed with analysis after matching. If it is the chosen way to proceed, then either treated or control units may be deleted during the matching stage, since the same parametric structure is assumed to apply to all observations. In other instances, researchers wish to reduce the assumptions inherent in their statistical model and so want to allow for the possibility that their treatment effect to vary over observations. In this situation, one popular quantity of interest used is the \emph{average treatment effect on the treated} (ATT). For example, for the treated group, the potential outcomes under control, $Y_i(0)$, are missing, whereas the outcomes under treatment, $Y_i(1)$, are observed, and the goal of the analysis is to impute the missing outcomes, $Y_i(0)$ for observations with $T_i=1$. We do this via simulation using a parametric statistical model such as regression, logit, or others (as described below). Once those potential outcomes are imputed from the model, the estimate of individual $i$'s treatment effect is $Y_i(1)-\widehat{Y}_i(0)$ where $\widehat{Y}_i(0)$ is a predicted value of the dependent variable for unit $i$ under the counterfactual condition where $T_i=0$. The in-sample average treatment effect for the treated individuals can then be obtained by averaging this difference over all observations $i$ where in fact $T_i=1$. Most \MatchIt\ algorithms retain all treated units, and choose some subset of or repeated units from the control group, so that estimating the ATT is straightforward. If one chooses options that allow matching with replacement, or any solution that has different numbers of controls (or treateds) within each subclass or strata (such as full matching), then the parametric analysis following matching must accomodate these procedures, such as by using fixed effects or weights, as appropriate. (Similar procedures can also be used to estimate various other quantities of interest such as the average treatment effect by computing it for all observations, but then one must be aware that the quantity of interest may change during the matching procedure as some control units may be dropped.) The imputation from the model can be done in at least two ways. Recall that the model is used to impute \emph{the value that the outcome variable would take among the treated units if those treated units were actually controls}. Thus, one reasonable approach would be to fit a model to the matched data and create simulated predicted values of the dependent variable for the treated units with $T_i$ switched counterfactually from 1 to 0. An alternative approach would be to fit a model without $T$ by using only the outcomes of the matched control units (i.e., using only observations where $T_i=0$). Then, given this fitted model, the missing outcomes $Y_i(0)$ are imputed for the matched treated units by using the values of the explanatory variables for the treated units. The first approach will usually have lower variance, since all observations are used, and the second may have less bias, since no assumption of constant parameters across the models of the potential outcomes under treatment and control is needed. See \citet*{HoImaKin07} for more details. Other quantities of interest can also be computed at the parametric stage, following any procedures you would have followed in the absence of matching. The advantage is that if matching is done well your answers will be more robust to many small changes in parametric specification. %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/notation.tex0000644000176200001440000001736712162551623016211 0ustar liggesusers\documentclass[oneside,letterpaper,titlepage,12pt]{article} %\usepackage[ae,hyper]{/usr/lib/R/share/texmf/Rd} \usepackage{makeidx} \usepackage{graphicx} \usepackage{natbib} \usepackage[reqno]{amsmath} \usepackage{amssymb} \usepackage{verbatim} \usepackage{epsf} \usepackage{url} \usepackage{html} \usepackage{dcolumn} \usepackage{longtable} \usepackage{vmargin} \setpapersize{USletter} \newcolumntype{.}{D{.}{.}{-1}} \newcolumntype{d}[1]{D{.}{.}{#1}} %\pagestyle{myheadings} \htmladdtonavigation{ \htmladdnormallink{% \htmladdimg{http://gking.harvard.edu/pics/home.gif}} {http://gking.harvard.edu/}} \newcommand{\hlink}{\htmladdnormallink} \bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.setcounter"} \setcounter{tocdepth}{3} \parindent=0cm \newcommand{\MatchIt}{\textsc{MatchIt}} \begin{document} \begin{center} Notation for \MatchIt \\ Elizabeth Stuart \\ \end{center} This document details the notation to be used in the matching paper to accompany \MatchIt, as discussed in conference call on April 27, 2004 and emails on April 27-29, 2004. \\ We first provide a general idea of the notation and ideas. Formal notation follows. \begin{enumerate} \item There exists in nature fixed values, $\theta_{1i}$ and $\theta_{0i}$, the potential outcomes under treatment and control, respectively. They, or more usually their difference, are our quantities of interest and hence the inferential target in this exercise. They are not generally known. There also exist in nature a vector of fixed values $X_i$ that are known and will play the role of covariates to condition on. Note that the use of $X_i$ in matching assumes that they are not affected by treatment assignment: $X_{1i}=X_{0i}=X_i$. If a researcher is interested in adjusting for a variable potentially affected by treatment assignment, then methods such as principal stratification should be used. [We can write a paragraph on that somewhere.] \item Then (i.e., only after step 1) $T_i$ is created, preferably by the experimenter, who assigns its values randomly, but in some cases $T_i$ is created by the world and we hope that it was created effectively randomly (or randomly conditional on the observed $X_i$). This is what is known as the assumption of unconfounded treatment assignment: That treatment assignment is independent of the potential outcomes given the covariates $X_i$. \item Finally, the observed outcome variable $Y_i$ is calculated deterministically as $Y_i = \theta_{1i}T_i + \theta_{0i}(1-T_i)$. This is an accounting identity, not a regression equation with an error term; i.e., it is just true, not really an assumption. \end{enumerate} The accounting identity in 3 implies that we observe $\theta_{1i}=Y_i$ (but not $\theta_{0i}$) when $T_i=1$, and we observe $\theta_{0i}=Y_i$ (but not $\theta_{1i}$) when $T_i=0$. Since the fundamental problem of causal inference indicates that either $\theta_{0i}$ or $\theta_{1i}$ will be unobserved for each $i$, we will need to infer their values, for which we define $\tilde\theta_{1i} \sim p(\theta_{1i})$ and $\tilde\theta_{0i} \sim p(\theta_{0i})$ as draws of the potential outcomes under treatment and control, respectively, from their respective predictive posterior distributions.\\ We now detail the notation utilized. Fixed, but sometimes unknown values are represented by Greek letters. Draws from the posterior distribution of these unknown values are represented by a tilde over the variable of interest. Fixed, but always known values are represented by capital Roman letters. We first define notation for individual $i$: \begin{itemize} \item $\theta_{1i}=$ individual $i$'s potential outcome under treatment \item $\theta_{0i}=$ individual $i$'s potential outcome under control \item $T_i=$ individual $i$'s observed treatment assignment \begin{itemize} \item $T_i=1$ means individual $i$ receives treatment \item $T_i=0$ means individual $i$ receives control \end{itemize} \item $\theta_{0i}$ and $\theta_{1i}$ are considered fixed quantities. Which one is observed depends on the random variable $T_i$. \item $Y_i=T_i \theta_{1i} + (1-T_i) \theta_{0i}$ is individual $i$'s observed outcome \item Let $Y_{1i}=(\theta_{1i}|T_i=1)$. Similarly, $Y_{0i}=(\theta_{0i}|T_i=0)$. One of these is observed for individual $i$. [Do we actually want this notation? It's not quite right since the capital Roman letter would imply that $Y_{1i}$ is always observed, but the notation may be handy (see below).] \item Let $\tilde{\theta}_{0i}$ be a draw from the posterior distribution of $\theta_{0i}$: $\tilde{\theta}_{0i} \sim p(\theta_{0i})$. Similarly, let $\tilde{\theta}_{1i}$ be a draw from the posterior distribution of $\theta_{1i}$: $\tilde{\theta}_{1i} \sim p(\theta_{1i})$. \item Consider variables $X_i$. If $X_{0i}=X_{1i}=X_i$ then $X$ is a ``proper covariate'' in that it is not affected by treatment assignment. Only proper covariates should be used in the matching procedure. That $X_i$ is not affected by treatment assignment is always an assumption (sometimes more reasonable than other times)--we never observe $X_{0i}$ and $X_{1i}$ for individual $i$. \end{itemize} For individual $i$, the potentially observed values can be represented by the following 2x2 table: \begin{center} \begin{tabular}{cc|c|c|} \multicolumn{4}{c}{\hspace*{4cm} Actual treatment assignment} \\ \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{Control \hfill Treatment} \\ \cline{3-4} & & & \phantom{abcd} \\ & Control & $\theta_{0i}$, $X_i$ & $\tilde{\theta}_{0i}$, $X_i$ \\ Potential & & & \phantom{abcd} \\ \cline{3-4} outcomes under & & & \phantom{abcd} \\ & Treatment & $\tilde{\theta}_{1i}$, $X_i$ & $\theta_{1i}$, $X_i$ \\ & & & \phantom{abcd} \\ \cline{3-4} \end{tabular} \end{center} For each individual, we are in either Column 1 or Column 2 (depending on treatment assignment). Within each column, 1 number will be observed and 1 will be a drawn value from the posterior distribution (i.e., the true value for that cell is missing). Thus, for each individual we are in the situation of one of the following two vectors of potential outcomes: \begin{center} \begin{tabular}{rl} If $T_i=1$: & $(\tilde\theta_{0i}, \theta_{1i})$ \\ If $T_i=0$: & $(\theta_{0i}, \tilde\theta_{1i})$ \\ \end{tabular} \end{center} Now consider $n$ individuals observed, with $n_1$ in the treated group and $n_0$ in the control group ($n=n_0+n_1$). Then we have the following notation: \begin{itemize} \item $n_1=\sum_{i=1}^n T_i$, $n_0=\sum_{i=1}^n (1-T_i)$ \item ${\bf Y_0} = \{Y_{i}|T_i=0\}$. ${\bf Y_0}$ is of length $n_0$. \item ${\bf Y_1} = \{Y_{i}|T_i=1\}$. ${\bf Y_1}$ is of length $n_1$. \item ${\bf Y} = \{ {\bf Y_0}, {\bf Y_1}\}$. ${\bf Y}$ is of length $n$. \item $\overline{Y}_1 = \frac{\sum_{i=1}^n T_i \theta_{1i}}{\sum_{i=1}^n T_i}=\frac{\sum_{i=1}^{n_1} Y_{1i}}{n_1}$ \item $\overline{Y}_0 = \frac{\sum_{i=1}^n (1-T_i) \theta_{0i}}{\sum_{i=1}^n (1-T_i)}=\frac{\sum_{i=1}^{n_0} Y_{0i}}{n_0}$ \item Observed sample variances would be calculated in a similar way (using ${\bf Y_0}$ and ${\bf Y_1}$). \end{itemize} Throughout, we will use $p()$ to represent pdf's and $g()$ for functional forms of models. One point to make sure we mention in the write-up is to say what OLS assumes. OLS with a treatment indicator does: $$Y_i|X_i, T_i \sim N(\alpha + \beta_0 T_i + {\boldsymbol \beta} {\bf X_i}, \sigma^2)$$ which assumes that the models of the potential outcomes follow parallel lines with equal residual variance: $$\theta_{1i}|X_i \sim N(\alpha + \beta_0 + {\boldsymbol \beta} {\bf X_i}, \sigma^2)$$ $$\theta_{0i}|X_i \sim N(\alpha + {\boldsymbol \beta} {\bf X_i}, \sigma^2).$$ We can also have a graphical representation of this, perhaps with some examples of outcomes that clearly don't have that nice parallel linear relationship, and show that this would be a very special case of what we're advocating. \end{document} MatchIt/vignettes/mdataref.tex0000644000176200001440000000660412162551623016131 0ustar liggesusers\section{\texttt{match.data()}: Extracting the Matched Data Set} \label{sec:match.data} \subsection{Usage} To extract the matched data set for subsequent analyses from the output object (see Section~\ref{sec:analysis}), we provide the function {\tt match.data()}. This is used as follows: \begin{verbatim} > m.data <- match.data(object, group = "all", distance = "distance", weights = "weights", subclass = "subclass") \end{verbatim} The output of the function {\tt match.data()} is the original data frame where additional information about matching (i.e., distance measure as well as resulting weights and subclasses) is added, restricted to units that were matched. \subsection{Arguments} {\tt match.data()} takes the following inputs: \begin{enumerate} \item {\tt object} is the output object from {\tt matchit()}. This is a required input. \item {\tt group} specifies for which matched group the user wants to extract the data. Available options are {\tt "all"} (all matched units), {\tt "treat"} (matched units in the treatment group), and {\tt "control"} (matched units in the control group). The default is {\tt "all"}. \item {\tt distance} specifies the variable name used to store the distance measure. The default is {\tt "distance"}. \item {\tt weights} specifies the variable name used to store the resulting weights from matching. The default is {\tt "weights"}. See Section~\ref{subsec:weights} for more details on the weights. \item {\tt subclass} specifies the variable name used to store the subclass indicator. The default is {\tt "subclass"}. \end{enumerate} \subsection{Examples} Here, we present examples for using {\tt match.data()}. Users can run these commands by typing {\tt demo(match.data)} at the R prompt. First, we load the Lalonde data, \begin{verbatim} > data(lalonde) \end{verbatim} The next line performs nearest neighbor matching based on the estimated propensity score from the logistic regression, \begin{verbatim} > m.out1 <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, + method = "nearest", distance = "logit") \end{verbatim} To obtain matched data, type the following command, \begin{verbatim} > m.data1 <- match.data(m.out1) \end{verbatim} It is easy to summarize the resulting matched data, \begin{verbatim} > summary(m.data1) \end{verbatim} To obtain matched data for the treatment or control group, specify the option {\tt group} as follows, \begin{verbatim} > m.data2 <- match.data(m.out1, group = "treat") > summary(m.data2) > m.data3 <- match.data(m.out1, group = "control") > summary(m.data3) \end{verbatim} We can also use the function to return unmatched data: \begin{verbatim} > unmatched.data <- lalonde[!row.names(lalonde)%in%row.names(match.data(m.out1)),] \end{verbatim} We can also specify different names for the subclass indicator, the weight variable, and the estimated distance measure. The following example first does a subclassification method, obtains the matched data with specified names for those three variables, and then print out the names of all variables in the resulting matched data. \begin{verbatim} > m.out2 <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, + method = "subclass") > m.data4 <- match.data(m.out2, subclass = "block", weights = "w", + distance = "pscore") > names(m.data4) \end{verbatim} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/matchitref.tex0000644000176200001440000004345312162551623016477 0ustar liggesusers\subsubsection{Syntax} \begin{verbatim} > m.out <- matchit(formula, data, method = "nearest", verbose = FALSE, ...) \end{verbatim} \subsubsection{Arguments} \paragraph{Arguments for All Matching Methods} \begin{itemize} \item \texttt{formula}: formula used to calculate the distance measure for matching (e.g., the propensity score model). It takes the usual syntax of R formulas, {\tt treat \~\ x1 + x2}, where {\tt treat} is a binary treatment indicator, and {\tt x1} and {\tt x2} are the pre-treatment covariates. Both the treatment indicator and pre-treatment covariates must be contained in the same data frame, which is specified as {\tt data} (see below). All of the usual R syntax for formulas work here. For example, {\tt x1:x2} represents the first order interaction term between {\tt x1} and {\tt x2}, and {\tt I(x1 \^\ 2)} represents the square term of {\tt x1}. See {\tt help(formula)} for details. \item \texttt{data}: the data frame containing the variables called in {\tt formula}. \item \texttt{method}: the matching method (default = \texttt{"nearest"}, nearest neighbor matching). Currently, \texttt{"exact"} (exact matching), \texttt{"full"} (full matching), \texttt{"nearest"} (nearest neighbor matching), \texttt{"optimal"} (optimal matching), \texttt{"subclass"} (subclassification), \texttt{"genetic"} (genetic matching), and \texttt{"cem"} (coarsened exact matching) are available. Note that within each of these matching methods, \MatchIt\ offers a variety of options. See below for more details. \item \texttt{verbose}: a logical value indicating whether to print the status of the matching algorithm (default = \texttt{FALSE}). \end{itemize} \paragraph{Additional Arguments for Specification of Distance Measures} \label{subsubsec:inputs-all} The following arguments specify distance measures that are used for matching methods. These arguments apply to all matching methods {\it except exact matching}. \begin{itemize} \item \texttt{distance}: the method used to estimate the distance measure (default = {\tt "logit"}, logistic regression) or a numerical vector of user's own distance measure. Before using any of these techniques, it is best to understand the theoretical groundings of these techniques and to evaluate the results. Most of these methods (such as logistic or probit regression) define the distance by first estimating the propensity score, defined as the probability of receiving treatment, conditional on the covariates. Available methods include: \begin{itemize} \item {\tt "mahalanobis"}: the Mahalanobis distance measure. \item binomial generalized linear models with one of the following link functions: \begin{itemize} \item \texttt{"logit"}: logistic link \item {\tt "linear.logit"}: logistic link with linear propensity score\footnote{The linear propensity scores are obtained by transforming back onto a linear scale.} \item \texttt{"probit"}: probit link \item {\tt "linear.probit"}: probit link with linear propensity score \item {\tt "cloglog"}: complementary log-log link \item {\tt "linear.cloglog"}: complementary log-log link with linear propensity score \item {\tt "log"}: log link \item {\tt "linear.log"}: log link with linear propensity score \item {\tt "cauchit"} Cauchy CDF link \item {\tt "linear.cauchit"} Cauchy CDF link with linear propensity score \end{itemize} \item Choose one of the following generalized additive models (see {\tt help(gam)} for more options). \begin{itemize} \item \texttt{"GAMlogit"}: logistic link \item {\tt "GAMlinear.logit"}: logistic link with linear propensity score \item \texttt{"GAMprobit"}: probit link \item {\tt "GAMlinear.probit"}: probit link with linear propensity score \item {\tt "GAMcloglog"}: complementary log-log link \item {\tt "GAMlinear.cloglog"}: complementary log-log link with linear propensity score \item {\tt "GAMlog"}: log link \item {\tt "GAMlinear.log"}: log link with linear propensity score, \item {\tt "GAMcauchit"}: Cauchy CDF link \item {\tt "GAMlinear.cauchit"}: Cauchy CDF link with linear propensity score \end{itemize} \item \texttt{"nnet"}: neural network model. See {\tt help(nnet)} for more options. \item \texttt{"rpart"}: classification trees. See {\tt help(rpart)} for more options. \end{itemize} \item \texttt{distance.options}: optional arguments for estimating the distance measure. The input to this argument should be a list. For example, if the distance measure is estimated with a logistic regression, users can increase the maximum IWLS iterations by \texttt{distance.options = list(maxit = 5000)}. Find additional options for general linear models using {\tt help(glm)} or {\tt help(family)}, for general additive models using {\tt help(gam)}, for neutral network models {\tt help(nnet)}, and for classification trees {\tt help(rpart)}. \item \texttt{discard}: specifies whether to discard units that fall outside some measure of support of the distance measure (default = \texttt{"none"}, discard no units). Discarding units may change the quantity of interest being estimated by changing the observations left in the analysis. Enter a logical vector indicating which unit should be discarded or choose from the following options: \begin{itemize} \item \texttt{"none"}: no units will be discarded before matching. Use this option when the units to be matched are substantially similar, such as in the case of matching treatment and control units from a field experiment that was close to (but not fully) randomized (e.g., \citealt{Imai05}), when caliper matching will restrict the donor pool, or when you do not wish to change the quantity of interest and the parametric methods to be used post-matching can be trusted to extrapolate. \item \texttt{"hull.both"}: all units that are not within the convex hull will be discarded. See \citet{KinZen06,KinZen07} for information about the convex hull in this context and as a measure of model dependence. \item \texttt{"both"}: all units (treated and control) that are outside the support of the distance measure will be discarded. \item \texttt{"hull.control"}: only control units that are not within the convex hull of the treated units will be discarded. \item \texttt{"control"}: only control units outside the support of the distance measure of the treated units will be discarded. Use this option when the average treatment effect on the treated is of most interest and when you are unwilling to discard non-overlapping treatment units (which would change the quantity of interest). \item \texttt{"hull.treat"}: only treated units that are not within the convex hull of the control units will be discarded. \item \texttt{"treat"}: only treated units outside the support of the distance measure of the control units will be discarded. Use this option when the average treatment effect on the control units is of most interest and when unwilling to discard control units. \end{itemize} \item \texttt{reestimate}: If {\tt FALSE} (default), the model for the distance measure will not be re-estimated after units are discarded. The input must be a logical value. Re-estimation may be desirable for efficiency reasons, especially if many units were discarded and so the post-discard samples are quite different from the original samples. \end{itemize} \paragraph{Additional Arguments for Subclassification} \label{subsubsec:inputs-subclass} \begin{itemize} \item \texttt{sub.by}: criteria for subclassification. Choose from: \texttt{"treat"} (default), the number of treatment units; \texttt{"control"}, the number of control units; or \texttt{"all"}, the total number of units. Changing the default will likely also signal a change in your quantity of interest from the average treatment effect on the treated to other quantities. \item \texttt{subclass}: either a scalar specifying the number of subclasses, or a vector of probabilities bounded between 0 and 1, which create quantiles of the distance measure using the units in the group specified by \texttt{sub.by} (default = \texttt{subclass = 6}). \end{itemize} \paragraph{Additional Arguments for Nearest Neighbor Matching} \label{subsubsec:inputs-nearest} \begin{itemize} \item \texttt{m.order}: the order in which to match treatment units with control units. \begin{itemize} \item {\tt "largest"} (default): matches from the largest value of the distance measure to the smallest. \item {\tt "smallest"}: matches from the smallest value of the distance measure to the largest. \item {\tt "random"}: matches in random order. \end{itemize} \item \texttt{replace}: logical value indicating whether each control unit can be matched to more than one treated unit (default = {\tt replace = FALSE}, each control unit is used at most once -- i.e., sampling without replacement). For matching with replacement, use \texttt{replace = TRUE}. After matching with replacement, the weights can be used to reflect the frequency with which each control unit was matched. \item \texttt{ratio}: the number of control units to match to each treated unit (default = {\tt 1}). If matching is done without replacement and there are fewer control units than {\tt ratio} times the number of eligible treated units (i.e., there are not enough control units for the specified method), then the higher ratios will have \texttt{NA} in place of the matching unit number in \texttt{match.matrix}. \item \texttt{exact}: variables on which to perform exact matching within the nearest neighbor matching (default = {\tt NULL}, no exact matching). If \texttt{exact} is specified, only matches that exactly match on the covariates in \texttt{exact} will be allowed. Within the matches that match on the variables in \texttt{exact}, the match with the closest distance measure will be chosen. \texttt{exact} should be entered as a vector of variable names (e.g., \texttt{exact = c("X1", "X2")}). \item \texttt{caliper}: the number of standard deviations of the distance measure within which to draw control units (default = {\tt 0}, no caliper matching). If a caliper is specified, a control unit within the caliper for a treated unit is randomly selected as the match for that treated unit. If \texttt{caliper != 0}, there are two additional options: \begin{itemize} \item \texttt{calclosest}: whether to take the nearest available match if no matches are available within the \texttt{caliper} (default = {\tt FALSE}). \item \texttt{mahvars}: variables on which to perform Mahalanobis-metric matching within each caliper (default = {\tt NULL}). Variables should be entered as a vector of variable names (e.g., \texttt{mahvars = c("X1", "X2")}). If \texttt{mahvars} is specified without \texttt{caliper}, the caliper is set to 0.25. \end{itemize} \item \texttt{subclass} and \texttt{sub.by}: See the options for subclassification for more details on these options. If a \texttt{subclass} is specified within \texttt{method = "nearest"}, the matched units will be placed into subclasses after the nearest neighbor matching is completed. \end{itemize} \paragraph{Additional Arguments for Optimal Matching} \label{subsubsec:inputs-optimal} \begin{itemize} \item {\tt ratio}: the number of control units to be matched to each treatment unit (default = {\tt 1}). \item {\tt ...}: additional inputs that can be passed to the {\tt fullmatch()} function in the {\tt optmatch} package. See {\tt help(fullmatch)} or \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details. \end{itemize} \paragraph{Additional Arguments for Full Matching} \label{subsubsec:inputs-full} See {\tt help(fullmatch)} (part of this information is copied below) or \hlink{http://www.stat.lsa.umich.edu/\~{}bbh/optmatch.html}{http://www.stat.lsa.umich.edu/~bbh/optmatch.html} for details. \begin{itemize} \item {\tt min.controls}: The minimum ratio of controls to treatments that is to be permitted within a matched set: should be nonnegative and finite. If {\tt min.controls} is not a whole number, the reciprocal of a whole number, or zero, then it is rounded down to the nearest whole number or reciprocal of a whole number. \item {\tt max.controls}: The maximum ratio of controls to treatments that is to be permitted within a matched set: should be positive and numeric. If {\tt max.controls} is not a whole number, the reciprocal of a whole number, or {\tt Inf}, then it is rounded up to the nearest whole number or reciprocal of a whole number. \item {\tt omit.fraction}: Optionally, specify what fraction of controls or treated subjects are to be rejected. If {\tt omit.fraction} is a positive fraction less than one, then {\tt fullmatch()} leaves up to that fraction of the control reservoir unmatched. If {\tt omit.fraction} is a negative number greater than $-1$, then {\tt fullmatch()} leaves up to $|{\rm omit.fraction}|$ of the treated group unmatched. Positive values are only accepted if ${\rm max.controls} >= 1$; negative values, only if ${\rm min.controls} <= 1$. If {\tt omit.fraction} is not specified, then only those treated and control subjects without permissible matches among the control and treated subjects, respectively, are omitted. \item {\tt ...}: Additional inputs that can be passed to the {\tt fullmatch()} function in the {\tt optmatch} package. \end{itemize} \paragraph{Additional Arguments for Genetic Matching} \label{subsubsec:inputs-genetic} The available options are listed below. \begin{itemize} \item {\tt ratio}: the number of control units to be matched to each treatment unit (default = {\tt 1}). \item {\tt ...}: additional minor inputs that can be passed to the {\tt GenMatch()} function in the {\tt Matching} package. See {\tt help(GenMatch)} or\\ \hlink{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html}{http://sekhon.polisci.berkeley.edu/library/Matching/html/GenMatch.html} for details. \end{itemize} \paragraph{Additional Arguments for Coarsened Exact Matching} \label{subsubsec:inputs-cem} The available options are listed here: \begin{itemize} \item{cutpoints} named list each describing the cutpoints for the variables. Each list element is either a vector of cutpoints, a number of cutpoints, a method for automatic bin contruction. \item{k2k} return k-to-k matching? \item{verbose} controls level of verbosity \item{dist} user defined distance function \item {\tt ...}: additional minor inputs that can be passed to the {\tt cem()} function in the {\tt cem} package. See {\tt help(cem)} or \hlink{http://gking.harvard.edu/cem}{http://gking.harvard.edu/cem} for details. \end{itemize} \subsubsection{Output Values} \label{sec:outputs} Regardless of the type of matching performed, the \texttt{matchit} output object contains the following elements:\footnote{When inapplicable or unnecessary, these elements may equal {\tt NULL}. For example, when exact matching, {\tt match.matrix = NULL}.} \begin{itemize} \item \texttt{call}: the original {\tt matchit()} call. \item \texttt{formula}: the formula used to specify the model for estimating the distance measure. \item \texttt{model}: the output of the model used to estimate the distance measure. \texttt{summary(m.out\$model)} will give the summary of the model where \texttt{m.out} is the output object from \texttt{matchit()}. \item \texttt{match.matrix}: an $n_1 \times$ \texttt{ratio} matrix where: \begin{itemize} \item the row names represent the names of the treatment units (which match the row names of the data frame specified in \texttt{data}). \item each column stores the name(s) of the control unit(s) matched to the treatment unit of that row. For example, when the \texttt{ratio} input for nearest neighbor or optimal matching is specified as 3, the three columns of \texttt{match.matrix} represent the three control units matched to one treatment unit). \item \texttt{NA} indicates that the treatment unit was not matched. \end{itemize} \item \texttt{discarded}: a vector of length $n$ that displays whether the units were ineligible for matching due to common support restrictions. It equals \texttt{TRUE} if unit $i$ was discarded, and it is set to \texttt{FALSE} otherwise. \item \texttt{distance}: a vector of length $n$ with the estimated distance measure for each unit. \item \texttt{weights}: a vector of length $n$ with the weights assigned to each unit in the matching process. Unmatched units have weights equal to $0$. Matched treated units have weight $1$. Each matched control unit has weight proportional to the number of treatment units to which it was matched, and the sum of the control weights is equal to the number of uniquely matched control units. \item \texttt{subclass}: the subclass index in an ordinal scale from 1 to the total number of subclasses as specified in \texttt{subclass} (or the total number of subclasses from full or exact matching). Unmatched units have \texttt{NA}. \item \texttt{q.cut}: the subclass cut-points that classify the distance measure. \item \texttt{treat}: the treatment indicator from \texttt{data} (the left-hand side of \texttt{formula}). \item \texttt{X}: the covariates used for estimating the distance measure (the right-hand side of \texttt{formula}). When applicable, \texttt{X} is augmented by covariates contained in \texttt{mahvars} and \texttt{exact}. \item \texttt{nn}: A basic summary table of matched data (e.g., the number of matched units) \end{itemize} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/matchit2zelig.tex0000644000176200001440000003030312162551623017105 0ustar liggesusers\section{Conducting Analyses after Matching} \label{sec:analysis} Any software package may be used for parametric analysis following \MatchIt. This includes any of the relevant R packages, or other statistical software by exporting the resulting matched data sets using R commands such as {\tt write.csv()} and {\tt write.table()} for ASCII files or {\tt write.dta()} in the {\tt foreign} package for a STATA binary file. When variable numbers of treated and control units have been matched to each other (e.g., through exact matching, full matching, or k:1 matching with replacement), the weights created by MatchIt should be used (e.g., in a weighted regression) to ensure that the matched treated and control groups are weighted up to be similar. Users should also remember that the weights created by MatchIt estimate the average treatment effect on the treated, with the control units weighted to resemble the treated units. See below for more detail on the weights. With subclassification, estimates should be obtained within each subclass and then aggregated across subclasses. When it is not possible to calculate an effect within each subclass, again the weights can be used to weight the matched units. In this section, we show how to use \hlink{Zelig}{http://gking.harvard.edu/zelig/} with \MatchIt. Zelig \citep{ImaKinLau06} is an R package that implements a large variety of statistical models (using numerous existing R packages) with a single easy-to-use interface, gives easily interpretable results by simulating quantities of interest, provides numerical and graphical summaries, and is easily extensible to include new methods. \subsection{Quick Overview} The general syntax is as follows. First, we use \texttt{match.data()} to create the matched data from the \MatchIt\ output object (\texttt{m.out}) by excluding unmatched units from the original data, and including information produced by the particular matching procedure (i.e., primarily a new data set, but also information that may result such as weights, subclasses, or the distance measure). \begin{verbatim} > m.data <- match.data(m.out) \end{verbatim} where {\tt m.data} is the resulting matched data. Zelig analyses all use three commands --- \texttt{zelig}, \texttt{setx}, and \texttt{sim}. For example, the basic statistical analysis is performed first: \begin{verbatim} > z.out <- zelig(Y ~ treat + x1 + x2, model = mymodel, data = m.data) \end{verbatim} where {\tt Y} is the outcome variable, {\tt mymodel} is the selected model, and {\tt z.out} is the output object from {\tt zelig}. This output object includes estimated coefficients, standard errors, and other typical outputs from your chosen statistical model. Its contents can be examined via \texttt{summary(z.out)} or \texttt{plot(z.out)}, but the idea of Zelig is that these statistical results are typically only intermediate quantities needed to compute your ultimate quantities of interest, which in the case of matching are usually causal inferences. To get these causal quantities, we use Zelig's other two commands. Thus, we can set the explanatory variables at their means (the default) and change the treatment variable from a 0 to a 1: \begin{verbatim} > x.out <- setx(z.out, treat=0) > x1.out <- setx(z.out, treat=1) \end{verbatim} and finally compute the resulting estimates of the causal effects and examine a summary: \begin{verbatim} > s.out <- sim(z.out, x = x.out, x1 = x1.out) > summary(s.out) \end{verbatim} \subsection{Examples} We now give four examples using the Lalonde data. They are meant to be read sequentially. You can run these example commands by typing {\tt demo(analysis)}. Although we use the linear least squares model in these examples, a wide range of other models are available in Zelig (for the list of supported models, see \hlink{\url{http://gking.harvard.edu/zelig/docs/Models_Zelig_Can.html}} {http://gking.harvard.edu/zelig/docs/Models_Zelig_Can.html}. To load the Zelig package after installing it, type \begin{verbatim} > library(Zelig) \end{verbatim} \begin{description} \item[Model-Based Estimates] In our first example, we conduct a standard parametric analysis and compute quantities of interest in the most common way. We begin with nearest neighbor matching with a logistic regression-based propensity score, discarding control units outside the convex hull of the treated units \citep{KinZen06,KinZen07}: \begin{verbatim} > m.out <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, method = "nearest", discard = "hull.control", data = lalonde) \end{verbatim} Then we check balance using the summary and plot procedures (which we don't show here). When the best balance is achieved, we run the parametric analysis: \begin{verbatim} > z.out <- zelig(re78 ~ treat + age + educ + black + hispan + nodegree + married + re74 + re75, data = match.data(m.out), model = "ls") \end{verbatim} and then set the explanatory variables at their means (the default) and change the treatment variable from a 0 to a 1: \begin{verbatim} > x.out <- setx(z.out, treat=0) > x1.out <- setx(z.out, treat=1) \end{verbatim} and finally compute the result and examine a summary: \begin{verbatim} > s.out <- sim(z.out, x = x.out, x1 = x1.out) > summary(s.out) \end{verbatim} \item[Average Treatment Effect on the Treated] We illustrate now how to estimate the average treatment effect on the treated in a way that is quite robust. We do this by estimating the coefficients in the control group alone. We begin by conducting nearest neighbor matching with a logistic regression-based propensity score: \begin{verbatim} > m.out1 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, method = "nearest", data = lalonde) \end{verbatim} Then we check balance using the summary and plot procedures (which we don't show here). We reestimate the matching procedure until we achieve the best balance possible. (The running examples here are meant merely to illustrate, not to suggest that we've achieved the best balance.) Then we go to Zelig, and in this case choose to fit a linear least squares model to the control group only: \begin{verbatim} > z.out1 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = match.data(m.out1, "control"), model = "ls") \end{verbatim} where the {\tt "control"} option in {\tt match.data()} extracts only the matched control units and {\tt ls} specifies least squares regression. In a smaller data set, this example should probably be changed to include all the data in this estimation (using \texttt{data = match.data(m.out1)} for the data) and by including the treatment indicator (which is excluded in the example since its a constant in the control group.) Next, we use the coefficients estimated in this way from the control group, and combine them with the values of the covariates set to the values of the treated units. We do this by choosing conditional prediction (which means use the observed values) in \texttt{setx()}. The {\tt sim()} command does the imputation. \begin{verbatim} > x.out1 <- setx(z.out1, data = match.data(m.out1, "treat"), cond = TRUE) > s.out1 <- sim(z.out1, x = x.out1) \end{verbatim} Finally, we obtain a summary of the results by \begin{verbatim} > summary(s.out1) \end{verbatim} \item[Average Treatment Effect (Overall)] To estimate the average treatment effect, we continue with the previous example and fit the linear model to the {\it treatment group}: \begin{verbatim} > z.out2 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = match.data(m.out1, "treat"), model = "ls") \end{verbatim} We then conduct the same simulation procedure in order to impute the counterfactual outcome for the {\it control group}, \begin{verbatim} > x.out2 <- setx(z.out2, data = match.data(m.out1, "control"), cond = TRUE) > s.out2 <- sim(z.out2, x = x.out2) \end{verbatim} In this calculation, Zelig is computing the difference between observed and the expected values. This means that the treatment effect for the control units is the effect of control (observed control outcome minus the imputed outcome under treatment from the model). Hence, to combine treatment effects just reverse the signs of the estimated treatment effect of controls. \begin{verbatim} > ate.all <- c(s.out1$qi$att.ev, -s.out2$qi$att.ev) \end{verbatim} The point estimate, its standard error, and the $95\%$ confidence interval is given by \begin{verbatim} > mean(ate.all) > sd(ate.all) > quantile(ate.all, c(0.025, 0.975)) \end{verbatim} \item[Subclassification] In subclassification, the average treatment effect estimates are obtained separately for each subclass, and then aggregated for an overall estimate. Estimating the treatment effects separately for each subclass, and then aggregating across subclasses, can increase the robustness of the ultimate results since the parametric analysis within each subclass requires only local rather than global assumptions. However, fewer observations are obviously available within each subclass, and so this option is normally chosen for larger data sets. We begin this example by conducting subclassification with four subclasses, \begin{verbatim} > m.out2 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = lalonde, method = "subclass", subclass = 4) \end{verbatim} When balance is as good as we can get it, we then fit a linear regression within each subclass by controlling for the estimated propensity score (called \texttt{distance}) and other covariates. In most software, this would involve running four separate regressions and then combining the results. In Zelig, however, all we need to do is to use the {\tt by} option: \begin{verbatim} > z.out3 <- zelig(re78 ~ re74 + re75 + distance, data = match.data(m.out2, "control"), model = "ls", by = "subclass") \end{verbatim} The same set of commands as in the first example are used to do the imputation of the counterfactual outcomes for the treated units: \begin{verbatim} > x.out3 <- setx(z.out3, data = match.data(m.out2, "treat"), fn = NULL, cond = TRUE) > s.out3 <- sim(z.out3, x = x.out3) > summary(s.out3) \end{verbatim} It is also possible to get the summary result for each subclass. For example, the following command summarizes the result for the second subclass. \begin{verbatim} > summary(s.out3, subset = 2) \end{verbatim} \item[How Adjustment After Exact Matching Has No Effect] Regression adjustment after exact one-to-one exact matching gives the identical answer as a simple, unadjusted difference in means. General exact matching, as implemented in MatchIt, allows one-to-many matches, so to see the same result we must weight when adjusting. In other words: weighted regression adjustment after general exact matching gives the identical answer as a simple, unadjusted weighted difference in means. For example: \begin{verbatim} > m.out <- matchit(treat ~ educ + black + hispan, data = lalonde, method = "exact") > m.data <- match.data(m.out) > ## weighted diff in means > weighted.mean(mdata$re78[mdata$treat == 1], mdata$weights[mdata$treat==1]) - weighted.mean(mdata$re78[mdata$treat==0], mdata$weights[mdata$treat==0]) [1] 807 > ## weighted least squares without covariates > zelig(re78 ~ treat, data = m.data, model = "ls", weights = "weights") Call: zelig(formula = re78 ~ treat, model = "ls", data = m.data, weights = "weights") Coefficients: (Intercept) treat 5524 807 > ## weighted least squares with covariates > zelig(re78 ~ treat + black + hispan + educ, data = m.data, model = "ls", weights = "weights") Call: zelig(formula = re78 ~ treat + black + hispan + educ, model = "ls", data = m.data, weights = "weights") Coefficients: (Intercept) treat black hispan educ 314 807 -1882 258 657 \end{verbatim} \end{description} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/matchit.tex0000644000176200001440000002047712162551623016003 0ustar liggesusers\documentclass[oneside,letterpaper,12pt]{book} \usepackage{bibentry} \usepackage{graphicx} \usepackage{natbib} \usepackage[reqno]{amsmath} \usepackage{amssymb} \usepackage{verbatim} \usepackage{epsf} \usepackage{url} \usepackage{html} \usepackage{dcolumn} \usepackage{fullpage} \bibpunct{(}{)}{;}{a}{}{,} \newcolumntype{.}{D{.}{.}{-1}} \newcolumntype{d}[1]{D{.}{.}{#1}} %\pagestyle{myheadings} \htmladdtonavigation{ \htmladdnormallink{% \htmladdimg{http://gking.harvard.edu/pics/home.gif}} {http://gking.harvard.edu/}} \newcommand{\hlink}{\htmladdnormallink} %\bodytext{ BACKGROUND="http://gking.harvard.edu/pics/temple.jpg"} \setcounter{tocdepth}{3} \setcounter{secnumdepth}{4} \newcommand{\MatchIt}{\textsc{MatchIt}} \title{\MatchIt: Nonparametric Preprocessing for Parametric Causal Inference\thanks{We thank Olivia Lau for helpful suggestions about incorporating \MatchIt\, into Zelig.}} \author{Daniel E. Ho,\thanks{Assistant Professor of Law \& Robert E.\ Paradise Faculty Scholar, Stanford Law School (559 Nathan Abbott Way, Stanford CA 94305; \texttt{http://dho.stanford.edu}, \texttt{dho@law.stanford.edu}, (650) 723-9560).} \and % Kosuke Imai,\thanks{Assistant Professor, Department of Politics, Princeton University (Corwin Hall 041, Department of Politics, Princeton University, Princeton NJ 08544, USA; \texttt{http://imai.princeton.edu}, \texttt{kimai@Princeton.Edu}).} \and % Gary King,\thanks{David Florence Professor of Government, Harvard University (Institute for Quantitative Social Science, 1737 Cambridge Street, Harvard University, Cambridge MA 02138; \texttt{http://GKing.Harvard.Edu}, \texttt{King@Harvard.Edu}, (617) 495-2027).} \and % Elizabeth A. Stuart\thanks{Assistant Professor, Departments of Mental Health and Biostatistics, Johns Hopkins Bloomberg School of Public Health (624 N Broadway, Room 804, Baltimore, MD 21205; \texttt{http://www.biostat.jhsph.edu/$\sim$estuart}, \texttt{estuart@jhsph.edu}).}} %\makeindex \begin{document} \maketitle \begin{rawhtml}

[Also available is a downloadable PDF version of this entire document] \end{rawhtml} \tableofcontents \nobibliography* %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \clearpage \chapter{Introduction} \input{intro} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \chapter{Statistical Overview} \input{overview} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \chapter{User's Guide to \MatchIt} \label{methods} \input{preprocess} \input{balance} \input{matchit2zelig} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \chapter{Reference Manual} \label{chap:reference} \section{\texttt{matchit()}: Implementation of Matching Methods} \label{sec:matchit} Use \texttt{matchit()} to implement a variety of matching procedures including exact matching, nearest neighbor matching, subclassification, optimal matching, genetic matching, and full matching. The output of {\tt matchit()} can be analyzed via any standard R package, by exporting the data for use in another program, or most simply via \hlink{Zelig}{http://gking.harvard.edu/zelig} in R. \input{matchitref} \input{summaryref} \input{plotref} \input{mdataref} \input{faq} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \chapter{What's New?} \begin{itemize} \item \textbf{2.4-20} (October 24, 2011): bug fix for GAM models (thanks to Felix Thoemmes) \item \textbf{2.4-18} (April 26, 2011): JSS version, no change in code. \item \textbf{2.4-17} (April 2, 2011): a minor documentation fix. \item \textbf{2.4-16} (January 8, 2011): a bug fix for user defined distance. \item \textbf{2.4-15} (December 11, 2010): a bug fix in the mahalanobis matching. \item \textbf{2.4-14} (August 12, 2010): a bug fix in {\tt match.data()} so that it can be called within a function (thanks to Ajay Shah, George Baah, and Ben Dominique); \MatchIt now does not specify digits for printing (thanks to Chris Hane); A summary table of matched data is now stored in the output (thanks to George Baah) \item \textbf{2.4-11} (June 25, 2009): More flexible inputs in plotting. \item \textbf{2.4-10} (February 2, 2009): Minor documentation fixes \item \textbf{2.4-8,2.4-9} (January 29, 2009): Minor documentation fixes \item \textbf{2.4-7} (August 4, 2008): Fixed minor bug in subclassification (thanks to Ben Domingue) \item \textbf{2.4-6} (July 21, 2008): Improved summary object for exact matching (thanks to Andrew Stokes) \item \textbf{2.4-5} (July 20, 2008): Fixed a minor bug. \item \textbf{2.4-4} (July 18, 2008): Fixed another bug with regard to the discard option (thanks to Ben Dominique). \item \textbf{2.4-3} (July 18, 2008): Fixed a bug in full matching regarding the discard option (thanks to Ben Dominique). Some updates of documentation regarding coarsened exact matching (2.4-1 and 2.4-2). \item \textbf{2.4} (June 12, 2008): Included coarsened exact matching; documentation bug fixes (thanks to Will Lowe) \item \textbf{2.3-1} (October 11, 2007): Stable release for R 2.6. Documentation improved. Some minor bug fixes and improvements. \item \textbf{2.2-14} (September 2, 2007): Stable release for R 2.5. Documentation improved for full matching. (Thanks to Langche Zeng) \item \textbf{2.2-13} (April 10, 2007): Stable release for R 2.4. Additional fix to package dependencies. Bug fix for summary(). \item \textbf{2.2-12} (April 6, 2007): Stable release for R 2.4. Fix to package dependencies. \item \textbf{2.2-11} (July 13, 2006): Stable release for R 2.3. Fix to ensure summary() command works with character variables in dataframe (thanks to Daniel Gerlanc). \item \textbf{2.2-10} (May 9, 2006): Stable release for R 2.3. A bug fix in {\tt demo(analysis)} (thanks to Julia Gray). \item \textbf{2.2-9} (May 3, 2006): Stable release for R 2.3. A minor change to DESCRIPTION file. \item \textbf{2.2-8} (May 1, 2006): Stable release for R 2.3. Removed dependency on Zelig (thanks to Dave Kane). \item \textbf{2.2-7} (April 11, 2006): Stable release for R 2.2. Error message for missing values in the data frame added (thanks to Olivia Lau). \item \textbf{2.2-6} (April 4, 2006): Stable release for R 2.2. Bug fixes related to {\tt reestimate} in {\tt matchit()} and {\tt match.data()} (thanks to Ani Ruhil and Claire Aussems). \item \textbf{2.2-5} (December 7, 2005): Stable release for R 2.2. Changed URL of {\tt WhatIf} to CRAN. \item \textbf{2.2-4} (December 3, 2005): Stable release for R 2.2. User's own distance measure can be used with \MatchIt\, (thanks to Nelson Lim). \item \textbf{2.2-3} (November 18, 2005): Stable release for R 2.2. standardize option added to full matching and subclass (thanks to Jeronimo Cortina). \item \textbf{2.2-2} (November 9, 2005): Stable release for R 2.2. {\tt optmatch} package now on CRAN. Changed URL for that package. \item \textbf{2.2-1} (November 1, 2005): Stable release for R 2.2. balance measures based on empirical CDF are added as a new option {\tt standardize} in {\tt summary()}. \item \textbf{2.1-4} (October 14, 2005): Stable release for R 2.2. strictly empirical (no interpolation) quantile-quantile functions and plots are used. \item \textbf{2.1-3} (September 27, 2005): Stable release for R 2.1. automated the installation of optional packages. fixed a coding error in {\tt summary()}, the documentation edited. \item \textbf{2.1-2} (September 27, 2005): Stable release for R 2.1. minor changes to file names, the option {\tt "whichxs"} added to the {\tt plot()}, major editing of the documentation. \item \textbf{2.1-1} (September 16, 2005): Stable release for R 2.1. Genetic matching added. \item \textbf{2.0-1} (August 29, 2005): Stable release for R 2.1. Major revisions including some syntax changes. Statistical tests are no longer used for balance checking, which are now based on the empirical covariate distributions (e.g., quantile-quantile plot). \item \textbf{1.0-2} (August 10, 2005): Stable release for R 2.1. Minor bug fixes (Thanks to Bart Bonikowski). \item \textbf{1.0-1} (January 3, 2005): Stable release for R 2.0. 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164.22 c 289.57 163.15 288.70 162.27 287.63 162.27 c 286.56 162.27 285.68 163.15 285.68 164.22 c B 285.30 164.11 m 285.30 165.18 286.17 166.05 287.24 166.05 c 288.31 166.05 289.18 165.18 289.18 164.11 c 289.18 163.04 288.31 162.17 287.24 162.17 c 286.17 162.17 285.30 163.04 285.30 164.11 c B 282.97 170.31 m 282.97 171.38 283.85 172.25 284.92 172.25 c 285.99 172.25 286.86 171.38 286.86 170.31 c 286.86 169.24 285.99 168.36 284.92 168.36 c 283.85 168.36 282.97 169.24 282.97 170.31 c B 139.75 165.37 m 139.75 166.44 140.62 167.32 141.69 167.32 c 142.76 167.32 143.63 166.44 143.63 165.37 c 143.63 164.31 142.76 163.43 141.69 163.43 c 140.62 163.43 139.75 164.31 139.75 165.37 c B 286.07 169.58 m 286.07 170.65 286.95 171.52 288.02 171.52 c 289.08 171.52 289.96 170.65 289.96 169.58 c 289.96 168.51 289.08 167.63 288.02 167.63 c 286.95 167.63 286.07 168.51 286.07 169.58 c B 286.07 172.34 m 286.07 173.41 286.95 174.28 288.02 174.28 c 289.08 174.28 289.96 173.41 289.96 172.34 c 289.96 171.27 289.08 170.40 288.02 170.40 c 286.95 170.40 286.07 171.27 286.07 172.34 c B 286.85 169.79 m 286.85 170.86 287.72 171.73 288.79 171.73 c 289.86 171.73 290.73 170.86 290.73 169.79 c 290.73 168.72 289.86 167.85 288.79 167.85 c 287.72 167.85 286.85 168.72 286.85 169.79 c B 283.36 170.74 m 283.36 171.81 284.24 172.69 285.31 172.69 c 286.37 172.69 287.25 171.81 287.25 170.74 c 287.25 169.68 286.37 168.80 285.31 168.80 c 284.24 168.80 283.36 169.68 283.36 170.74 c B 140.00 163.20 m 140.00 164.27 140.88 165.14 141.95 165.14 c 143.02 165.14 143.89 164.27 143.89 163.20 c 143.89 162.13 143.02 161.25 141.95 161.25 c 140.88 161.25 140.00 162.13 140.00 163.20 c B 138.98 169.63 m 138.98 170.69 139.85 171.57 140.92 171.57 c 141.99 171.57 142.87 170.69 142.87 169.63 c 142.87 168.56 141.99 167.68 140.92 167.68 c 139.85 167.68 138.98 168.56 138.98 169.63 c B 285.30 171.12 m 285.30 172.19 286.17 173.06 287.24 173.06 c 288.31 173.06 289.18 172.19 289.18 171.12 c 289.18 170.05 288.31 169.17 287.24 169.17 c 286.17 169.17 285.30 170.05 285.30 171.12 c B 286.07 171.22 m 286.07 172.29 286.95 173.17 288.02 173.17 c 289.08 173.17 289.96 172.29 289.96 171.22 c 289.96 170.15 289.08 169.28 288.02 169.28 c 286.95 169.28 286.07 170.15 286.07 171.22 c B 284.91 163.67 m 284.91 164.74 285.78 165.61 286.85 165.61 c 287.92 165.61 288.80 164.74 288.80 163.67 c 288.80 162.60 287.92 161.72 286.85 161.72 c 285.78 161.72 284.91 162.60 284.91 163.67 c B 284.52 162.78 m 284.52 163.85 285.40 164.72 286.47 164.72 c 287.54 164.72 288.41 163.85 288.41 162.78 c 288.41 161.71 287.54 160.83 286.47 160.83 c 285.40 160.83 284.52 161.71 284.52 162.78 c B 71.30 166.57 m 71.30 167.64 72.18 168.51 73.25 168.51 c 74.32 168.51 75.19 167.64 75.19 166.57 c 75.19 165.50 74.32 164.63 73.25 164.63 c 72.18 164.63 71.30 165.50 71.30 166.57 c B 285.68 173.00 m 285.68 174.07 286.56 174.94 287.63 174.94 c 288.70 174.94 289.57 174.07 289.57 173.00 c 289.57 171.93 288.70 171.05 287.63 171.05 c 286.56 171.05 285.68 171.93 285.68 173.00 c B 139.75 173.47 m 139.75 174.54 140.62 175.41 141.69 175.41 c 142.76 175.41 143.63 174.54 143.63 173.47 c 143.63 172.40 142.76 171.53 141.69 171.53 c 140.62 171.53 139.75 172.40 139.75 173.47 c B 284.52 173.78 m 284.52 174.85 285.40 175.73 286.47 175.73 c 287.54 175.73 288.41 174.85 288.41 173.78 c 288.41 172.71 287.54 171.84 286.47 171.84 c 285.40 171.84 284.52 172.71 284.52 173.78 c B 284.91 172.62 m 284.91 173.69 285.78 174.56 286.85 174.56 c 287.92 174.56 288.80 173.69 288.80 172.62 c 288.80 171.55 287.92 170.67 286.85 170.67 c 285.78 170.67 284.91 171.55 284.91 172.62 c B 283.36 165.02 m 283.36 166.09 284.24 166.97 285.31 166.97 c 286.37 166.97 287.25 166.09 287.25 165.02 c 287.25 163.95 286.37 163.08 285.31 163.08 c 284.24 163.08 283.36 163.95 283.36 165.02 c B 286.07 169.00 m 286.07 170.07 286.95 170.94 288.02 170.94 c 289.08 170.94 289.96 170.07 289.96 169.00 c 289.96 167.93 289.08 167.05 288.02 167.05 c 286.95 167.05 286.07 167.93 286.07 169.00 c B 284.52 162.96 m 284.52 164.03 285.40 164.90 286.47 164.90 c 287.54 164.90 288.41 164.03 288.41 162.96 c 288.41 161.89 287.54 161.01 286.47 161.01 c 285.40 161.01 284.52 161.89 284.52 162.96 c B 139.75 168.39 m 139.75 169.46 140.62 170.33 141.69 170.33 c 142.76 170.33 143.63 169.46 143.63 168.39 c 143.63 167.32 142.76 166.44 141.69 166.44 c 140.62 166.44 139.75 167.32 139.75 168.39 c B 285.68 169.75 m 285.68 170.82 286.56 171.69 287.63 171.69 c 288.70 171.69 289.57 170.82 289.57 169.75 c 289.57 168.68 288.70 167.80 287.63 167.80 c 286.56 167.80 285.68 168.68 285.68 169.75 c B 286.07 170.79 m 286.07 171.86 286.95 172.73 288.02 172.73 c 289.08 172.73 289.96 171.86 289.96 170.79 c 289.96 169.72 289.08 168.85 288.02 168.85 c 286.95 168.85 286.07 169.72 286.07 170.79 c B 139.75 165.92 m 139.75 166.98 140.62 167.86 141.69 167.86 c 142.76 167.86 143.63 166.98 143.63 165.92 c 143.63 164.85 142.76 163.97 141.69 163.97 c 140.62 163.97 139.75 164.85 139.75 165.92 c B 285.30 164.91 m 285.30 165.98 286.17 166.86 287.24 166.86 c 288.31 166.86 289.18 165.98 289.18 164.91 c 289.18 163.85 288.31 162.97 287.24 162.97 c 286.17 162.97 285.30 163.85 285.30 164.91 c B 284.91 167.28 m 284.91 168.35 285.78 169.23 286.85 169.23 c 287.92 169.23 288.80 168.35 288.80 167.28 c 288.80 166.22 287.92 165.34 286.85 165.34 c 285.78 165.34 284.91 166.22 284.91 167.28 c B 137.96 173.05 m 137.96 174.12 138.84 174.99 139.91 174.99 c 140.98 174.99 141.85 174.12 141.85 173.05 c 141.85 171.98 140.98 171.10 139.91 171.10 c 138.84 171.10 137.96 171.98 137.96 173.05 c B 140.52 164.29 m 140.52 165.36 141.39 166.23 142.46 166.23 c 143.53 166.23 144.41 165.36 144.41 164.29 c 144.41 163.22 143.53 162.35 142.46 162.35 c 141.39 162.35 140.52 163.22 140.52 164.29 c B 285.68 166.06 m 285.68 167.12 286.56 168.00 287.63 168.00 c 288.70 168.00 289.57 167.12 289.57 166.06 c 289.57 164.99 288.70 164.11 287.63 164.11 c 286.56 164.11 285.68 164.99 285.68 166.06 c B 284.52 166.69 m 284.52 167.76 285.40 168.64 286.47 168.64 c 287.54 168.64 288.41 167.76 288.41 166.69 c 288.41 165.62 287.54 164.75 286.47 164.75 c 285.40 164.75 284.52 165.62 284.52 166.69 c B 284.52 167.30 m 284.52 168.37 285.40 169.24 286.47 169.24 c 287.54 169.24 288.41 168.37 288.41 167.30 c 288.41 166.23 287.54 165.35 286.47 165.35 c 285.40 165.35 284.52 166.23 284.52 167.30 c B 285.30 170.40 m 285.30 171.47 286.17 172.35 287.24 172.35 c 288.31 172.35 289.18 171.47 289.18 170.40 c 289.18 169.33 288.31 168.46 287.24 168.46 c 286.17 168.46 285.30 169.33 285.30 170.40 c B 285.68 164.57 m 285.68 165.64 286.56 166.51 287.63 166.51 c 288.70 166.51 289.57 165.64 289.57 164.57 c 289.57 163.50 288.70 162.63 287.63 162.63 c 286.56 162.63 285.68 163.50 285.68 164.57 c B 282.97 171.93 m 282.97 173.00 283.85 173.87 284.92 173.87 c 285.99 173.87 286.86 173.00 286.86 171.93 c 286.86 170.86 285.99 169.99 284.92 169.99 c 283.85 169.99 282.97 170.86 282.97 171.93 c B 284.91 173.37 m 284.91 174.44 285.78 175.31 286.85 175.31 c 287.92 175.31 288.80 174.44 288.80 173.37 c 288.80 172.30 287.92 171.42 286.85 171.42 c 285.78 171.42 284.91 172.30 284.91 173.37 c B 285.68 171.08 m 285.68 172.15 286.56 173.02 287.63 173.02 c 288.70 173.02 289.57 172.15 289.57 171.08 c 289.57 170.01 288.70 169.13 287.63 169.13 c 286.56 169.13 285.68 170.01 285.68 171.08 c B 284.14 163.22 m 284.14 164.29 285.01 165.16 286.08 165.16 c 287.15 165.16 288.02 164.29 288.02 163.22 c 288.02 162.15 287.15 161.27 286.08 161.27 c 285.01 161.27 284.14 162.15 284.14 163.22 c B 138.22 167.81 m 138.22 168.87 139.09 169.75 140.16 169.75 c 141.23 169.75 142.10 168.87 142.10 167.81 c 142.10 166.74 141.23 165.86 140.16 165.86 c 139.09 165.86 138.22 166.74 138.22 167.81 c B 286.46 163.07 m 286.46 164.14 287.33 165.02 288.40 165.02 c 289.47 165.02 290.35 164.14 290.35 163.07 c 290.35 162.00 289.47 161.13 288.40 161.13 c 287.33 161.13 286.46 162.00 286.46 163.07 c B 284.91 165.20 m 284.91 166.26 285.78 167.14 286.85 167.14 c 287.92 167.14 288.80 166.26 288.80 165.20 c 288.80 164.13 287.92 163.25 286.85 163.25 c 285.78 163.25 284.91 164.13 284.91 165.20 c B 286.46 170.23 m 286.46 171.30 287.33 172.18 288.40 172.18 c 289.47 172.18 290.35 171.30 290.35 170.23 c 290.35 169.16 289.47 168.29 288.40 168.29 c 287.33 168.29 286.46 169.16 286.46 170.23 c B 283.75 172.52 m 283.75 173.59 284.62 174.47 285.69 174.47 c 286.76 174.47 287.64 173.59 287.64 172.52 c 287.64 171.45 286.76 170.58 285.69 170.58 c 284.62 170.58 283.75 171.45 283.75 172.52 c B 286.07 172.94 m 286.07 174.01 286.95 174.88 288.02 174.88 c 289.08 174.88 289.96 174.01 289.96 172.94 c 289.96 171.87 289.08 170.99 288.02 170.99 c 286.95 170.99 286.07 171.87 286.07 172.94 c B 287.23 173.33 m 287.23 174.40 288.11 175.28 289.18 175.28 c 290.25 175.28 291.12 174.40 291.12 173.33 c 291.12 172.27 290.25 171.39 289.18 171.39 c 288.11 171.39 287.23 172.27 287.23 173.33 c B 285.68 169.71 m 285.68 170.78 286.56 171.65 287.63 171.65 c 288.70 171.65 289.57 170.78 289.57 169.71 c 289.57 168.64 288.70 167.76 287.63 167.76 c 286.56 167.76 285.68 168.64 285.68 169.71 c B 286.07 165.76 m 286.07 166.83 286.95 167.71 288.02 167.71 c 289.08 167.71 289.96 166.83 289.96 165.76 c 289.96 164.69 289.08 163.82 288.02 163.82 c 286.95 163.82 286.07 164.69 286.07 165.76 c B 284.91 165.27 m 284.91 166.34 285.78 167.21 286.85 167.21 c 287.92 167.21 288.80 166.34 288.80 165.27 c 288.80 164.20 287.92 163.32 286.85 163.32 c 285.78 163.32 284.91 164.20 284.91 165.27 c B 285.68 165.31 m 285.68 166.37 286.56 167.25 287.63 167.25 c 288.70 167.25 289.57 166.37 289.57 165.31 c 289.57 164.24 288.70 163.36 287.63 163.36 c 286.56 163.36 285.68 164.24 285.68 165.31 c B 285.68 164.13 m 285.68 165.20 286.56 166.08 287.63 166.08 c 288.70 166.08 289.57 165.20 289.57 164.13 c 289.57 163.06 288.70 162.19 287.63 162.19 c 286.56 162.19 285.68 163.06 285.68 164.13 c B 286.07 170.94 m 286.07 172.01 286.95 172.88 288.02 172.88 c 289.08 172.88 289.96 172.01 289.96 170.94 c 289.96 169.87 289.08 168.99 288.02 168.99 c 286.95 168.99 286.07 169.87 286.07 170.94 c B 286.07 169.24 m 286.07 170.31 286.95 171.19 288.02 171.19 c 289.08 171.19 289.96 170.31 289.96 169.24 c 289.96 168.17 289.08 167.30 288.02 167.30 c 286.95 167.30 286.07 168.17 286.07 169.24 c B 286.07 162.99 m 286.07 164.06 286.95 164.93 288.02 164.93 c 289.08 164.93 289.96 164.06 289.96 162.99 c 289.96 161.92 289.08 161.04 288.02 161.04 c 286.95 161.04 286.07 161.92 286.07 162.99 c B 286.46 163.61 m 286.46 164.68 287.33 165.55 288.40 165.55 c 289.47 165.55 290.35 164.68 290.35 163.61 c 290.35 162.54 289.47 161.66 288.40 161.66 c 287.33 161.66 286.46 162.54 286.46 163.61 c B 284.52 171.58 m 284.52 172.65 285.40 173.53 286.47 173.53 c 287.54 173.53 288.41 172.65 288.41 171.58 c 288.41 170.52 287.54 169.64 286.47 169.64 c 285.40 169.64 284.52 170.52 284.52 171.58 c B 285.68 167.53 m 285.68 168.60 286.56 169.47 287.63 169.47 c 288.70 169.47 289.57 168.60 289.57 167.53 c 289.57 166.46 288.70 165.59 287.63 165.59 c 286.56 165.59 285.68 166.46 285.68 167.53 c B 156.48 166.00 m 156.48 167.07 157.36 167.94 158.43 167.94 c 159.50 167.94 160.37 167.07 160.37 166.00 c 160.37 164.93 159.50 164.05 158.43 164.05 c 157.36 164.05 156.48 164.93 156.48 166.00 c B 285.68 165.61 m 285.68 166.68 286.56 167.55 287.63 167.55 c 288.70 167.55 289.57 166.68 289.57 165.61 c 289.57 164.54 288.70 163.67 287.63 163.67 c 286.56 163.67 285.68 164.54 285.68 165.61 c B 286.07 162.85 m 286.07 163.92 286.95 164.79 288.02 164.79 c 289.08 164.79 289.96 163.92 289.96 162.85 c 289.96 161.78 289.08 160.90 288.02 160.90 c 286.95 160.90 286.07 161.78 286.07 162.85 c B 284.52 166.62 m 284.52 167.69 285.40 168.56 286.47 168.56 c 287.54 168.56 288.41 167.69 288.41 166.62 c 288.41 165.55 287.54 164.67 286.47 164.67 c 285.40 164.67 284.52 165.55 284.52 166.62 c B 139.75 170.04 m 139.75 171.11 140.62 171.99 141.69 171.99 c 142.76 171.99 143.63 171.11 143.63 170.04 c 143.63 168.97 142.76 168.10 141.69 168.10 c 140.62 168.10 139.75 168.97 139.75 170.04 c B 285.30 169.28 m 285.30 170.35 286.17 171.22 287.24 171.22 c 288.31 171.22 289.18 170.35 289.18 169.28 c 289.18 168.21 288.31 167.34 287.24 167.34 c 286.17 167.34 285.30 168.21 285.30 169.28 c B 285.68 170.75 m 285.68 171.82 286.56 172.70 287.63 172.70 c 288.70 172.70 289.57 171.82 289.57 170.75 c 289.57 169.68 288.70 168.81 287.63 168.81 c 286.56 168.81 285.68 169.68 285.68 170.75 c B 286.07 172.13 m 286.07 173.20 286.95 174.07 288.02 174.07 c 289.08 174.07 289.96 173.20 289.96 172.13 c 289.96 171.06 289.08 170.19 288.02 170.19 c 286.95 170.19 286.07 171.06 286.07 172.13 c B 285.68 170.88 m 285.68 171.95 286.56 172.82 287.63 172.82 c 288.70 172.82 289.57 171.95 289.57 170.88 c 289.57 169.81 288.70 168.94 287.63 168.94 c 286.56 168.94 285.68 169.81 285.68 170.88 c B 286.07 165.57 m 286.07 166.64 286.95 167.52 288.02 167.52 c 289.08 167.52 289.96 166.64 289.96 165.57 c 289.96 164.50 289.08 163.63 288.02 163.63 c 286.95 163.63 286.07 164.50 286.07 165.57 c B 284.91 169.67 m 284.91 170.74 285.78 171.61 286.85 171.61 c 287.92 171.61 288.80 170.74 288.80 169.67 c 288.80 168.60 287.92 167.73 286.85 167.73 c 285.78 167.73 284.91 168.60 284.91 169.67 c B 286.07 162.83 m 286.07 163.90 286.95 164.77 288.02 164.77 c 289.08 164.77 289.96 163.90 289.96 162.83 c 289.96 161.76 289.08 160.88 288.02 160.88 c 286.95 160.88 286.07 161.76 286.07 162.83 c B 285.30 172.23 m 285.30 173.30 286.17 174.17 287.24 174.17 c 288.31 174.17 289.18 173.30 289.18 172.23 c 289.18 171.16 288.31 170.29 287.24 170.29 c 286.17 170.29 285.30 171.16 285.30 172.23 c B 156.77 163.49 m 156.77 164.56 157.64 165.43 158.71 165.43 c 159.78 165.43 160.65 164.56 160.65 163.49 c 160.65 162.42 159.78 161.55 158.71 161.55 c 157.64 161.55 156.77 162.42 156.77 163.49 c B 285.30 169.92 m 285.30 170.98 286.17 171.86 287.24 171.86 c 288.31 171.86 289.18 170.98 289.18 169.92 c 289.18 168.85 288.31 167.97 287.24 167.97 c 286.17 167.97 285.30 168.85 285.30 169.92 c B 285.30 172.16 m 285.30 173.23 286.17 174.11 287.24 174.11 c 288.31 174.11 289.18 173.23 289.18 172.16 c 289.18 171.09 288.31 170.22 287.24 170.22 c 286.17 170.22 285.30 171.09 285.30 172.16 c B 285.68 170.89 m 285.68 171.96 286.56 172.84 287.63 172.84 c 288.70 172.84 289.57 171.96 289.57 170.89 c 289.57 169.83 288.70 168.95 287.63 168.95 c 286.56 168.95 285.68 169.83 285.68 170.89 c B 285.68 167.08 m 285.68 168.15 286.56 169.02 287.63 169.02 c 288.70 169.02 289.57 168.15 289.57 167.08 c 289.57 166.01 288.70 165.14 287.63 165.14 c 286.56 165.14 285.68 166.01 285.68 167.08 c B 285.68 164.82 m 285.68 165.89 286.56 166.76 287.63 166.76 c 288.70 166.76 289.57 165.89 289.57 164.82 c 289.57 163.75 288.70 162.87 287.63 162.87 c 286.56 162.87 285.68 163.75 285.68 164.82 c B 284.91 169.73 m 284.91 170.80 285.78 171.68 286.85 171.68 c 287.92 171.68 288.80 170.80 288.80 169.73 c 288.80 168.66 287.92 167.79 286.85 167.79 c 285.78 167.79 284.91 168.66 284.91 169.73 c B 286.46 168.27 m 286.46 169.34 287.33 170.22 288.40 170.22 c 289.47 170.22 290.35 169.34 290.35 168.27 c 290.35 167.20 289.47 166.33 288.40 166.33 c 287.33 166.33 286.46 167.20 286.46 168.27 c B 284.91 164.71 m 284.91 165.78 285.78 166.66 286.85 166.66 c 287.92 166.66 288.80 165.78 288.80 164.71 c 288.80 163.64 287.92 162.77 286.85 162.77 c 285.78 162.77 284.91 163.64 284.91 164.71 c B 285.68 164.77 m 285.68 165.84 286.56 166.72 287.63 166.72 c 288.70 166.72 289.57 165.84 289.57 164.77 c 289.57 163.70 288.70 162.83 287.63 162.83 c 286.56 162.83 285.68 163.70 285.68 164.77 c B 121.61 172.06 m 121.61 173.12 122.49 174.00 123.56 174.00 c 124.63 174.00 125.50 173.12 125.50 172.06 c 125.50 170.99 124.63 170.11 123.56 170.11 c 122.49 170.11 121.61 170.99 121.61 172.06 c B 202.65 166.99 m 202.65 168.06 203.52 168.94 204.59 168.94 c 205.66 168.94 206.54 168.06 206.54 166.99 c 206.54 165.92 205.66 165.05 204.59 165.05 c 203.52 165.05 202.65 165.92 202.65 166.99 c B 152.74 172.97 m 152.74 174.04 153.62 174.91 154.68 174.91 c 155.75 174.91 156.63 174.04 156.63 172.97 c 156.63 171.90 155.75 171.03 154.68 171.03 c 153.62 171.03 152.74 171.90 152.74 172.97 c B 273.73 168.46 m 273.73 169.52 274.61 170.40 275.68 170.40 c 276.75 170.40 277.62 169.52 277.62 168.46 c 277.62 167.39 276.75 166.51 275.68 166.51 c 274.61 166.51 273.73 167.39 273.73 168.46 c B 275.70 171.20 m 275.70 172.27 276.57 173.14 277.64 173.14 c 278.71 173.14 279.58 172.27 279.58 171.20 c 279.58 170.13 278.71 169.25 277.64 169.25 c 276.57 169.25 275.70 170.13 275.70 171.20 c B 110.98 172.28 m 110.98 173.35 111.86 174.22 112.93 174.22 c 113.99 174.22 114.87 173.35 114.87 172.28 c 114.87 171.21 113.99 170.34 112.93 170.34 c 111.86 170.34 110.98 171.21 110.98 172.28 c B 238.25 169.14 m 238.25 170.21 239.12 171.08 240.19 171.08 c 241.26 171.08 242.14 170.21 242.14 169.14 c 242.14 168.07 241.26 167.19 240.19 167.19 c 239.12 167.19 238.25 168.07 238.25 169.14 c B 259.47 171.64 m 259.47 172.70 260.35 173.58 261.42 173.58 c 262.49 173.58 263.36 172.70 263.36 171.64 c 263.36 170.57 262.49 169.69 261.42 169.69 c 260.35 169.69 259.47 170.57 259.47 171.64 c B 289.91 166.77 m 289.91 167.84 290.79 168.72 291.85 168.72 c 292.92 168.72 293.80 167.84 293.80 166.77 c 293.80 165.70 292.92 164.83 291.85 164.83 c 290.79 164.83 289.91 165.70 289.91 166.77 c B 289.21 173.88 m 289.21 174.95 290.09 175.83 291.16 175.83 c 292.23 175.83 293.10 174.95 293.10 173.88 c 293.10 172.81 292.23 171.94 291.16 171.94 c 290.09 171.94 289.21 172.81 289.21 173.88 c B 215.55 172.74 m 215.55 173.81 216.43 174.69 217.50 174.69 c 218.57 174.69 219.44 173.81 219.44 172.74 c 219.44 171.67 218.57 170.80 217.50 170.80 c 216.43 170.80 215.55 171.67 215.55 172.74 c B 293.76 173.71 m 293.76 174.78 294.64 175.66 295.71 175.66 c 296.77 175.66 297.65 174.78 297.65 173.71 c 297.65 172.64 296.77 171.77 295.71 171.77 c 294.64 171.77 293.76 172.64 293.76 173.71 c B 293.42 168.83 m 293.42 169.90 294.30 170.77 295.37 170.77 c 296.44 170.77 297.31 169.90 297.31 168.83 c 297.31 167.76 296.44 166.89 295.37 166.89 c 294.30 166.89 293.42 167.76 293.42 168.83 c B 293.03 170.13 m 293.03 171.20 293.91 172.07 294.98 172.07 c 296.05 172.07 296.92 171.20 296.92 170.13 c 296.92 169.06 296.05 168.18 294.98 168.18 c 293.91 168.18 293.03 169.06 293.03 170.13 c B 78.11 163.33 m 78.11 164.40 78.99 165.28 80.06 165.28 c 81.13 165.28 82.00 164.40 82.00 163.33 c 82.00 162.26 81.13 161.39 80.06 161.39 c 78.99 161.39 78.11 162.26 78.11 163.33 c B 243.47 163.51 m 243.47 164.57 244.34 165.45 245.41 165.45 c 246.48 165.45 247.35 164.57 247.35 163.51 c 247.35 162.44 246.48 161.56 245.41 161.56 c 244.34 161.56 243.47 162.44 243.47 163.51 c B 145.63 172.89 m 145.63 173.96 146.50 174.84 147.57 174.84 c 148.64 174.84 149.51 173.96 149.51 172.89 c 149.51 171.82 148.64 170.95 147.57 170.95 c 146.50 170.95 145.63 171.82 145.63 172.89 c B 218.29 167.64 m 218.29 168.71 219.17 169.59 220.24 169.59 c 221.31 169.59 222.18 168.71 222.18 167.64 c 222.18 166.57 221.31 165.70 220.24 165.70 c 219.17 165.70 218.29 166.57 218.29 167.64 c B 145.91 170.42 m 145.91 171.49 146.78 172.36 147.85 172.36 c 148.92 172.36 149.79 171.49 149.79 170.42 c 149.79 169.35 148.92 168.47 147.85 168.47 c 146.78 168.47 145.91 169.35 145.91 170.42 c B 88.52 169.85 m 88.52 170.92 89.40 171.80 90.47 171.80 c 91.54 171.80 92.41 170.92 92.41 169.85 c 92.41 168.78 91.54 167.91 90.47 167.91 c 89.40 167.91 88.52 168.78 88.52 169.85 c B 246.97 169.06 m 246.97 170.13 247.84 171.01 248.91 171.01 c 249.98 171.01 250.86 170.13 250.86 169.06 c 250.86 168.00 249.98 167.12 248.91 167.12 c 247.84 167.12 246.97 168.00 246.97 169.06 c B 273.30 170.48 m 273.30 171.55 274.17 172.42 275.24 172.42 c 276.31 172.42 277.18 171.55 277.18 170.48 c 277.18 169.41 276.31 168.53 275.24 168.53 c 274.17 168.53 273.30 169.41 273.30 170.48 c B 298.75 171.82 m 298.75 172.89 299.63 173.76 300.70 173.76 c 301.76 173.76 302.64 172.89 302.64 171.82 c 302.64 170.75 301.76 169.88 300.70 169.88 c 299.63 169.88 298.75 170.75 298.75 171.82 c B 149.40 167.67 m 149.40 168.74 150.27 169.61 151.34 169.61 c 152.41 169.61 153.28 168.74 153.28 167.67 c 153.28 166.60 152.41 165.72 151.34 165.72 c 150.27 165.72 149.40 166.60 149.40 167.67 c B 111.09 169.87 m 111.09 170.94 111.96 171.81 113.03 171.81 c 114.10 171.81 114.97 170.94 114.97 169.87 c 114.97 168.80 114.10 167.93 113.03 167.93 c 111.96 167.93 111.09 168.80 111.09 169.87 c B 143.24 173.27 m 143.24 174.34 144.11 175.22 145.18 175.22 c 146.25 175.22 147.13 174.34 147.13 173.27 c 147.13 172.20 146.25 171.33 145.18 171.33 c 144.11 171.33 143.24 172.20 143.24 173.27 c B 302.20 170.82 m 302.20 171.89 303.07 172.76 304.14 172.76 c 305.21 172.76 306.09 171.89 306.09 170.82 c 306.09 169.75 305.21 168.87 304.14 168.87 c 303.07 168.87 302.20 169.75 302.20 170.82 c B 150.87 169.86 m 150.87 170.93 151.74 171.80 152.81 171.80 c 153.88 171.80 154.76 170.93 154.76 169.86 c 154.76 168.79 153.88 167.91 152.81 167.91 c 151.74 167.91 150.87 168.79 150.87 169.86 c B 273.01 170.60 m 273.01 171.66 273.89 172.54 274.96 172.54 c 276.03 172.54 276.90 171.66 276.90 170.60 c 276.90 169.53 276.03 168.65 274.96 168.65 c 273.89 168.65 273.01 169.53 273.01 170.60 c B 162.99 167.08 m 162.99 168.15 163.86 169.03 164.93 169.03 c 166.00 169.03 166.88 168.15 166.88 167.08 c 166.88 166.01 166.00 165.14 164.93 165.14 c 163.86 165.14 162.99 166.01 162.99 167.08 c B 308.35 164.99 m 308.35 166.06 309.22 166.93 310.29 166.93 c 311.36 166.93 312.23 166.06 312.23 164.99 c 312.23 163.92 311.36 163.05 310.29 163.05 c 309.22 163.05 308.35 163.92 308.35 164.99 c B 185.27 163.31 m 185.27 164.38 186.15 165.26 187.22 165.26 c 188.29 165.26 189.16 164.38 189.16 163.31 c 189.16 162.25 188.29 161.37 187.22 161.37 c 186.15 161.37 185.27 162.25 185.27 163.31 c B 132.31 173.80 m 132.31 174.87 133.18 175.74 134.25 175.74 c 135.32 175.74 136.20 174.87 136.20 173.80 c 136.20 172.73 135.32 171.86 134.25 171.86 c 133.18 171.86 132.31 172.73 132.31 173.80 c B 249.97 167.41 m 249.97 168.48 250.85 169.35 251.92 169.35 c 252.99 169.35 253.86 168.48 253.86 167.41 c 253.86 166.34 252.99 165.46 251.92 165.46 c 250.85 165.46 249.97 166.34 249.97 167.41 c B 266.02 169.36 m 266.02 170.43 266.89 171.31 267.96 171.31 c 269.03 171.31 269.91 170.43 269.91 169.36 c 269.91 168.29 269.03 167.42 267.96 167.42 c 266.89 167.42 266.02 168.29 266.02 169.36 c B 312.65 162.84 m 312.65 163.91 313.53 164.79 314.59 164.79 c 315.66 164.79 316.54 163.91 316.54 162.84 c 316.54 161.77 315.66 160.90 314.59 160.90 c 313.53 160.90 312.65 161.77 312.65 162.84 c B 279.52 163.25 m 279.52 164.31 280.39 165.19 281.46 165.19 c 282.53 165.19 283.40 164.31 283.40 163.25 c 283.40 162.18 282.53 161.30 281.46 161.30 c 280.39 161.30 279.52 162.18 279.52 163.25 c B 311.57 164.99 m 311.57 166.06 312.44 166.94 313.51 166.94 c 314.58 166.94 315.45 166.06 315.45 164.99 c 315.45 163.93 314.58 163.05 313.51 163.05 c 312.44 163.05 311.57 163.93 311.57 164.99 c B 281.42 168.78 m 281.42 169.85 282.30 170.72 283.37 170.72 c 284.44 170.72 285.31 169.85 285.31 168.78 c 285.31 167.71 284.44 166.83 283.37 166.83 c 282.30 166.83 281.42 167.71 281.42 168.78 c B 159.37 170.46 m 159.37 171.53 160.25 172.41 161.31 172.41 c 162.38 172.41 163.26 171.53 163.26 170.46 c 163.26 169.39 162.38 168.52 161.31 168.52 c 160.25 168.52 159.37 169.39 159.37 170.46 c B 243.11 172.75 m 243.11 173.82 243.98 174.70 245.05 174.70 c 246.12 174.70 247.00 173.82 247.00 172.75 c 247.00 171.68 246.12 170.81 245.05 170.81 c 243.98 170.81 243.11 171.68 243.11 172.75 c B 314.17 167.00 m 314.17 168.07 315.04 168.95 316.11 168.95 c 317.18 168.95 318.05 168.07 318.05 167.00 c 318.05 165.93 317.18 165.06 316.11 165.06 c 315.04 165.06 314.17 165.93 314.17 167.00 c B 141.26 173.04 m 141.26 174.11 142.14 174.99 143.21 174.99 c 144.28 174.99 145.15 174.11 145.15 173.04 c 145.15 171.97 144.28 171.10 143.21 171.10 c 142.14 171.10 141.26 171.97 141.26 173.04 c B 276.93 173.66 m 276.93 174.73 277.80 175.61 278.87 175.61 c 279.94 175.61 280.82 174.73 280.82 173.66 c 280.82 172.59 279.94 171.72 278.87 171.72 c 277.80 171.72 276.93 172.59 276.93 173.66 c B 229.35 163.05 m 229.35 164.11 230.22 164.99 231.29 164.99 c 232.36 164.99 233.24 164.11 233.24 163.05 c 233.24 161.98 232.36 161.10 231.29 161.10 c 230.22 161.10 229.35 161.98 229.35 163.05 c B 161.09 166.51 m 161.09 167.58 161.96 168.46 163.03 168.46 c 164.10 168.46 164.98 167.58 164.98 166.51 c 164.98 165.44 164.10 164.57 163.03 164.57 c 161.96 164.57 161.09 165.44 161.09 166.51 c B 157.26 170.02 m 157.26 171.09 158.13 171.97 159.20 171.97 c 160.27 171.97 161.14 171.09 161.14 170.02 c 161.14 168.96 160.27 168.08 159.20 168.08 c 158.13 168.08 157.26 168.96 157.26 170.02 c B 318.36 172.62 m 318.36 173.69 319.23 174.56 320.30 174.56 c 321.37 174.56 322.24 173.69 322.24 172.62 c 322.24 171.55 321.37 170.68 320.30 170.68 c 319.23 170.68 318.36 171.55 318.36 172.62 c B 229.41 164.47 m 229.41 165.54 230.29 166.42 231.36 166.42 c 232.43 166.42 233.30 165.54 233.30 164.47 c 233.30 163.40 232.43 162.53 231.36 162.53 c 230.29 162.53 229.41 163.40 229.41 164.47 c B 307.18 170.25 m 307.18 171.32 308.06 172.19 309.13 172.19 c 310.20 172.19 311.07 171.32 311.07 170.25 c 311.07 169.18 310.20 168.30 309.13 168.30 c 308.06 168.30 307.18 169.18 307.18 170.25 c B 308.06 172.54 m 308.06 173.61 308.94 174.48 310.00 174.48 c 311.07 174.48 311.95 173.61 311.95 172.54 c 311.95 171.47 311.07 170.60 310.00 170.60 c 308.94 170.60 308.06 171.47 308.06 172.54 c B 292.74 169.05 m 292.74 170.12 293.62 170.99 294.69 170.99 c 295.76 170.99 296.63 170.12 296.63 169.05 c 296.63 167.98 295.76 167.11 294.69 167.11 c 293.62 167.11 292.74 167.98 292.74 169.05 c B 206.49 167.26 m 206.49 168.33 207.37 169.21 208.44 169.21 c 209.51 169.21 210.38 168.33 210.38 167.26 c 210.38 166.20 209.51 165.32 208.44 165.32 c 207.37 165.32 206.49 166.20 206.49 167.26 c B 77.63 162.97 m 77.63 164.04 78.51 164.92 79.57 164.92 c 80.64 164.92 81.52 164.04 81.52 162.97 c 81.52 161.90 80.64 161.03 79.57 161.03 c 78.51 161.03 77.63 161.90 77.63 162.97 c B 329.98 165.38 m 329.98 166.45 330.85 167.32 331.92 167.32 c 332.99 167.32 333.86 166.45 333.86 165.38 c 333.86 164.31 332.99 163.44 331.92 163.44 c 330.85 163.44 329.98 164.31 329.98 165.38 c B 201.48 163.12 m 201.48 164.19 202.36 165.06 203.43 165.06 c 204.50 165.06 205.37 164.19 205.37 163.12 c 205.37 162.05 204.50 161.17 203.43 161.17 c 202.36 161.17 201.48 162.05 201.48 163.12 c B 65.38 166.01 m 65.38 167.08 66.25 167.96 67.32 167.96 c 68.39 167.96 69.26 167.08 69.26 166.01 c 69.26 164.94 68.39 164.07 67.32 164.07 c 66.25 164.07 65.38 164.94 65.38 166.01 c B 194.20 168.16 m 194.20 169.23 195.07 170.10 196.14 170.10 c 197.21 170.10 198.09 169.23 198.09 168.16 c 198.09 167.09 197.21 166.22 196.14 166.22 c 195.07 166.22 194.20 167.09 194.20 168.16 c B 165.03 165.02 m 165.03 166.09 165.91 166.96 166.98 166.96 c 168.05 166.96 168.92 166.09 168.92 165.02 c 168.92 163.95 168.05 163.07 166.98 163.07 c 165.91 163.07 165.03 163.95 165.03 165.02 c B 252.54 163.51 m 252.54 164.58 253.42 165.46 254.49 165.46 c 255.56 165.46 256.43 164.58 256.43 163.51 c 256.43 162.44 255.56 161.57 254.49 161.57 c 253.42 161.57 252.54 162.44 252.54 163.51 c B 325.83 166.55 m 325.83 167.62 326.71 168.50 327.77 168.50 c 328.84 168.50 329.72 167.62 329.72 166.55 c 329.72 165.48 328.84 164.61 327.77 164.61 c 326.71 164.61 325.83 165.48 325.83 166.55 c B 104.91 166.08 m 104.91 167.15 105.78 168.03 106.85 168.03 c 107.92 168.03 108.80 167.15 108.80 166.08 c 108.80 165.01 107.92 164.14 106.85 164.14 c 105.78 164.14 104.91 165.01 104.91 166.08 c B 91.16 173.88 m 91.16 174.95 92.03 175.82 93.10 175.82 c 94.17 175.82 95.05 174.95 95.05 173.88 c 95.05 172.81 94.17 171.93 93.10 171.93 c 92.03 171.93 91.16 172.81 91.16 173.88 c B 288.27 167.77 m 288.27 168.84 289.15 169.71 290.21 169.71 c 291.28 169.71 292.16 168.84 292.16 167.77 c 292.16 166.70 291.28 165.83 290.21 165.83 c 289.15 165.83 288.27 166.70 288.27 167.77 c B 75.59 171.30 m 75.59 172.37 76.46 173.25 77.53 173.25 c 78.60 173.25 79.48 172.37 79.48 171.30 c 79.48 170.23 78.60 169.36 77.53 169.36 c 76.46 169.36 75.59 170.23 75.59 171.30 c B 328.10 169.77 m 328.10 170.84 328.97 171.71 330.04 171.71 c 331.11 171.71 331.98 170.84 331.98 169.77 c 331.98 168.70 331.11 167.82 330.04 167.82 c 328.97 167.82 328.10 168.70 328.10 169.77 c B 96.32 168.21 m 96.32 169.28 97.19 170.16 98.26 170.16 c 99.33 170.16 100.21 169.28 100.21 168.21 c 100.21 167.14 99.33 166.27 98.26 166.27 c 97.19 166.27 96.32 167.14 96.32 168.21 c B 275.96 165.83 m 275.96 166.90 276.83 167.77 277.90 167.77 c 278.97 167.77 279.85 166.90 279.85 165.83 c 279.85 164.76 278.97 163.89 277.90 163.89 c 276.83 163.89 275.96 164.76 275.96 165.83 c B 233.10 164.09 m 233.10 165.15 233.98 166.03 235.05 166.03 c 236.12 166.03 236.99 165.15 236.99 164.09 c 236.99 163.02 236.12 162.14 235.05 162.14 c 233.98 162.14 233.10 163.02 233.10 164.09 c B 62.31 163.11 m 62.31 164.18 63.19 165.05 64.25 165.05 c 65.32 165.05 66.20 164.18 66.20 163.11 c 66.20 162.04 65.32 161.17 64.25 161.17 c 63.19 161.17 62.31 162.04 62.31 163.11 c B 33.96 171.89 m 33.96 172.96 34.84 173.83 35.91 173.83 c 36.97 173.83 37.85 172.96 37.85 171.89 c 37.85 170.82 36.97 169.95 35.91 169.95 c 34.84 169.95 33.96 170.82 33.96 171.89 c B 108.30 172.58 m 108.30 173.64 109.17 174.52 110.24 174.52 c 111.31 174.52 112.19 173.64 112.19 172.58 c 112.19 171.51 111.31 170.63 110.24 170.63 c 109.17 170.63 108.30 171.51 108.30 172.58 c B 130.99 167.28 m 130.99 168.35 131.86 169.22 132.93 169.22 c 134.00 169.22 134.88 168.35 134.88 167.28 c 134.88 166.21 134.00 165.33 132.93 165.33 c 131.86 165.33 130.99 166.21 130.99 167.28 c B 174.05 163.94 m 174.05 165.01 174.93 165.88 176.00 165.88 c 177.07 165.88 177.94 165.01 177.94 163.94 c 177.94 162.87 177.07 162.00 176.00 162.00 c 174.93 162.00 174.05 162.87 174.05 163.94 c B 65.46 118.02 m 65.46 119.09 66.34 119.96 67.41 119.96 c 68.48 119.96 69.35 119.09 69.35 118.02 c 69.35 116.95 68.48 116.07 67.41 116.07 c 66.34 116.07 65.46 116.95 65.46 118.02 c B 307.03 120.16 m 307.03 121.23 307.91 122.10 308.97 122.10 c 310.04 122.10 310.92 121.23 310.92 120.16 c 310.92 119.09 310.04 118.21 308.97 118.21 c 307.91 118.21 307.03 119.09 307.03 120.16 c B 88.60 126.48 m 88.60 127.55 89.48 128.42 90.55 128.42 c 91.61 128.42 92.49 127.55 92.49 126.48 c 92.49 125.41 91.61 124.53 90.55 124.53 c 89.48 124.53 88.60 125.41 88.60 126.48 c B 61.79 126.72 m 61.79 127.79 62.66 128.66 63.73 128.66 c 64.80 128.66 65.67 127.79 65.67 126.72 c 65.67 125.65 64.80 124.78 63.73 124.78 c 62.66 124.78 61.79 125.65 61.79 126.72 c B 91.42 116.75 m 91.42 117.82 92.30 118.69 93.37 118.69 c 94.44 118.69 95.31 117.82 95.31 116.75 c 95.31 115.68 94.44 114.81 93.37 114.81 c 92.30 114.81 91.42 115.68 91.42 116.75 c B 146.31 118.27 m 146.31 119.34 147.19 120.22 148.26 120.22 c 149.33 120.22 150.20 119.34 150.20 118.27 c 150.20 117.20 149.33 116.33 148.26 116.33 c 147.19 116.33 146.31 117.20 146.31 118.27 c B 152.76 120.43 m 152.76 121.50 153.64 122.38 154.70 122.38 c 155.77 122.38 156.65 121.50 156.65 120.43 c 156.65 119.36 155.77 118.49 154.70 118.49 c 153.64 118.49 152.76 119.36 152.76 120.43 c B 133.68 116.01 m 133.68 117.08 134.56 117.96 135.63 117.96 c 136.69 117.96 137.57 117.08 137.57 116.01 c 137.57 114.94 136.69 114.07 135.63 114.07 c 134.56 114.07 133.68 114.94 133.68 116.01 c B 137.90 123.44 m 137.90 124.51 138.78 125.38 139.85 125.38 c 140.92 125.38 141.79 124.51 141.79 123.44 c 141.79 122.37 140.92 121.50 139.85 121.50 c 138.78 121.50 137.90 122.37 137.90 123.44 c B 214.55 124.22 m 214.55 125.29 215.42 126.17 216.49 126.17 c 217.56 126.17 218.43 125.29 218.43 124.22 c 218.43 123.15 217.56 122.28 216.49 122.28 c 215.42 122.28 214.55 123.15 214.55 124.22 c B 295.37 122.28 m 295.37 123.35 296.25 124.23 297.32 124.23 c 298.39 124.23 299.26 123.35 299.26 122.28 c 299.26 121.21 298.39 120.34 297.32 120.34 c 296.25 120.34 295.37 121.21 295.37 122.28 c B 165.11 125.37 m 165.11 126.44 165.98 127.31 167.05 127.31 c 168.12 127.31 168.99 126.44 168.99 125.37 c 168.99 124.30 168.12 123.42 167.05 123.42 c 165.98 123.42 165.11 124.30 165.11 125.37 c B 289.07 124.62 m 289.07 125.69 289.95 126.57 291.01 126.57 c 292.08 126.57 292.96 125.69 292.96 124.62 c 292.96 123.56 292.08 122.68 291.01 122.68 c 289.95 122.68 289.07 123.56 289.07 124.62 c B 71.21 122.60 m 71.21 123.67 72.08 124.54 73.15 124.54 c 74.22 124.54 75.10 123.67 75.10 122.60 c 75.10 121.53 74.22 120.65 73.15 120.65 c 72.08 120.65 71.21 121.53 71.21 122.60 c B 143.89 118.28 m 143.89 119.35 144.76 120.22 145.83 120.22 c 146.90 120.22 147.77 119.35 147.77 118.28 c 147.77 117.21 146.90 116.34 145.83 116.34 c 144.76 116.34 143.89 117.21 143.89 118.28 c B 216.68 125.97 m 216.68 127.04 217.55 127.91 218.62 127.91 c 219.69 127.91 220.56 127.04 220.56 125.97 c 220.56 124.90 219.69 124.03 218.62 124.03 c 217.55 124.03 216.68 124.90 216.68 125.97 c B 195.75 124.45 m 195.75 125.52 196.62 126.39 197.69 126.39 c 198.76 126.39 199.64 125.52 199.64 124.45 c 199.64 123.38 198.76 122.50 197.69 122.50 c 196.62 122.50 195.75 123.38 195.75 124.45 c B 287.12 117.50 m 287.12 118.57 288.00 119.45 289.07 119.45 c 290.13 119.45 291.01 118.57 291.01 117.50 c 291.01 116.43 290.13 115.56 289.07 115.56 c 288.00 115.56 287.12 116.43 287.12 117.50 c B 253.02 119.76 m 253.02 120.83 253.89 121.70 254.96 121.70 c 256.03 121.70 256.91 120.83 256.91 119.76 c 256.91 118.69 256.03 117.81 254.96 117.81 c 253.89 117.81 253.02 118.69 253.02 119.76 c B 323.76 125.93 m 323.76 127.00 324.64 127.88 325.71 127.88 c 326.77 127.88 327.65 127.00 327.65 125.93 c 327.65 124.87 326.77 123.99 325.71 123.99 c 324.64 123.99 323.76 124.87 323.76 125.93 c B 250.66 119.88 m 250.66 120.95 251.54 121.82 252.61 121.82 c 253.68 121.82 254.55 120.95 254.55 119.88 c 254.55 118.81 253.68 117.93 252.61 117.93 c 251.54 117.93 250.66 118.81 250.66 119.88 c B 158.69 125.79 m 158.69 126.86 159.57 127.73 160.63 127.73 c 161.70 127.73 162.58 126.86 162.58 125.79 c 162.58 124.72 161.70 123.85 160.63 123.85 c 159.57 123.85 158.69 124.72 158.69 125.79 c B 111.79 123.27 m 111.79 124.34 112.67 125.21 113.74 125.21 c 114.81 125.21 115.68 124.34 115.68 123.27 c 115.68 122.20 114.81 121.33 113.74 121.33 c 112.67 121.33 111.79 122.20 111.79 123.27 c B 321.08 121.69 m 321.08 122.76 321.96 123.63 323.03 123.63 c 324.10 123.63 324.97 122.76 324.97 121.69 c 324.97 120.62 324.10 119.75 323.03 119.75 c 321.96 119.75 321.08 120.62 321.08 121.69 c B 121.77 117.76 m 121.77 118.83 122.65 119.71 123.72 119.71 c 124.78 119.71 125.66 118.83 125.66 117.76 c 125.66 116.70 124.78 115.82 123.72 115.82 c 122.65 115.82 121.77 116.70 121.77 117.76 c B 318.51 119.60 m 318.51 120.67 319.38 121.54 320.45 121.54 c 321.52 121.54 322.40 120.67 322.40 119.60 c 322.40 118.53 321.52 117.65 320.45 117.65 c 319.38 117.65 318.51 118.53 318.51 119.60 c B 316.93 126.97 m 316.93 128.04 317.81 128.91 318.88 128.91 c 319.95 128.91 320.82 128.04 320.82 126.97 c 320.82 125.90 319.95 125.03 318.88 125.03 c 317.81 125.03 316.93 125.90 316.93 126.97 c B 238.00 116.09 m 238.00 117.16 238.87 118.04 239.94 118.04 c 241.01 118.04 241.89 117.16 241.89 116.09 c 241.89 115.03 241.01 114.15 239.94 114.15 c 238.87 114.15 238.00 115.03 238.00 116.09 c B 204.38 117.83 m 204.38 118.90 205.26 119.77 206.33 119.77 c 207.40 119.77 208.27 118.90 208.27 117.83 c 208.27 116.76 207.40 115.88 206.33 115.88 c 205.26 115.88 204.38 116.76 204.38 117.83 c B 298.79 119.77 m 298.79 120.84 299.66 121.71 300.73 121.71 c 301.80 121.71 302.67 120.84 302.67 119.77 c 302.67 118.70 301.80 117.82 300.73 117.82 c 299.66 117.82 298.79 118.70 298.79 119.77 c B 285.48 117.10 m 285.48 118.17 286.36 119.04 287.43 119.04 c 288.49 119.04 289.37 118.17 289.37 117.10 c 289.37 116.03 288.49 115.15 287.43 115.15 c 286.36 115.15 285.48 116.03 285.48 117.10 c B 108.39 123.56 m 108.39 124.63 109.27 125.51 110.34 125.51 c 111.41 125.51 112.28 124.63 112.28 123.56 c 112.28 122.49 111.41 121.62 110.34 121.62 c 109.27 121.62 108.39 122.49 108.39 123.56 c B 306.92 126.04 m 306.92 127.11 307.79 127.99 308.86 127.99 c 309.93 127.99 310.81 127.11 310.81 126.04 c 310.81 124.97 309.93 124.10 308.86 124.10 c 307.79 124.10 306.92 124.97 306.92 126.04 c B 269.84 125.02 m 269.84 126.09 270.72 126.97 271.79 126.97 c 272.86 126.97 273.73 126.09 273.73 125.02 c 273.73 123.95 272.86 123.08 271.79 123.08 c 270.72 123.08 269.84 123.95 269.84 125.02 c B 310.41 121.90 m 310.41 122.97 311.28 123.84 312.35 123.84 c 313.42 123.84 314.30 122.97 314.30 121.90 c 314.30 120.83 313.42 119.95 312.35 119.95 c 311.28 119.95 310.41 120.83 310.41 121.90 c B 292.39 118.30 m 292.39 119.37 293.27 120.24 294.34 120.24 c 295.41 120.24 296.28 119.37 296.28 118.30 c 296.28 117.23 295.41 116.35 294.34 116.35 c 293.27 116.35 292.39 117.23 292.39 118.30 c B 304.12 123.62 m 304.12 124.69 304.99 125.56 306.06 125.56 c 307.13 125.56 308.00 124.69 308.00 123.62 c 308.00 122.55 307.13 121.68 306.06 121.68 c 304.99 121.68 304.12 122.55 304.12 123.62 c B 172.22 125.57 m 172.22 126.64 173.09 127.52 174.16 127.52 c 175.23 127.52 176.11 126.64 176.11 125.57 c 176.11 124.51 175.23 123.63 174.16 123.63 c 173.09 123.63 172.22 124.51 172.22 125.57 c B 304.95 116.93 m 304.95 118.00 305.83 118.88 306.89 118.88 c 307.96 118.88 308.84 118.00 308.84 116.93 c 308.84 115.86 307.96 114.99 306.89 114.99 c 305.83 114.99 304.95 115.86 304.95 116.93 c B 304.52 124.07 m 304.52 125.14 305.40 126.01 306.47 126.01 c 307.54 126.01 308.41 125.14 308.41 124.07 c 308.41 123.00 307.54 122.12 306.47 122.12 c 305.40 122.12 304.52 123.00 304.52 124.07 c B 286.61 118.11 m 286.61 119.18 287.49 120.06 288.56 120.06 c 289.63 120.06 290.50 119.18 290.50 118.11 c 290.50 117.04 289.63 116.17 288.56 116.17 c 287.49 116.17 286.61 117.04 286.61 118.11 c B 270.27 120.29 m 270.27 121.35 271.14 122.23 272.21 122.23 c 273.28 122.23 274.16 121.35 274.16 120.29 c 274.16 119.22 273.28 118.34 272.21 118.34 c 271.14 118.34 270.27 119.22 270.27 120.29 c B 302.92 116.31 m 302.92 117.38 303.80 118.25 304.87 118.25 c 305.94 118.25 306.81 117.38 306.81 116.31 c 306.81 115.24 305.94 114.37 304.87 114.37 c 303.80 114.37 302.92 115.24 302.92 116.31 c B 272.83 118.29 m 272.83 119.36 273.70 120.23 274.77 120.23 c 275.84 120.23 276.72 119.36 276.72 118.29 c 276.72 117.22 275.84 116.34 274.77 116.34 c 273.70 116.34 272.83 117.22 272.83 118.29 c B 276.02 124.62 m 276.02 125.69 276.89 126.57 277.96 126.57 c 279.03 126.57 279.91 125.69 279.91 124.62 c 279.91 123.55 279.03 122.68 277.96 122.68 c 276.89 122.68 276.02 123.55 276.02 124.62 c B 283.16 123.74 m 283.16 124.81 284.04 125.69 285.11 125.69 c 286.18 125.69 287.05 124.81 287.05 123.74 c 287.05 122.67 286.18 121.80 285.11 121.80 c 284.04 121.80 283.16 122.67 283.16 123.74 c B 292.73 116.47 m 292.73 117.54 293.60 118.41 294.67 118.41 c 295.74 118.41 296.62 117.54 296.62 116.47 c 296.62 115.40 295.74 114.52 294.67 114.52 c 293.60 114.52 292.73 115.40 292.73 116.47 c B 283.79 125.06 m 283.79 126.13 284.66 127.01 285.73 127.01 c 286.80 127.01 287.67 126.13 287.67 125.06 c 287.67 123.99 286.80 123.12 285.73 123.12 c 284.66 123.12 283.79 123.99 283.79 125.06 c B 277.97 121.45 m 277.97 122.52 278.84 123.39 279.91 123.39 c 280.98 123.39 281.85 122.52 281.85 121.45 c 281.85 120.38 280.98 119.51 279.91 119.51 c 278.84 119.51 277.97 120.38 277.97 121.45 c B 292.09 121.77 m 292.09 122.84 292.96 123.71 294.03 123.71 c 295.10 123.71 295.98 122.84 295.98 121.77 c 295.98 120.70 295.10 119.83 294.03 119.83 c 292.96 119.83 292.09 120.70 292.09 121.77 c B 228.78 121.17 m 228.78 122.24 229.66 123.11 230.73 123.11 c 231.80 123.11 232.67 122.24 232.67 121.17 c 232.67 120.10 231.80 119.23 230.73 119.23 c 229.66 119.23 228.78 120.10 228.78 121.17 c B 187.04 119.84 m 187.04 120.91 187.91 121.79 188.98 121.79 c 190.05 121.79 190.93 120.91 190.93 119.84 c 190.93 118.77 190.05 117.90 188.98 117.90 c 187.91 117.90 187.04 118.77 187.04 119.84 c B 259.50 119.33 m 259.50 120.40 260.38 121.27 261.45 121.27 c 262.52 121.27 263.39 120.40 263.39 119.33 c 263.39 118.26 262.52 117.38 261.45 117.38 c 260.38 117.38 259.50 118.26 259.50 119.33 c B 75.63 122.30 m 75.63 123.37 76.50 124.24 77.57 124.24 c 78.64 124.24 79.52 123.37 79.52 122.30 c 79.52 121.23 78.64 120.35 77.57 120.35 c 76.50 120.35 75.63 121.23 75.63 122.30 c B 283.15 126.82 m 283.15 127.88 284.03 128.76 285.10 128.76 c 286.17 128.76 287.04 127.88 287.04 126.82 c 287.04 125.75 286.17 124.87 285.10 124.87 c 284.03 124.87 283.15 125.75 283.15 126.82 c B 286.71 117.57 m 286.71 118.64 287.59 119.52 288.66 119.52 c 289.73 119.52 290.60 118.64 290.60 117.57 c 290.60 116.51 289.73 115.63 288.66 115.63 c 287.59 115.63 286.71 116.51 286.71 117.57 c B 289.15 125.47 m 289.15 126.54 290.02 127.42 291.09 127.42 c 292.16 127.42 293.04 126.54 293.04 125.47 c 293.04 124.40 292.16 123.53 291.09 123.53 c 290.02 123.53 289.15 124.40 289.15 125.47 c B 105.07 117.04 m 105.07 118.11 105.94 118.99 107.01 118.99 c 108.08 118.99 108.96 118.11 108.96 117.04 c 108.96 115.98 108.08 115.10 107.01 115.10 c 105.94 115.10 105.07 115.98 105.07 117.04 c B 294.89 118.83 m 294.89 119.90 295.76 120.78 296.83 120.78 c 297.90 120.78 298.78 119.90 298.78 118.83 c 298.78 117.76 297.90 116.89 296.83 116.89 c 295.76 116.89 294.89 117.76 294.89 118.83 c B 294.06 119.64 m 294.06 120.71 294.93 121.59 296.00 121.59 c 297.07 121.59 297.95 120.71 297.95 119.64 c 297.95 118.57 297.07 117.70 296.00 117.70 c 294.93 117.70 294.06 118.57 294.06 119.64 c B 290.12 118.69 m 290.12 119.76 290.99 120.63 292.06 120.63 c 293.13 120.63 294.01 119.76 294.01 118.69 c 294.01 117.62 293.13 116.75 292.06 116.75 c 290.99 116.75 290.12 117.62 290.12 118.69 c B 284.30 117.82 m 284.30 118.89 285.17 119.77 286.24 119.77 c 287.31 119.77 288.18 118.89 288.18 117.82 c 288.18 116.75 287.31 115.88 286.24 115.88 c 285.17 115.88 284.30 116.75 284.30 117.82 c B 295.15 124.09 m 295.15 125.16 296.03 126.03 297.10 126.03 c 298.17 126.03 299.04 125.16 299.04 124.09 c 299.04 123.02 298.17 122.14 297.10 122.14 c 296.03 122.14 295.15 123.02 295.15 124.09 c B 163.28 119.92 m 163.28 120.99 164.16 121.86 165.23 121.86 c 166.30 121.86 167.17 120.99 167.17 119.92 c 167.17 118.85 166.30 117.98 165.23 117.98 c 164.16 117.98 163.28 118.85 163.28 119.92 c B 282.34 121.17 m 282.34 122.24 283.22 123.12 284.29 123.12 c 285.36 123.12 286.23 122.24 286.23 121.17 c 286.23 120.10 285.36 119.23 284.29 119.23 c 283.22 119.23 282.34 120.10 282.34 121.17 c B 290.82 119.16 m 290.82 120.23 291.70 121.10 292.77 121.10 c 293.83 121.10 294.71 120.23 294.71 119.16 c 294.71 118.09 293.83 117.22 292.77 117.22 c 291.70 117.22 290.82 118.09 290.82 119.16 c B 285.88 118.86 m 285.88 119.93 286.76 120.81 287.83 120.81 c 288.90 120.81 289.77 119.93 289.77 118.86 c 289.77 117.79 288.90 116.92 287.83 116.92 c 286.76 116.92 285.88 117.79 285.88 118.86 c B 146.06 115.96 m 146.06 117.03 146.93 117.91 148.00 117.91 c 149.07 117.91 149.95 117.03 149.95 115.96 c 149.95 114.89 149.07 114.02 148.00 114.02 c 146.93 114.02 146.06 114.89 146.06 115.96 c B 270.15 123.47 m 270.15 124.54 271.03 125.41 272.10 125.41 c 273.17 125.41 274.04 124.54 274.04 123.47 c 274.04 122.40 273.17 121.52 272.10 121.52 c 271.03 121.52 270.15 122.40 270.15 123.47 c B 280.80 123.48 m 280.80 124.55 281.67 125.42 282.74 125.42 c 283.81 125.42 284.68 124.55 284.68 123.48 c 284.68 122.41 283.81 121.54 282.74 121.54 c 281.67 121.54 280.80 122.41 280.80 123.48 c B 161.79 124.45 m 161.79 125.52 162.66 126.39 163.73 126.39 c 164.80 126.39 165.68 125.52 165.68 124.45 c 165.68 123.38 164.80 122.50 163.73 122.50 c 162.66 122.50 161.79 123.38 161.79 124.45 c B 230.12 123.06 m 230.12 124.13 230.99 125.01 232.06 125.01 c 233.13 125.01 234.00 124.13 234.00 123.06 c 234.00 122.00 233.13 121.12 232.06 121.12 c 230.99 121.12 230.12 122.00 230.12 123.06 c B 285.04 120.14 m 285.04 121.21 285.92 122.08 286.99 122.08 c 288.05 122.08 288.93 121.21 288.93 120.14 c 288.93 119.07 288.05 118.20 286.99 118.20 c 285.92 118.20 285.04 119.07 285.04 120.14 c B 282.82 124.04 m 282.82 125.11 283.70 125.98 284.77 125.98 c 285.84 125.98 286.71 125.11 286.71 124.04 c 286.71 122.97 285.84 122.09 284.77 122.09 c 283.70 122.09 282.82 122.97 282.82 124.04 c B 149.09 120.61 m 149.09 121.68 149.96 122.55 151.03 122.55 c 152.10 122.55 152.97 121.68 152.97 120.61 c 152.97 119.54 152.10 118.66 151.03 118.66 c 149.96 118.66 149.09 119.54 149.09 120.61 c B 289.84 121.34 m 289.84 122.41 290.72 123.28 291.79 123.28 c 292.85 123.28 293.73 122.41 293.73 121.34 c 293.73 120.27 292.85 119.39 291.79 119.39 c 290.72 119.39 289.84 120.27 289.84 121.34 c B 279.67 121.90 m 279.67 122.97 280.54 123.84 281.61 123.84 c 282.68 123.84 283.55 122.97 283.55 121.90 c 283.55 120.83 282.68 119.95 281.61 119.95 c 280.54 119.95 279.67 120.83 279.67 121.90 c B 275.08 124.70 m 275.08 125.77 275.95 126.65 277.02 126.65 c 278.09 126.65 278.97 125.77 278.97 124.70 c 278.97 123.63 278.09 122.76 277.02 122.76 c 275.95 122.76 275.08 123.63 275.08 124.70 c B 289.11 123.33 m 289.11 124.40 289.98 125.28 291.05 125.28 c 292.12 125.28 293.00 124.40 293.00 123.33 c 293.00 122.26 292.12 121.39 291.05 121.39 c 289.98 121.39 289.11 122.26 289.11 123.33 c B 96.36 121.53 m 96.36 122.60 97.24 123.48 98.31 123.48 c 99.38 123.48 100.25 122.60 100.25 121.53 c 100.25 120.46 99.38 119.59 98.31 119.59 c 97.24 119.59 96.36 120.46 96.36 121.53 c B 289.23 117.20 m 289.23 118.27 290.10 119.15 291.17 119.15 c 292.24 119.15 293.12 118.27 293.12 117.20 c 293.12 116.14 292.24 115.26 291.17 115.26 c 290.10 115.26 289.23 116.14 289.23 117.20 c B 77.75 122.19 m 77.75 123.26 78.62 124.13 79.69 124.13 c 80.76 124.13 81.64 123.26 81.64 122.19 c 81.64 121.12 80.76 120.24 79.69 120.24 c 78.62 120.24 77.75 121.12 77.75 122.19 c B 288.17 118.12 m 288.17 119.18 289.04 120.06 290.11 120.06 c 291.18 120.06 292.05 119.18 292.05 118.12 c 292.05 117.05 291.18 116.17 290.11 116.17 c 289.04 116.17 288.17 117.05 288.17 118.12 c B 154.90 125.40 m 154.90 126.47 155.78 127.34 156.85 127.34 c 157.91 127.34 158.79 126.47 158.79 125.40 c 158.79 124.33 157.91 123.46 156.85 123.46 c 155.78 123.46 154.90 124.33 154.90 125.40 c B 287.93 125.31 m 287.93 126.38 288.80 127.25 289.87 127.25 c 290.94 127.25 291.82 126.38 291.82 125.31 c 291.82 124.24 290.94 123.37 289.87 123.37 c 288.80 123.37 287.93 124.24 287.93 125.31 c B 288.30 125.07 m 288.30 126.14 289.17 127.02 290.24 127.02 c 291.31 127.02 292.19 126.14 292.19 125.07 c 292.19 124.00 291.31 123.13 290.24 123.13 c 289.17 123.13 288.30 124.00 288.30 125.07 c B 287.73 122.12 m 287.73 123.19 288.60 124.06 289.67 124.06 c 290.74 124.06 291.62 123.19 291.62 122.12 c 291.62 121.05 290.74 120.17 289.67 120.17 c 288.60 120.17 287.73 121.05 287.73 122.12 c B 286.77 121.79 m 286.77 122.86 287.65 123.73 288.72 123.73 c 289.79 123.73 290.66 122.86 290.66 121.79 c 290.66 120.72 289.79 119.84 288.72 119.84 c 287.65 119.84 286.77 120.72 286.77 121.79 c B 281.65 126.97 m 281.65 128.04 282.53 128.92 283.59 128.92 c 284.66 128.92 285.54 128.04 285.54 126.97 c 285.54 125.90 284.66 125.03 283.59 125.03 c 282.53 125.03 281.65 125.90 281.65 126.97 c B 150.93 122.50 m 150.93 123.57 151.81 124.45 152.88 124.45 c 153.95 124.45 154.82 123.57 154.82 122.50 c 154.82 121.43 153.95 120.56 152.88 120.56 c 151.81 120.56 150.93 121.43 150.93 122.50 c B 271.32 119.50 m 271.32 120.57 272.19 121.44 273.26 121.44 c 274.33 121.44 275.20 120.57 275.20 119.50 c 275.20 118.43 274.33 117.55 273.26 117.55 c 272.19 117.55 271.32 118.43 271.32 119.50 c B 286.04 119.87 m 286.04 120.94 286.91 121.81 287.98 121.81 c 289.05 121.81 289.93 120.94 289.93 119.87 c 289.93 118.80 289.05 117.93 287.98 117.93 c 286.91 117.93 286.04 118.80 286.04 119.87 c B 272.43 122.87 m 272.43 123.94 273.31 124.81 274.37 124.81 c 275.44 124.81 276.32 123.94 276.32 122.87 c 276.32 121.80 275.44 120.93 274.37 120.93 c 273.31 120.93 272.43 121.80 272.43 122.87 c B 284.33 121.85 m 284.33 122.92 285.21 123.80 286.28 123.80 c 287.35 123.80 288.22 122.92 288.22 121.85 c 288.22 120.79 287.35 119.91 286.28 119.91 c 285.21 119.91 284.33 120.79 284.33 121.85 c B 287.23 126.11 m 287.23 127.18 288.10 128.06 289.17 128.06 c 290.24 128.06 291.11 127.18 291.11 126.11 c 291.11 125.04 290.24 124.17 289.17 124.17 c 288.10 124.17 287.23 125.04 287.23 126.11 c B 287.15 116.97 m 287.15 118.04 288.03 118.92 289.10 118.92 c 290.17 118.92 291.04 118.04 291.04 116.97 c 291.04 115.91 290.17 115.03 289.10 115.03 c 288.03 115.03 287.15 115.91 287.15 116.97 c B 287.04 123.93 m 287.04 125.00 287.92 125.87 288.99 125.87 c 290.06 125.87 290.93 125.00 290.93 123.93 c 290.93 122.86 290.06 121.98 288.99 121.98 c 287.92 121.98 287.04 122.86 287.04 123.93 c B 139.78 122.14 m 139.78 123.21 140.66 124.08 141.73 124.08 c 142.80 124.08 143.67 123.21 143.67 122.14 c 143.67 121.07 142.80 120.20 141.73 120.20 c 140.66 120.20 139.78 121.07 139.78 122.14 c B 286.84 124.03 m 286.84 125.10 287.72 125.97 288.79 125.97 c 289.86 125.97 290.73 125.10 290.73 124.03 c 290.73 122.96 289.86 122.08 288.79 122.08 c 287.72 122.08 286.84 122.96 286.84 124.03 c B 265.34 123.20 m 265.34 124.27 266.22 125.15 267.29 125.15 c 268.36 125.15 269.23 124.27 269.23 123.20 c 269.23 122.13 268.36 121.26 267.29 121.26 c 266.22 121.26 265.34 122.13 265.34 123.20 c B 284.20 120.50 m 284.20 121.57 285.08 122.45 286.15 122.45 c 287.22 122.45 288.09 121.57 288.09 120.50 c 288.09 119.43 287.22 118.56 286.15 118.56 c 285.08 118.56 284.20 119.43 284.20 120.50 c B 34.72 122.79 m 34.72 123.86 35.59 124.73 36.66 124.73 c 37.73 124.73 38.60 123.86 38.60 122.79 c 38.60 121.72 37.73 120.85 36.66 120.85 c 35.59 120.85 34.72 121.72 34.72 122.79 c B 77.16 118.77 m 77.16 119.84 78.03 120.71 79.10 120.71 c 80.17 120.71 81.04 119.84 81.04 118.77 c 81.04 117.70 80.17 116.83 79.10 116.83 c 78.03 116.83 77.16 117.70 77.16 118.77 c B 202.77 121.16 m 202.77 122.23 203.64 123.10 204.71 123.10 c 205.78 123.10 206.66 122.23 206.66 121.16 c 206.66 120.09 205.78 119.22 204.71 119.22 c 203.64 119.22 202.77 120.09 202.77 121.16 c B 207.27 116.52 m 207.27 117.59 208.14 118.47 209.21 118.47 c 210.28 118.47 211.15 117.59 211.15 116.52 c 211.15 115.45 210.28 114.58 209.21 114.58 c 208.14 114.58 207.27 115.45 207.27 116.52 c B 233.21 116.68 m 233.21 117.75 234.08 118.62 235.15 118.62 c 236.22 118.62 237.09 117.75 237.09 116.68 c 237.09 115.61 236.22 114.74 235.15 114.74 c 234.08 114.74 233.21 115.61 233.21 116.68 c B 110.32 119.28 m 110.32 120.35 111.19 121.23 112.26 121.23 c 113.33 121.23 114.21 120.35 114.21 119.28 c 114.21 118.22 113.33 117.34 112.26 117.34 c 111.19 117.34 110.32 118.22 110.32 119.28 c B 238.48 125.46 m 238.48 126.53 239.36 127.41 240.43 127.41 c 241.50 127.41 242.37 126.53 242.37 125.46 c 242.37 124.39 241.50 123.52 240.43 123.52 c 239.36 123.52 238.48 124.39 238.48 125.46 c B 238.57 117.75 m 238.57 118.82 239.44 119.70 240.51 119.70 c 241.58 119.70 242.45 118.82 242.45 117.75 c 242.45 116.68 241.58 115.81 240.51 115.81 c 239.44 115.81 238.57 116.68 238.57 117.75 c B 250.33 118.09 m 250.33 119.16 251.20 120.03 252.27 120.03 c 253.34 120.03 254.21 119.16 254.21 118.09 c 254.21 117.02 253.34 116.14 252.27 116.14 c 251.20 116.14 250.33 117.02 250.33 118.09 c B 262.35 119.72 m 262.35 120.79 263.23 121.66 264.30 121.66 c 265.37 121.66 266.24 120.79 266.24 119.72 c 266.24 118.65 265.37 117.77 264.30 117.77 c 263.23 117.77 262.35 118.65 262.35 119.72 c B 263.06 119.94 m 263.06 121.01 263.94 121.88 265.01 121.88 c 266.08 121.88 266.95 121.01 266.95 119.94 c 266.95 118.87 266.08 117.99 265.01 117.99 c 263.94 117.99 263.06 118.87 263.06 119.94 c B 262.71 124.54 m 262.71 125.61 263.58 126.49 264.65 126.49 c 265.72 126.49 266.60 125.61 266.60 124.54 c 266.60 123.47 265.72 122.60 264.65 122.60 c 263.58 122.60 262.71 123.47 262.71 124.54 c B 141.23 118.24 m 141.23 119.31 142.11 120.18 143.17 120.18 c 144.24 120.18 145.12 119.31 145.12 118.24 c 145.12 117.17 144.24 116.29 143.17 116.29 c 142.11 116.29 141.23 117.17 141.23 118.24 c B 264.82 118.25 m 264.82 119.32 265.70 120.20 266.76 120.20 c 267.83 120.20 268.71 119.32 268.71 118.25 c 268.71 117.18 267.83 116.31 266.76 116.31 c 265.70 116.31 264.82 117.18 264.82 118.25 c B 265.35 124.69 m 265.35 125.76 266.22 126.63 267.29 126.63 c 268.36 126.63 269.24 125.76 269.24 124.69 c 269.24 123.62 268.36 122.74 267.29 122.74 c 266.22 122.74 265.35 123.62 265.35 124.69 c B 267.63 117.88 m 267.63 118.94 268.51 119.82 269.58 119.82 c 270.64 119.82 271.52 118.94 271.52 117.88 c 271.52 116.81 270.64 115.93 269.58 115.93 c 268.51 115.93 267.63 116.81 267.63 117.88 c B 265.53 120.61 m 265.53 121.68 266.40 122.55 267.47 122.55 c 268.54 122.55 269.42 121.68 269.42 120.61 c 269.42 119.54 268.54 118.66 267.47 118.66 c 266.40 118.66 265.53 119.54 265.53 120.61 c B 130.84 117.60 m 130.84 118.67 131.72 119.54 132.79 119.54 c 133.86 119.54 134.73 118.67 134.73 117.60 c 134.73 116.53 133.86 115.65 132.79 115.65 c 131.72 115.65 130.84 116.53 130.84 117.60 c B 276.13 117.89 m 276.13 118.96 277.01 119.84 278.08 119.84 c 279.15 119.84 280.02 118.96 280.02 117.89 c 280.02 116.82 279.15 115.95 278.08 115.95 c 277.01 115.95 276.13 116.82 276.13 117.89 c B 278.04 119.27 m 278.04 120.34 278.92 121.21 279.99 121.21 c 281.05 121.21 281.93 120.34 281.93 119.27 c 281.93 118.20 281.05 117.32 279.99 117.32 c 278.92 117.32 278.04 118.20 278.04 119.27 c B 278.02 119.99 m 278.02 121.06 278.89 121.93 279.96 121.93 c 281.03 121.93 281.90 121.06 281.90 119.99 c 281.90 118.92 281.03 118.05 279.96 118.05 c 278.89 118.05 278.02 118.92 278.02 119.99 c B 279.46 126.26 m 279.46 127.33 280.34 128.21 281.41 128.21 c 282.48 128.21 283.35 127.33 283.35 126.26 c 283.35 125.19 282.48 124.32 281.41 124.32 c 280.34 124.32 279.46 125.19 279.46 126.26 c B 276.87 120.81 m 276.87 121.88 277.75 122.75 278.82 122.75 c 279.89 122.75 280.76 121.88 280.76 120.81 c 280.76 119.74 279.89 118.87 278.82 118.87 c 277.75 118.87 276.87 119.74 276.87 120.81 c B 281.12 120.91 m 281.12 121.98 282.00 122.85 283.07 122.85 c 284.14 122.85 285.01 121.98 285.01 120.91 c 285.01 119.84 284.14 118.96 283.07 118.96 c 282.00 118.96 281.12 119.84 281.12 120.91 c B 285.38 123.71 m 285.38 124.78 286.25 125.65 287.32 125.65 c 288.39 125.65 289.26 124.78 289.26 123.71 c 289.26 122.64 288.39 121.77 287.32 121.77 c 286.25 121.77 285.38 122.64 285.38 123.71 c B 284.52 118.08 m 284.52 119.15 285.40 120.02 286.47 120.02 c 287.54 120.02 288.41 119.15 288.41 118.08 c 288.41 117.01 287.54 116.14 286.47 116.14 c 285.40 116.14 284.52 117.01 284.52 118.08 c B 156.77 122.03 m 156.77 123.09 157.64 123.97 158.71 123.97 c 159.78 123.97 160.65 123.09 160.65 122.03 c 160.65 120.96 159.78 120.08 158.71 120.08 c 157.64 120.08 156.77 120.96 156.77 122.03 c B 285.30 124.31 m 285.30 125.38 286.17 126.26 287.24 126.26 c 288.31 126.26 289.18 125.38 289.18 124.31 c 289.18 123.24 288.31 122.37 287.24 122.37 c 286.17 122.37 285.30 123.24 285.30 124.31 c B 139.75 126.70 m 139.75 127.77 140.62 128.64 141.69 128.64 c 142.76 128.64 143.63 127.77 143.63 126.70 c 143.63 125.63 142.76 124.76 141.69 124.76 c 140.62 124.76 139.75 125.63 139.75 126.70 c B 284.52 122.65 m 284.52 123.72 285.40 124.60 286.47 124.60 c 287.54 124.60 288.41 123.72 288.41 122.65 c 288.41 121.58 287.54 120.71 286.47 120.71 c 285.40 120.71 284.52 121.58 284.52 122.65 c B 284.91 124.96 m 284.91 126.03 285.78 126.91 286.85 126.91 c 287.92 126.91 288.80 126.03 288.80 124.96 c 288.80 123.89 287.92 123.02 286.85 123.02 c 285.78 123.02 284.91 123.89 284.91 124.96 c B 286.85 125.78 m 286.85 126.85 287.72 127.73 288.79 127.73 c 289.86 127.73 290.73 126.85 290.73 125.78 c 290.73 124.71 289.86 123.84 288.79 123.84 c 287.72 123.84 286.85 124.71 286.85 125.78 c B 284.91 126.97 m 284.91 128.04 285.78 128.92 286.85 128.92 c 287.92 128.92 288.80 128.04 288.80 126.97 c 288.80 125.90 287.92 125.03 286.85 125.03 c 285.78 125.03 284.91 125.90 284.91 126.97 c B 286.07 116.80 m 286.07 117.87 286.95 118.74 288.02 118.74 c 289.08 118.74 289.96 117.87 289.96 116.80 c 289.96 115.73 289.08 114.85 288.02 114.85 c 286.95 114.85 286.07 115.73 286.07 116.80 c B 285.68 124.57 m 285.68 125.64 286.56 126.51 287.63 126.51 c 288.70 126.51 289.57 125.64 289.57 124.57 c 289.57 123.50 288.70 122.62 287.63 122.62 c 286.56 122.62 285.68 123.50 285.68 124.57 c B 139.75 126.20 m 139.75 127.27 140.62 128.15 141.69 128.15 c 142.76 128.15 143.63 127.27 143.63 126.20 c 143.63 125.13 142.76 124.26 141.69 124.26 c 140.62 124.26 139.75 125.13 139.75 126.20 c B 284.52 123.60 m 284.52 124.66 285.40 125.54 286.47 125.54 c 287.54 125.54 288.41 124.66 288.41 123.60 c 288.41 122.53 287.54 121.65 286.47 121.65 c 285.40 121.65 284.52 122.53 284.52 123.60 c B 284.91 119.93 m 284.91 120.99 285.78 121.87 286.85 121.87 c 287.92 121.87 288.80 120.99 288.80 119.93 c 288.80 118.86 287.92 117.98 286.85 117.98 c 285.78 117.98 284.91 118.86 284.91 119.93 c B 139.49 122.53 m 139.49 123.60 140.37 124.47 141.43 124.47 c 142.50 124.47 143.38 123.60 143.38 122.53 c 143.38 121.46 142.50 120.58 141.43 120.58 c 140.37 120.58 139.49 121.46 139.49 122.53 c B 286.46 118.13 m 286.46 119.20 287.33 120.07 288.40 120.07 c 289.47 120.07 290.35 119.20 290.35 118.13 c 290.35 117.06 289.47 116.19 288.40 116.19 c 287.33 116.19 286.46 117.06 286.46 118.13 c B 139.75 124.64 m 139.75 125.71 140.62 126.58 141.69 126.58 c 142.76 126.58 143.63 125.71 143.63 124.64 c 143.63 123.57 142.76 122.70 141.69 122.70 c 140.62 122.70 139.75 123.57 139.75 124.64 c B 286.07 117.01 m 286.07 118.08 286.95 118.95 288.02 118.95 c 289.08 118.95 289.96 118.08 289.96 117.01 c 289.96 115.94 289.08 115.07 288.02 115.07 c 286.95 115.07 286.07 115.94 286.07 117.01 c B 155.36 118.95 m 155.36 120.02 156.23 120.90 157.30 120.90 c 158.37 120.90 159.24 120.02 159.24 118.95 c 159.24 117.88 158.37 117.01 157.30 117.01 c 156.23 117.01 155.36 117.88 155.36 118.95 c B 157.62 125.26 m 157.62 126.33 158.49 127.20 159.56 127.20 c 160.63 127.20 161.50 126.33 161.50 125.26 c 161.50 124.19 160.63 123.31 159.56 123.31 c 158.49 123.31 157.62 124.19 157.62 125.26 c B 286.07 119.40 m 286.07 120.47 286.95 121.35 288.02 121.35 c 289.08 121.35 289.96 120.47 289.96 119.40 c 289.96 118.33 289.08 117.46 288.02 117.46 c 286.95 117.46 286.07 118.33 286.07 119.40 c B 282.97 125.59 m 282.97 126.66 283.85 127.54 284.92 127.54 c 285.99 127.54 286.86 126.66 286.86 125.59 c 286.86 124.53 285.99 123.65 284.92 123.65 c 283.85 123.65 282.97 124.53 282.97 125.59 c B 285.68 117.83 m 285.68 118.90 286.56 119.77 287.63 119.77 c 288.70 119.77 289.57 118.90 289.57 117.83 c 289.57 116.76 288.70 115.88 287.63 115.88 c 286.56 115.88 285.68 116.76 285.68 117.83 c B 285.68 123.68 m 285.68 124.75 286.56 125.63 287.63 125.63 c 288.70 125.63 289.57 124.75 289.57 123.68 c 289.57 122.62 288.70 121.74 287.63 121.74 c 286.56 121.74 285.68 122.62 285.68 123.68 c B 285.30 116.88 m 285.30 117.95 286.17 118.82 287.24 118.82 c 288.31 118.82 289.18 117.95 289.18 116.88 c 289.18 115.81 288.31 114.94 287.24 114.94 c 286.17 114.94 285.30 115.81 285.30 116.88 c B 140.00 116.30 m 140.00 117.37 140.88 118.24 141.95 118.24 c 143.02 118.24 143.89 117.37 143.89 116.30 c 143.89 115.23 143.02 114.35 141.95 114.35 c 140.88 114.35 140.00 115.23 140.00 116.30 c B 138.98 124.13 m 138.98 125.20 139.85 126.08 140.92 126.08 c 141.99 126.08 142.87 125.20 142.87 124.13 c 142.87 123.06 141.99 122.19 140.92 122.19 c 139.85 122.19 138.98 123.06 138.98 124.13 c B 286.07 121.43 m 286.07 122.50 286.95 123.37 288.02 123.37 c 289.08 123.37 289.96 122.50 289.96 121.43 c 289.96 120.36 289.08 119.48 288.02 119.48 c 286.95 119.48 286.07 120.36 286.07 121.43 c B 140.52 115.72 m 140.52 116.79 141.39 117.67 142.46 117.67 c 143.53 117.67 144.41 116.79 144.41 115.72 c 144.41 114.65 143.53 113.78 142.46 113.78 c 141.39 113.78 140.52 114.65 140.52 115.72 c B 284.91 120.88 m 284.91 121.95 285.78 122.82 286.85 122.82 c 287.92 122.82 288.80 121.95 288.80 120.88 c 288.80 119.81 287.92 118.93 286.85 118.93 c 285.78 118.93 284.91 119.81 284.91 120.88 c B 284.52 121.34 m 284.52 122.41 285.40 123.29 286.47 123.29 c 287.54 123.29 288.41 122.41 288.41 121.34 c 288.41 120.27 287.54 119.40 286.47 119.40 c 285.40 119.40 284.52 120.27 284.52 121.34 c B 286.85 120.52 m 286.85 121.59 287.72 122.47 288.79 122.47 c 289.86 122.47 290.73 121.59 290.73 120.52 c 290.73 119.45 289.86 118.58 288.79 118.58 c 287.72 118.58 286.85 119.45 286.85 120.52 c B 284.52 117.00 m 284.52 118.07 285.40 118.94 286.47 118.94 c 287.54 118.94 288.41 118.07 288.41 117.00 c 288.41 115.93 287.54 115.05 286.47 115.05 c 285.40 115.05 284.52 115.93 284.52 117.00 c B 285.30 118.79 m 285.30 119.86 286.17 120.73 287.24 120.73 c 288.31 120.73 289.18 119.86 289.18 118.79 c 289.18 117.72 288.31 116.84 287.24 116.84 c 286.17 116.84 285.30 117.72 285.30 118.79 c B 284.52 120.96 m 284.52 122.03 285.40 122.91 286.47 122.91 c 287.54 122.91 288.41 122.03 288.41 120.96 c 288.41 119.89 287.54 119.02 286.47 119.02 c 285.40 119.02 284.52 119.89 284.52 120.96 c B 140.00 118.56 m 140.00 119.63 140.88 120.51 141.95 120.51 c 143.02 120.51 143.89 119.63 143.89 118.56 c 143.89 117.49 143.02 116.62 141.95 116.62 c 140.88 116.62 140.00 117.49 140.00 118.56 c B 284.14 117.67 m 284.14 118.74 285.01 119.62 286.08 119.62 c 287.15 119.62 288.02 118.74 288.02 117.67 c 288.02 116.60 287.15 115.73 286.08 115.73 c 285.01 115.73 284.14 116.60 284.14 117.67 c B 287.23 125.45 m 287.23 126.52 288.11 127.40 289.18 127.40 c 290.25 127.40 291.12 126.52 291.12 125.45 c 291.12 124.38 290.25 123.51 289.18 123.51 c 288.11 123.51 287.23 124.38 287.23 125.45 c B 285.30 126.25 m 285.30 127.32 286.17 128.19 287.24 128.19 c 288.31 128.19 289.18 127.32 289.18 126.25 c 289.18 125.18 288.31 124.31 287.24 124.31 c 286.17 124.31 285.30 125.18 285.30 126.25 c B 286.07 124.11 m 286.07 125.18 286.95 126.06 288.02 126.06 c 289.08 126.06 289.96 125.18 289.96 124.11 c 289.96 123.05 289.08 122.17 288.02 122.17 c 286.95 122.17 286.07 123.05 286.07 124.11 c B 139.75 126.35 m 139.75 127.42 140.62 128.30 141.69 128.30 c 142.76 128.30 143.63 127.42 143.63 126.35 c 143.63 125.28 142.76 124.41 141.69 124.41 c 140.62 124.41 139.75 125.28 139.75 126.35 c B 285.30 117.29 m 285.30 118.36 286.17 119.23 287.24 119.23 c 288.31 119.23 289.18 118.36 289.18 117.29 c 289.18 116.22 288.31 115.35 287.24 115.35 c 286.17 115.35 285.30 116.22 285.30 117.29 c B 287.62 121.62 m 287.62 122.68 288.50 123.56 289.57 123.56 c 290.64 123.56 291.51 122.68 291.51 121.62 c 291.51 120.55 290.64 119.67 289.57 119.67 c 288.50 119.67 287.62 120.55 287.62 121.62 c B 285.30 116.62 m 285.30 117.69 286.17 118.57 287.24 118.57 c 288.31 118.57 289.18 117.69 289.18 116.62 c 289.18 115.55 288.31 114.68 287.24 114.68 c 286.17 114.68 285.30 115.55 285.30 116.62 c B 286.07 119.61 m 286.07 120.68 286.95 121.55 288.02 121.55 c 289.08 121.55 289.96 120.68 289.96 119.61 c 289.96 118.54 289.08 117.66 288.02 117.66 c 286.95 117.66 286.07 118.54 286.07 119.61 c B 139.49 120.81 m 139.49 121.88 140.37 122.75 141.43 122.75 c 142.50 122.75 143.38 121.88 143.38 120.81 c 143.38 119.74 142.50 118.87 141.43 118.87 c 140.37 118.87 139.49 119.74 139.49 120.81 c B 283.75 116.04 m 283.75 117.11 284.62 117.99 285.69 117.99 c 286.76 117.99 287.64 117.11 287.64 116.04 c 287.64 114.97 286.76 114.10 285.69 114.10 c 284.62 114.10 283.75 114.97 283.75 116.04 c B 286.07 123.29 m 286.07 124.36 286.95 125.24 288.02 125.24 c 289.08 125.24 289.96 124.36 289.96 123.29 c 289.96 122.23 289.08 121.35 288.02 121.35 c 286.95 121.35 286.07 122.23 286.07 123.29 c B 156.48 118.24 m 156.48 119.31 157.36 120.19 158.43 120.19 c 159.50 120.19 160.37 119.31 160.37 118.24 c 160.37 117.18 159.50 116.30 158.43 116.30 c 157.36 116.30 156.48 117.18 156.48 118.24 c B 282.97 120.36 m 282.97 121.43 283.85 122.30 284.92 122.30 c 285.99 122.30 286.86 121.43 286.86 120.36 c 286.86 119.29 285.99 118.41 284.92 118.41 c 283.85 118.41 282.97 119.29 282.97 120.36 c B 138.98 124.79 m 138.98 125.86 139.85 126.73 140.92 126.73 c 141.99 126.73 142.87 125.86 142.87 124.79 c 142.87 123.72 141.99 122.84 140.92 122.84 c 139.85 122.84 138.98 123.72 138.98 124.79 c B 284.91 124.75 m 284.91 125.82 285.78 126.70 286.85 126.70 c 287.92 126.70 288.80 125.82 288.80 124.75 c 288.80 123.68 287.92 122.81 286.85 122.81 c 285.78 122.81 284.91 123.68 284.91 124.75 c B 285.30 124.30 m 285.30 125.37 286.17 126.25 287.24 126.25 c 288.31 126.25 289.18 125.37 289.18 124.30 c 289.18 123.23 288.31 122.36 287.24 122.36 c 286.17 122.36 285.30 123.23 285.30 124.30 c B 286.85 118.03 m 286.85 119.09 287.72 119.97 288.79 119.97 c 289.86 119.97 290.73 119.09 290.73 118.03 c 290.73 116.96 289.86 116.08 288.79 116.08 c 287.72 116.08 286.85 116.96 286.85 118.03 c B 285.68 125.18 m 285.68 126.24 286.56 127.12 287.63 127.12 c 288.70 127.12 289.57 126.24 289.57 125.18 c 289.57 124.11 288.70 123.23 287.63 123.23 c 286.56 123.23 285.68 124.11 285.68 125.18 c B 288.40 122.30 m 288.40 123.37 289.27 124.25 290.34 124.25 c 291.41 124.25 292.29 123.37 292.29 122.30 c 292.29 121.23 291.41 120.36 290.34 120.36 c 289.27 120.36 288.40 121.23 288.40 122.30 c B 138.22 122.96 m 138.22 124.03 139.09 124.91 140.16 124.91 c 141.23 124.91 142.10 124.03 142.10 122.96 c 142.10 121.89 141.23 121.02 140.16 121.02 c 139.09 121.02 138.22 121.89 138.22 122.96 c B 284.91 126.26 m 284.91 127.33 285.78 128.21 286.85 128.21 c 287.92 128.21 288.80 127.33 288.80 126.26 c 288.80 125.19 287.92 124.32 286.85 124.32 c 285.78 124.32 284.91 125.19 284.91 126.26 c B 339.25 211.48 m 339.25 212.55 340.13 213.42 341.20 213.42 c 342.26 213.42 343.14 212.55 343.14 211.48 c 343.14 210.41 342.26 209.53 341.20 209.53 c 340.13 209.53 339.25 210.41 339.25 211.48 c B 364.60 214.15 m 364.60 215.22 365.48 216.10 366.55 216.10 c 367.61 216.10 368.49 215.22 368.49 214.15 c 368.49 213.08 367.61 212.21 366.55 212.21 c 365.48 212.21 364.60 213.08 364.60 214.15 c B 87.17 78.86 m 87.17 79.93 88.04 80.81 89.11 80.81 c 90.18 80.81 91.06 79.93 91.06 78.86 c 91.06 77.79 90.18 76.92 89.11 76.92 c 88.04 76.92 87.17 77.79 87.17 78.86 c B 59.96 74.24 m 59.96 75.31 60.83 76.19 61.90 76.19 c 62.97 76.19 63.85 75.31 63.85 74.24 c 63.85 73.18 62.97 72.30 61.90 72.30 c 60.83 72.30 59.96 73.18 59.96 74.24 c B 54.70 75.59 m 54.70 76.66 55.57 77.53 56.64 77.53 c 57.71 77.53 58.59 76.66 58.59 75.59 c 58.59 74.52 57.71 73.65 56.64 73.65 c 55.57 73.65 54.70 74.52 54.70 75.59 c B 99.45 79.33 m 99.45 80.40 100.32 81.27 101.39 81.27 c 102.46 81.27 103.34 80.40 103.34 79.33 c 103.34 78.26 102.46 77.39 101.39 77.39 c 100.32 77.39 99.45 78.26 99.45 79.33 c B 102.25 76.06 m 102.25 77.13 103.13 78.00 104.20 78.00 c 105.27 78.00 106.14 77.13 106.14 76.06 c 106.14 74.99 105.27 74.11 104.20 74.11 c 103.13 74.11 102.25 74.99 102.25 76.06 c B 72.02 70.44 m 72.02 71.51 72.89 72.38 73.96 72.38 c 75.03 72.38 75.90 71.51 75.90 70.44 c 75.90 69.37 75.03 68.50 73.96 68.50 c 72.89 68.50 72.02 69.37 72.02 70.44 c B 45.81 76.25 m 45.81 77.32 46.68 78.20 47.75 78.20 c 48.82 78.20 49.70 77.32 49.70 76.25 c 49.70 75.19 48.82 74.31 47.75 74.31 c 46.68 74.31 45.81 75.19 45.81 76.25 c B 80.75 75.51 m 80.75 76.58 81.62 77.45 82.69 77.45 c 83.76 77.45 84.64 76.58 84.64 75.51 c 84.64 74.44 83.76 73.57 82.69 73.57 c 81.62 73.57 80.75 74.44 80.75 75.51 c B 119.01 79.33 m 119.01 80.40 119.88 81.28 120.95 81.28 c 122.02 81.28 122.90 80.40 122.90 79.33 c 122.90 78.26 122.02 77.39 120.95 77.39 c 119.88 77.39 119.01 78.26 119.01 79.33 c B 74.44 69.30 m 74.44 70.36 75.31 71.24 76.38 71.24 c 77.45 71.24 78.33 70.36 78.33 69.30 c 78.33 68.23 77.45 67.35 76.38 67.35 c 75.31 67.35 74.44 68.23 74.44 69.30 c B 140.07 77.31 m 140.07 78.38 140.95 79.25 142.02 79.25 c 143.09 79.25 143.96 78.38 143.96 77.31 c 143.96 76.24 143.09 75.36 142.02 75.36 c 140.95 75.36 140.07 76.24 140.07 77.31 c B 222.44 73.67 m 222.44 74.74 223.32 75.62 224.39 75.62 c 225.46 75.62 226.33 74.74 226.33 73.67 c 226.33 72.60 225.46 71.73 224.39 71.73 c 223.32 71.73 222.44 72.60 222.44 73.67 c B 97.21 75.56 m 97.21 76.63 98.08 77.50 99.15 77.50 c 100.22 77.50 101.10 76.63 101.10 75.56 c 101.10 74.49 100.22 73.62 99.15 73.62 c 98.08 73.62 97.21 74.49 97.21 75.56 c B 179.91 79.43 m 179.91 80.49 180.79 81.37 181.85 81.37 c 182.92 81.37 183.80 80.49 183.80 79.43 c 183.80 78.36 182.92 77.48 181.85 77.48 c 180.79 77.48 179.91 78.36 179.91 79.43 c B 108.50 74.11 m 108.50 75.18 109.37 76.05 110.44 76.05 c 111.51 76.05 112.38 75.18 112.38 74.11 c 112.38 73.04 111.51 72.16 110.44 72.16 c 109.37 72.16 108.50 73.04 108.50 74.11 c B 180.04 76.51 m 180.04 77.58 180.91 78.45 181.98 78.45 c 183.05 78.45 183.92 77.58 183.92 76.51 c 183.92 75.44 183.05 74.56 181.98 74.56 c 180.91 74.56 180.04 75.44 180.04 76.51 c B 100.94 69.46 m 100.94 70.53 101.82 71.41 102.89 71.41 c 103.96 71.41 104.83 70.53 104.83 69.46 c 104.83 68.39 103.96 67.52 102.89 67.52 c 101.82 67.52 100.94 68.39 100.94 69.46 c B 43.92 76.03 m 43.92 77.10 44.79 77.98 45.86 77.98 c 46.93 77.98 47.81 77.10 47.81 76.03 c 47.81 74.97 46.93 74.09 45.86 74.09 c 44.79 74.09 43.92 74.97 43.92 76.03 c B 65.52 69.84 m 65.52 70.91 66.39 71.78 67.46 71.78 c 68.53 71.78 69.41 70.91 69.41 69.84 c 69.41 68.77 68.53 67.90 67.46 67.90 c 66.39 67.90 65.52 68.77 65.52 69.84 c B 43.98 77.27 m 43.98 78.34 44.86 79.22 45.93 79.22 c 47.00 79.22 47.87 78.34 47.87 77.27 c 47.87 76.20 47.00 75.33 45.93 75.33 c 44.86 75.33 43.98 76.20 43.98 77.27 c B 112.17 77.05 m 112.17 78.12 113.04 78.99 114.11 78.99 c 115.18 78.99 116.06 78.12 116.06 77.05 c 116.06 75.98 115.18 75.10 114.11 75.10 c 113.04 75.10 112.17 75.98 112.17 77.05 c B 55.25 72.41 m 55.25 73.48 56.12 74.36 57.19 74.36 c 58.26 74.36 59.14 73.48 59.14 72.41 c 59.14 71.34 58.26 70.47 57.19 70.47 c 56.12 70.47 55.25 71.34 55.25 72.41 c B 169.14 75.47 m 169.14 76.54 170.01 77.42 171.08 77.42 c 172.15 77.42 173.03 76.54 173.03 75.47 c 173.03 74.40 172.15 73.53 171.08 73.53 c 170.01 73.53 169.14 74.40 169.14 75.47 c B 85.79 72.68 m 85.79 73.75 86.66 74.62 87.73 74.62 c 88.80 74.62 89.68 73.75 89.68 72.68 c 89.68 71.61 88.80 70.73 87.73 70.73 c 86.66 70.73 85.79 71.61 85.79 72.68 c B 47.76 73.58 m 47.76 74.65 48.63 75.53 49.70 75.53 c 50.77 75.53 51.65 74.65 51.65 73.58 c 51.65 72.51 50.77 71.64 49.70 71.64 c 48.63 71.64 47.76 72.51 47.76 73.58 c B 98.51 76.08 m 98.51 77.15 99.38 78.03 100.45 78.03 c 101.52 78.03 102.40 77.15 102.40 76.08 c 102.40 75.02 101.52 74.14 100.45 74.14 c 99.38 74.14 98.51 75.02 98.51 76.08 c B 97.08 70.38 m 97.08 71.45 97.95 72.33 99.02 72.33 c 100.09 72.33 100.97 71.45 100.97 70.38 c 100.97 69.31 100.09 68.44 99.02 68.44 c 97.95 68.44 97.08 69.31 97.08 70.38 c B 150.05 76.34 m 150.05 77.41 150.93 78.28 152.00 78.28 c 153.06 78.28 153.94 77.41 153.94 76.34 c 153.94 75.27 153.06 74.39 152.00 74.39 c 150.93 74.39 150.05 75.27 150.05 76.34 c B 81.32 74.53 m 81.32 75.60 82.19 76.48 83.26 76.48 c 84.33 76.48 85.20 75.60 85.20 74.53 c 85.20 73.46 84.33 72.59 83.26 72.59 c 82.19 72.59 81.32 73.46 81.32 74.53 c B 88.83 72.62 m 88.83 73.69 89.71 74.57 90.78 74.57 c 91.85 74.57 92.72 73.69 92.72 72.62 c 92.72 71.55 91.85 70.68 90.78 70.68 c 89.71 70.68 88.83 71.55 88.83 72.62 c B 48.76 70.81 m 48.76 71.87 49.63 72.75 50.70 72.75 c 51.77 72.75 52.64 71.87 52.64 70.81 c 52.64 69.74 51.77 68.86 50.70 68.86 c 49.63 68.86 48.76 69.74 48.76 70.81 c B 171.52 74.51 m 171.52 75.58 172.40 76.45 173.47 76.45 c 174.54 76.45 175.41 75.58 175.41 74.51 c 175.41 73.44 174.54 72.56 173.47 72.56 c 172.40 72.56 171.52 73.44 171.52 74.51 c B 103.76 69.91 m 103.76 70.98 104.64 71.85 105.71 71.85 c 106.77 71.85 107.65 70.98 107.65 69.91 c 107.65 68.84 106.77 67.97 105.71 67.97 c 104.64 67.97 103.76 68.84 103.76 69.91 c B 87.00 69.62 m 87.00 70.69 87.87 71.57 88.94 71.57 c 90.01 71.57 90.89 70.69 90.89 69.62 c 90.89 68.55 90.01 67.68 88.94 67.68 c 87.87 67.68 87.00 68.55 87.00 69.62 c B 93.14 78.45 m 93.14 79.52 94.02 80.40 95.09 80.40 c 96.16 80.40 97.03 79.52 97.03 78.45 c 97.03 77.38 96.16 76.51 95.09 76.51 c 94.02 76.51 93.14 77.38 93.14 78.45 c B 128.63 79.54 m 128.63 80.61 129.51 81.49 130.58 81.49 c 131.65 81.49 132.52 80.61 132.52 79.54 c 132.52 78.47 131.65 77.60 130.58 77.60 c 129.51 77.60 128.63 78.47 128.63 79.54 c B 103.55 75.88 m 103.55 76.95 104.43 77.82 105.50 77.82 c 106.57 77.82 107.44 76.95 107.44 75.88 c 107.44 74.81 106.57 73.93 105.50 73.93 c 104.43 73.93 103.55 74.81 103.55 75.88 c B 115.31 78.44 m 115.31 79.51 116.19 80.39 117.26 80.39 c 118.32 80.39 119.20 79.51 119.20 78.44 c 119.20 77.38 118.32 76.50 117.26 76.50 c 116.19 76.50 115.31 77.38 115.31 78.44 c B 79.59 69.44 m 79.59 70.51 80.46 71.39 81.53 71.39 c 82.60 71.39 83.48 70.51 83.48 69.44 c 83.48 68.37 82.60 67.50 81.53 67.50 c 80.46 67.50 79.59 68.37 79.59 69.44 c B 48.76 79.83 m 48.76 80.89 49.63 81.77 50.70 81.77 c 51.77 81.77 52.65 80.89 52.65 79.83 c 52.65 78.76 51.77 77.88 50.70 77.88 c 49.63 77.88 48.76 78.76 48.76 79.83 c B 103.19 77.08 m 103.19 78.15 104.07 79.02 105.14 79.02 c 106.20 79.02 107.08 78.15 107.08 77.08 c 107.08 76.01 106.20 75.14 105.14 75.14 c 104.07 75.14 103.19 76.01 103.19 77.08 c B 190.05 76.21 m 190.05 77.28 190.93 78.16 192.00 78.16 c 193.07 78.16 193.94 77.28 193.94 76.21 c 193.94 75.15 193.07 74.27 192.00 74.27 c 190.93 74.27 190.05 75.15 190.05 76.21 c B 70.66 77.87 m 70.66 78.94 71.54 79.82 72.61 79.82 c 73.68 79.82 74.55 78.94 74.55 77.87 c 74.55 76.81 73.68 75.93 72.61 75.93 c 71.54 75.93 70.66 76.81 70.66 77.87 c B 57.10 71.50 m 57.10 72.57 57.98 73.45 59.05 73.45 c 60.11 73.45 60.99 72.57 60.99 71.50 c 60.99 70.43 60.11 69.56 59.05 69.56 c 57.98 69.56 57.10 70.43 57.10 71.50 c B 55.66 72.73 m 55.66 73.80 56.54 74.68 57.61 74.68 c 58.67 74.68 59.55 73.80 59.55 72.73 c 59.55 71.66 58.67 70.79 57.61 70.79 c 56.54 70.79 55.66 71.66 55.66 72.73 c B 40.89 71.63 m 40.89 72.70 41.77 73.57 42.84 73.57 c 43.91 73.57 44.78 72.70 44.78 71.63 c 44.78 70.56 43.91 69.68 42.84 69.68 c 41.77 69.68 40.89 70.56 40.89 71.63 c B 46.31 70.28 m 46.31 71.35 47.19 72.23 48.26 72.23 c 49.33 72.23 50.20 71.35 50.20 70.28 c 50.20 69.21 49.33 68.34 48.26 68.34 c 47.19 68.34 46.31 69.21 46.31 70.28 c B 91.78 71.53 m 91.78 72.60 92.65 73.47 93.72 73.47 c 94.79 73.47 95.67 72.60 95.67 71.53 c 95.67 70.46 94.79 69.58 93.72 69.58 c 92.65 69.58 91.78 70.46 91.78 71.53 c B 101.63 75.60 m 101.63 76.67 102.51 77.54 103.58 77.54 c 104.65 77.54 105.52 76.67 105.52 75.60 c 105.52 74.53 104.65 73.66 103.58 73.66 c 102.51 73.66 101.63 74.53 101.63 75.60 c B 92.06 70.29 m 92.06 71.36 92.93 72.24 94.00 72.24 c 95.07 72.24 95.94 71.36 95.94 70.29 c 95.94 69.22 95.07 68.35 94.00 68.35 c 92.93 68.35 92.06 69.22 92.06 70.29 c B 44.21 77.18 m 44.21 78.25 45.08 79.12 46.15 79.12 c 47.22 79.12 48.10 78.25 48.10 77.18 c 48.10 76.11 47.22 75.24 46.15 75.24 c 45.08 75.24 44.21 76.11 44.21 77.18 c B 89.81 74.42 m 89.81 75.49 90.69 76.37 91.76 76.37 c 92.83 76.37 93.70 75.49 93.70 74.42 c 93.70 73.35 92.83 72.48 91.76 72.48 c 90.69 72.48 89.81 73.35 89.81 74.42 c B 127.61 74.29 m 127.61 75.36 128.49 76.23 129.56 76.23 c 130.63 76.23 131.50 75.36 131.50 74.29 c 131.50 73.22 130.63 72.34 129.56 72.34 c 128.49 72.34 127.61 73.22 127.61 74.29 c B 90.37 69.64 m 90.37 70.71 91.24 71.58 92.31 71.58 c 93.38 71.58 94.25 70.71 94.25 69.64 c 94.25 68.57 93.38 67.69 92.31 67.69 c 91.24 67.69 90.37 68.57 90.37 69.64 c B 126.96 71.70 m 126.96 72.77 127.84 73.65 128.91 73.65 c 129.98 73.65 130.85 72.77 130.85 71.70 c 130.85 70.64 129.98 69.76 128.91 69.76 c 127.84 69.76 126.96 70.64 126.96 71.70 c B 75.13 76.06 m 75.13 77.13 76.00 78.01 77.07 78.01 c 78.14 78.01 79.02 77.13 79.02 76.06 c 79.02 74.99 78.14 74.12 77.07 74.12 c 76.00 74.12 75.13 74.99 75.13 76.06 c B 89.82 74.57 m 89.82 75.64 90.70 76.51 91.77 76.51 c 92.83 76.51 93.71 75.64 93.71 74.57 c 93.71 73.50 92.83 72.63 91.77 72.63 c 90.70 72.63 89.82 73.50 89.82 74.57 c B 99.41 76.08 m 99.41 77.15 100.28 78.03 101.35 78.03 c 102.42 78.03 103.30 77.15 103.30 76.08 c 103.30 75.01 102.42 74.14 101.35 74.14 c 100.28 74.14 99.41 75.01 99.41 76.08 c B 167.46 79.49 m 167.46 80.56 168.34 81.44 169.41 81.44 c 170.48 81.44 171.35 80.56 171.35 79.49 c 171.35 78.42 170.48 77.55 169.41 77.55 c 168.34 77.55 167.46 78.42 167.46 79.49 c B 87.68 69.78 m 87.68 70.85 88.56 71.72 89.63 71.72 c 90.70 71.72 91.57 70.85 91.57 69.78 c 91.57 68.71 90.70 67.83 89.63 67.83 c 88.56 67.83 87.68 68.71 87.68 69.78 c B 129.91 79.04 m 129.91 80.11 130.79 80.98 131.86 80.98 c 132.93 80.98 133.80 80.11 133.80 79.04 c 133.80 77.97 132.93 77.10 131.86 77.10 c 130.79 77.10 129.91 77.97 129.91 79.04 c B 123.47 79.54 m 123.47 80.61 124.35 81.49 125.42 81.49 c 126.48 81.49 127.36 80.61 127.36 79.54 c 127.36 78.47 126.48 77.60 125.42 77.60 c 124.35 77.60 123.47 78.47 123.47 79.54 c B 90.33 72.08 m 90.33 73.15 91.20 74.03 92.27 74.03 c 93.34 74.03 94.22 73.15 94.22 72.08 c 94.22 71.02 93.34 70.14 92.27 70.14 c 91.20 70.14 90.33 71.02 90.33 72.08 c B 67.95 75.67 m 67.95 76.74 68.82 77.62 69.89 77.62 c 70.96 77.62 71.83 76.74 71.83 75.67 c 71.83 74.60 70.96 73.73 69.89 73.73 c 68.82 73.73 67.95 74.60 67.95 75.67 c B 224.25 76.57 m 224.25 77.64 225.12 78.51 226.19 78.51 c 227.26 78.51 228.14 77.64 228.14 76.57 c 228.14 75.50 227.26 74.62 226.19 74.62 c 225.12 74.62 224.25 75.50 224.25 76.57 c B 36.89 69.40 m 36.89 70.47 37.76 71.34 38.83 71.34 c 39.90 71.34 40.77 70.47 40.77 69.40 c 40.77 68.33 39.90 67.45 38.83 67.45 c 37.76 67.45 36.89 68.33 36.89 69.40 c B 83.80 75.24 m 83.80 76.31 84.67 77.18 85.74 77.18 c 86.81 77.18 87.68 76.31 87.68 75.24 c 87.68 74.17 86.81 73.30 85.74 73.30 c 84.67 73.30 83.80 74.17 83.80 75.24 c B 117.90 75.04 m 117.90 76.11 118.78 76.99 119.85 76.99 c 120.91 76.99 121.79 76.11 121.79 75.04 c 121.79 73.97 120.91 73.10 119.85 73.10 c 118.78 73.10 117.90 73.97 117.90 75.04 c B 236.25 74.11 m 236.25 75.18 237.12 76.06 238.19 76.06 c 239.26 76.06 240.13 75.18 240.13 74.11 c 240.13 73.04 239.26 72.17 238.19 72.17 c 237.12 72.17 236.25 73.04 236.25 74.11 c B 31.93 78.55 m 31.93 79.62 32.81 80.50 33.88 80.50 c 34.95 80.50 35.82 79.62 35.82 78.55 c 35.82 77.48 34.95 76.61 33.88 76.61 c 32.81 76.61 31.93 77.48 31.93 78.55 c B 337.45 71.21 m 337.45 72.28 338.32 73.15 339.39 73.15 c 340.46 73.15 341.34 72.28 341.34 71.21 c 341.34 70.14 340.46 69.26 339.39 69.26 c 338.32 69.26 337.45 70.14 337.45 71.21 c B 80.44 74.44 m 80.44 75.50 81.32 76.38 82.39 76.38 c 83.46 76.38 84.33 75.50 84.33 74.44 c 84.33 73.37 83.46 72.49 82.39 72.49 c 81.32 72.49 80.44 73.37 80.44 74.44 c B 52.91 76.32 m 52.91 77.39 53.79 78.26 54.86 78.26 c 55.93 78.26 56.80 77.39 56.80 76.32 c 56.80 75.25 55.93 74.38 54.86 74.38 c 53.79 74.38 52.91 75.25 52.91 76.32 c B 107.41 73.09 m 107.41 74.16 108.28 75.04 109.35 75.04 c 110.42 75.04 111.30 74.16 111.30 73.09 c 111.30 72.02 110.42 71.15 109.35 71.15 c 108.28 71.15 107.41 72.02 107.41 73.09 c B 188.08 70.07 m 188.08 71.14 188.96 72.02 190.02 72.02 c 191.09 72.02 191.97 71.14 191.97 70.07 c 191.97 69.00 191.09 68.13 190.02 68.13 c 188.96 68.13 188.08 69.00 188.08 70.07 c B 54.02 68.87 m 54.02 69.94 54.89 70.81 55.96 70.81 c 57.03 70.81 57.91 69.94 57.91 68.87 c 57.91 67.80 57.03 66.92 55.96 66.92 c 54.89 66.92 54.02 67.80 54.02 68.87 c B 251.11 78.27 m 251.11 79.34 251.98 80.22 253.05 80.22 c 254.12 80.22 254.99 79.34 254.99 78.27 c 254.99 77.20 254.12 76.33 253.05 76.33 c 251.98 76.33 251.11 77.20 251.11 78.27 c B 71.54 71.08 m 71.54 72.15 72.41 73.03 73.48 73.03 c 74.55 73.03 75.42 72.15 75.42 71.08 c 75.42 70.01 74.55 69.14 73.48 69.14 c 72.41 69.14 71.54 70.01 71.54 71.08 c B 95.94 69.43 m 95.94 70.50 96.81 71.37 97.88 71.37 c 98.95 71.37 99.82 70.50 99.82 69.43 c 99.82 68.36 98.95 67.49 97.88 67.49 c 96.81 67.49 95.94 68.36 95.94 69.43 c B 119.75 68.99 m 119.75 70.06 120.62 70.93 121.69 70.93 c 122.76 70.93 123.63 70.06 123.63 68.99 c 123.63 67.92 122.76 67.05 121.69 67.05 c 120.62 67.05 119.75 67.92 119.75 68.99 c B 75.70 70.53 m 75.70 71.60 76.58 72.48 77.65 72.48 c 78.72 72.48 79.59 71.60 79.59 70.53 c 79.59 69.46 78.72 68.59 77.65 68.59 c 76.58 68.59 75.70 69.46 75.70 70.53 c B 87.35 70.01 m 87.35 71.08 88.22 71.95 89.29 71.95 c 90.36 71.95 91.24 71.08 91.24 70.01 c 91.24 68.94 90.36 68.06 89.29 68.06 c 88.22 68.06 87.35 68.94 87.35 70.01 c B 96.54 73.32 m 96.54 74.39 97.41 75.26 98.48 75.26 c 99.55 75.26 100.43 74.39 100.43 73.32 c 100.43 72.25 99.55 71.37 98.48 71.37 c 97.41 71.37 96.54 72.25 96.54 73.32 c B 123.84 78.77 m 123.84 79.84 124.72 80.71 125.79 80.71 c 126.86 80.71 127.73 79.84 127.73 78.77 c 127.73 77.70 126.86 76.82 125.79 76.82 c 124.72 76.82 123.84 77.70 123.84 78.77 c B 84.77 70.99 m 84.77 72.06 85.64 72.94 86.71 72.94 c 87.78 72.94 88.66 72.06 88.66 70.99 c 88.66 69.93 87.78 69.05 86.71 69.05 c 85.64 69.05 84.77 69.93 84.77 70.99 c B 297.16 74.43 m 297.16 75.50 298.03 76.37 299.10 76.37 c 300.17 76.37 301.05 75.50 301.05 74.43 c 301.05 73.36 300.17 72.48 299.10 72.48 c 298.03 72.48 297.16 73.36 297.16 74.43 c B 41.00 74.00 m 41.00 75.07 41.87 75.94 42.94 75.94 c 44.01 75.94 44.88 75.07 44.88 74.00 c 44.88 72.93 44.01 72.05 42.94 72.05 c 41.87 72.05 41.00 72.93 41.00 74.00 c B 169.04 74.70 m 169.04 75.77 169.91 76.65 170.98 76.65 c 172.05 76.65 172.92 75.77 172.92 74.70 c 172.92 73.63 172.05 72.76 170.98 72.76 c 169.91 72.76 169.04 73.63 169.04 74.70 c B 114.16 79.32 m 114.16 80.39 115.04 81.26 116.11 81.26 c 117.18 81.26 118.05 80.39 118.05 79.32 c 118.05 78.25 117.18 77.38 116.11 77.38 c 115.04 77.38 114.16 78.25 114.16 79.32 c B 98.32 73.29 m 98.32 74.36 99.19 75.23 100.26 75.23 c 101.33 75.23 102.21 74.36 102.21 73.29 c 102.21 72.22 101.33 71.34 100.26 71.34 c 99.19 71.34 98.32 72.22 98.32 73.29 c B 106.11 75.31 m 106.11 76.38 106.99 77.25 108.06 77.25 c 109.13 77.25 110.00 76.38 110.00 75.31 c 110.00 74.24 109.13 73.37 108.06 73.37 c 106.99 73.37 106.11 74.24 106.11 75.31 c B 51.80 77.02 m 51.80 78.09 52.67 78.96 53.74 78.96 c 54.81 78.96 55.69 78.09 55.69 77.02 c 55.69 75.95 54.81 75.07 53.74 75.07 c 52.67 75.07 51.80 75.95 51.80 77.02 c B 64.81 75.48 m 64.81 76.54 65.68 77.42 66.75 77.42 c 67.82 77.42 68.69 76.54 68.69 75.48 c 68.69 74.41 67.82 73.53 66.75 73.53 c 65.68 73.53 64.81 74.41 64.81 75.48 c B 64.52 78.83 m 64.52 79.90 65.40 80.78 66.47 80.78 c 67.54 80.78 68.41 79.90 68.41 78.83 c 68.41 77.76 67.54 76.89 66.47 76.89 c 65.40 76.89 64.52 77.76 64.52 78.83 c B 122.39 70.15 m 122.39 71.22 123.27 72.09 124.34 72.09 c 125.41 72.09 126.28 71.22 126.28 70.15 c 126.28 69.08 125.41 68.20 124.34 68.20 c 123.27 68.20 122.39 69.08 122.39 70.15 c B 64.52 78.27 m 64.52 79.34 65.40 80.21 66.46 80.21 c 67.53 80.21 68.41 79.34 68.41 78.27 c 68.41 77.20 67.53 76.32 66.46 76.32 c 65.40 76.32 64.52 77.20 64.52 78.27 c B 79.35 77.65 m 79.35 78.72 80.23 79.59 81.30 79.59 c 82.37 79.59 83.24 78.72 83.24 77.65 c 83.24 76.58 82.37 75.70 81.30 75.70 c 80.23 75.70 79.35 76.58 79.35 77.65 c B 44.53 73.95 m 44.53 75.02 45.40 75.90 46.47 75.90 c 47.54 75.90 48.41 75.02 48.41 73.95 c 48.41 72.88 47.54 72.01 46.47 72.01 c 45.40 72.01 44.53 72.88 44.53 73.95 c B 253.06 75.28 m 253.06 76.35 253.93 77.23 255.00 77.23 c 256.07 77.23 256.94 76.35 256.94 75.28 c 256.94 74.22 256.07 73.34 255.00 73.34 c 253.93 73.34 253.06 74.22 253.06 75.28 c B 37.55 70.98 m 37.55 72.05 38.42 72.93 39.49 72.93 c 40.56 72.93 41.44 72.05 41.44 70.98 c 41.44 69.91 40.56 69.04 39.49 69.04 c 38.42 69.04 37.55 69.91 37.55 70.98 c B 124.53 79.89 m 124.53 80.96 125.40 81.83 126.47 81.83 c 127.54 81.83 128.42 80.96 128.42 79.89 c 128.42 78.82 127.54 77.95 126.47 77.95 c 125.40 77.95 124.53 78.82 124.53 79.89 c B 95.29 77.45 m 95.29 78.52 96.16 79.39 97.23 79.39 c 98.30 79.39 99.18 78.52 99.18 77.45 c 99.18 76.38 98.30 75.50 97.23 75.50 c 96.16 75.50 95.29 76.38 95.29 77.45 c B 96.18 72.90 m 96.18 73.97 97.06 74.84 98.13 74.84 c 99.20 74.84 100.07 73.97 100.07 72.90 c 100.07 71.83 99.20 70.96 98.13 70.96 c 97.06 70.96 96.18 71.83 96.18 72.90 c B 47.42 74.90 m 47.42 75.97 48.29 76.84 49.36 76.84 c 50.43 76.84 51.31 75.97 51.31 74.90 c 51.31 73.83 50.43 72.96 49.36 72.96 c 48.29 72.96 47.42 73.83 47.42 74.90 c B 58.34 78.25 m 58.34 79.32 59.21 80.20 60.28 80.20 c 61.35 80.20 62.23 79.32 62.23 78.25 c 62.23 77.18 61.35 76.31 60.28 76.31 c 59.21 76.31 58.34 77.18 58.34 78.25 c B 45.08 75.64 m 45.08 76.71 45.95 77.58 47.02 77.58 c 48.09 77.58 48.97 76.71 48.97 75.64 c 48.97 74.57 48.09 73.69 47.02 73.69 c 45.95 73.69 45.08 74.57 45.08 75.64 c B 130.60 69.56 m 130.60 70.63 131.48 71.50 132.54 71.50 c 133.61 71.50 134.49 70.63 134.49 69.56 c 134.49 68.49 133.61 67.61 132.54 67.61 c 131.48 67.61 130.60 68.49 130.60 69.56 c B 149.73 75.11 m 149.73 76.18 150.60 77.06 151.67 77.06 c 152.74 77.06 153.62 76.18 153.62 75.11 c 153.62 74.05 152.74 73.17 151.67 73.17 c 150.60 73.17 149.73 74.05 149.73 75.11 c B 160.07 73.78 m 160.07 74.85 160.95 75.72 162.02 75.72 c 163.09 75.72 163.96 74.85 163.96 73.78 c 163.96 72.71 163.09 71.84 162.02 71.84 c 160.95 71.84 160.07 72.71 160.07 73.78 c B 258.59 76.72 m 258.59 77.78 259.47 78.66 260.54 78.66 c 261.60 78.66 262.48 77.78 262.48 76.72 c 262.48 75.65 261.60 74.77 260.54 74.77 c 259.47 74.77 258.59 75.65 258.59 76.72 c B 69.35 76.37 m 69.35 77.44 70.22 78.32 71.29 78.32 c 72.36 78.32 73.23 77.44 73.23 76.37 c 73.23 75.31 72.36 74.43 71.29 74.43 c 70.22 74.43 69.35 75.31 69.35 76.37 c B 114.48 75.89 m 114.48 76.95 115.36 77.83 116.43 77.83 c 117.50 77.83 118.37 76.95 118.37 75.89 c 118.37 74.82 117.50 73.94 116.43 73.94 c 115.36 73.94 114.48 74.82 114.48 75.89 c B 144.54 69.38 m 144.54 70.45 145.42 71.33 146.49 71.33 c 147.56 71.33 148.43 70.45 148.43 69.38 c 148.43 68.32 147.56 67.44 146.49 67.44 c 145.42 67.44 144.54 68.32 144.54 69.38 c B 300.97 73.90 m 300.97 74.97 301.84 75.85 302.91 75.85 c 303.98 75.85 304.85 74.97 304.85 73.90 c 304.85 72.84 303.98 71.96 302.91 71.96 c 301.84 71.96 300.97 72.84 300.97 73.90 c B 50.04 70.13 m 50.04 71.20 50.91 72.08 51.98 72.08 c 53.05 72.08 53.92 71.20 53.92 70.13 c 53.92 69.06 53.05 68.19 51.98 68.19 c 50.91 68.19 50.04 69.06 50.04 70.13 c B 49.98 73.53 m 49.98 74.60 50.85 75.47 51.92 75.47 c 52.99 75.47 53.86 74.60 53.86 73.53 c 53.86 72.46 52.99 71.59 51.92 71.59 c 50.85 71.59 49.98 72.46 49.98 73.53 c B 114.74 73.53 m 114.74 74.60 115.62 75.48 116.69 75.48 c 117.76 75.48 118.63 74.60 118.63 73.53 c 118.63 72.46 117.76 71.59 116.69 71.59 c 115.62 71.59 114.74 72.46 114.74 73.53 c B 87.08 71.34 m 87.08 72.41 87.96 73.29 89.03 73.29 c 90.10 73.29 90.97 72.41 90.97 71.34 c 90.97 70.27 90.10 69.40 89.03 69.40 c 87.96 69.40 87.08 70.27 87.08 71.34 c B 74.94 74.41 m 74.94 75.48 75.82 76.35 76.89 76.35 c 77.96 76.35 78.83 75.48 78.83 74.41 c 78.83 73.34 77.96 72.46 76.89 72.46 c 75.82 72.46 74.94 73.34 74.94 74.41 c B 179.93 73.02 m 179.93 74.09 180.81 74.96 181.87 74.96 c 182.94 74.96 183.82 74.09 183.82 73.02 c 183.82 71.95 182.94 71.08 181.87 71.08 c 180.81 71.08 179.93 71.95 179.93 73.02 c B 134.14 70.52 m 134.14 71.59 135.01 72.46 136.08 72.46 c 137.15 72.46 138.03 71.59 138.03 70.52 c 138.03 69.45 137.15 68.58 136.08 68.58 c 135.01 68.58 134.14 69.45 134.14 70.52 c B 221.74 68.77 m 221.74 69.84 222.61 70.72 223.68 70.72 c 224.75 70.72 225.63 69.84 225.63 68.77 c 225.63 67.70 224.75 66.83 223.68 66.83 c 222.61 66.83 221.74 67.70 221.74 68.77 c B 71.94 77.57 m 71.94 78.64 72.81 79.51 73.88 79.51 c 74.95 79.51 75.82 78.64 75.82 77.57 c 75.82 76.50 74.95 75.63 73.88 75.63 c 72.81 75.63 71.94 76.50 71.94 77.57 c B 71.09 69.39 m 71.09 70.46 71.97 71.34 73.04 71.34 c 74.11 71.34 74.98 70.46 74.98 69.39 c 74.98 68.32 74.11 67.45 73.04 67.45 c 71.97 67.45 71.09 68.32 71.09 69.39 c B 123.91 76.36 m 123.91 77.43 124.79 78.31 125.86 78.31 c 126.93 78.31 127.80 77.43 127.80 76.36 c 127.80 75.30 126.93 74.42 125.86 74.42 c 124.79 74.42 123.91 75.30 123.91 76.36 c B 55.15 75.29 m 55.15 76.36 56.03 77.24 57.10 77.24 c 58.16 77.24 59.04 76.36 59.04 75.29 c 59.04 74.22 58.16 73.35 57.10 73.35 c 56.03 73.35 55.15 74.22 55.15 75.29 c B 94.28 77.79 m 94.28 78.86 95.15 79.73 96.22 79.73 c 97.29 79.73 98.16 78.86 98.16 77.79 c 98.16 76.72 97.29 75.84 96.22 75.84 c 95.15 75.84 94.28 76.72 94.28 77.79 c B 147.00 69.31 m 147.00 70.38 147.88 71.25 148.95 71.25 c 150.02 71.25 150.89 70.38 150.89 69.31 c 150.89 68.24 150.02 67.37 148.95 67.37 c 147.88 67.37 147.00 68.24 147.00 69.31 c B 125.28 69.51 m 125.28 70.58 126.15 71.46 127.22 71.46 c 128.29 71.46 129.16 70.58 129.16 69.51 c 129.16 68.44 128.29 67.57 127.22 67.57 c 126.15 67.57 125.28 68.44 125.28 69.51 c B 71.61 78.55 m 71.61 79.62 72.49 80.49 73.56 80.49 c 74.63 80.49 75.50 79.62 75.50 78.55 c 75.50 77.48 74.63 76.61 73.56 76.61 c 72.49 76.61 71.61 77.48 71.61 78.55 c B 39.35 77.88 m 39.35 78.95 40.22 79.83 41.29 79.83 c 42.36 79.83 43.24 78.95 43.24 77.88 c 43.24 76.81 42.36 75.94 41.29 75.94 c 40.22 75.94 39.35 76.81 39.35 77.88 c B 197.24 78.61 m 197.24 79.67 198.11 80.55 199.18 80.55 c 200.25 80.55 201.12 79.67 201.12 78.61 c 201.12 77.54 200.25 76.66 199.18 76.66 c 198.11 76.66 197.24 77.54 197.24 78.61 c B 126.80 72.61 m 126.80 73.68 127.67 74.56 128.74 74.56 c 129.81 74.56 130.68 73.68 130.68 72.61 c 130.68 71.54 129.81 70.67 128.74 70.67 c 127.67 70.67 126.80 71.54 126.80 72.61 c B 128.90 78.56 m 128.90 79.63 129.77 80.50 130.84 80.50 c 131.91 80.50 132.78 79.63 132.78 78.56 c 132.78 77.49 131.91 76.62 130.84 76.62 c 129.77 76.62 128.90 77.49 128.90 78.56 c B 170.02 75.57 m 170.02 76.64 170.89 77.52 171.96 77.52 c 173.03 77.52 173.91 76.64 173.91 75.57 c 173.91 74.50 173.03 73.63 171.96 73.63 c 170.89 73.63 170.02 74.50 170.02 75.57 c B 90.14 72.31 m 90.14 73.38 91.01 74.26 92.08 74.26 c 93.15 74.26 94.03 73.38 94.03 72.31 c 94.03 71.24 93.15 70.37 92.08 70.37 c 91.01 70.37 90.14 71.24 90.14 72.31 c B 300.60 71.16 m 300.60 72.23 301.48 73.10 302.55 73.10 c 303.62 73.10 304.49 72.23 304.49 71.16 c 304.49 70.09 303.62 69.22 302.55 69.22 c 301.48 69.22 300.60 70.09 300.60 71.16 c B 300.47 77.29 m 300.47 78.36 301.35 79.23 302.42 79.23 c 303.49 79.23 304.36 78.36 304.36 77.29 c 304.36 76.22 303.49 75.34 302.42 75.34 c 301.35 75.34 300.47 76.22 300.47 77.29 c B 99.23 78.67 m 99.23 79.74 100.10 80.61 101.17 80.61 c 102.24 80.61 103.12 79.74 103.12 78.67 c 103.12 77.60 102.24 76.72 101.17 76.72 c 100.10 76.72 99.23 77.60 99.23 78.67 c B 168.15 79.16 m 168.15 80.23 169.03 81.10 170.10 81.10 c 171.17 81.10 172.04 80.23 172.04 79.16 c 172.04 78.09 171.17 77.21 170.10 77.21 c 169.03 77.21 168.15 78.09 168.15 79.16 c B 300.81 75.27 m 300.81 76.34 301.69 77.21 302.76 77.21 c 303.83 77.21 304.70 76.34 304.70 75.27 c 304.70 74.20 303.83 73.32 302.76 73.32 c 301.69 73.32 300.81 74.20 300.81 75.27 c B 63.07 76.83 m 63.07 77.90 63.94 78.77 65.01 78.77 c 66.08 78.77 66.96 77.90 66.96 76.83 c 66.96 75.76 66.08 74.88 65.01 74.88 c 63.94 74.88 63.07 75.76 63.07 76.83 c B 89.51 69.22 m 89.51 70.28 90.38 71.16 91.45 71.16 c 92.52 71.16 93.40 70.28 93.40 69.22 c 93.40 68.15 92.52 67.27 91.45 67.27 c 90.38 67.27 89.51 68.15 89.51 69.22 c B 147.40 76.79 m 147.40 77.86 148.28 78.73 149.35 78.73 c 150.41 78.73 151.29 77.86 151.29 76.79 c 151.29 75.72 150.41 74.84 149.35 74.84 c 148.28 74.84 147.40 75.72 147.40 76.79 c B 299.60 70.05 m 299.60 71.12 300.48 71.99 301.55 71.99 c 302.62 71.99 303.49 71.12 303.49 70.05 c 303.49 68.98 302.62 68.10 301.55 68.10 c 300.48 68.10 299.60 68.98 299.60 70.05 c B 134.11 70.39 m 134.11 71.46 134.98 72.33 136.05 72.33 c 137.12 72.33 138.00 71.46 138.00 70.39 c 138.00 69.32 137.12 68.44 136.05 68.44 c 134.98 68.44 134.11 69.32 134.11 70.39 c B 130.29 72.50 m 130.29 73.56 131.16 74.44 132.23 74.44 c 133.30 74.44 134.17 73.56 134.17 72.50 c 134.17 71.43 133.30 70.55 132.23 70.55 c 131.16 70.55 130.29 71.43 130.29 72.50 c B 45.87 69.89 m 45.87 70.96 46.75 71.84 47.82 71.84 c 48.89 71.84 49.76 70.96 49.76 69.89 c 49.76 68.82 48.89 67.95 47.82 67.95 c 46.75 67.95 45.87 68.82 45.87 69.89 c B 120.63 71.85 m 120.63 72.92 121.50 73.79 122.57 73.79 c 123.64 73.79 124.51 72.92 124.51 71.85 c 124.51 70.78 123.64 69.91 122.57 69.91 c 121.50 69.91 120.63 70.78 120.63 71.85 c B 52.31 70.11 m 52.31 71.17 53.18 72.05 54.25 72.05 c 55.32 72.05 56.20 71.17 56.20 70.11 c 56.20 69.04 55.32 68.16 54.25 68.16 c 53.18 68.16 52.31 69.04 52.31 70.11 c B 75.71 79.59 m 75.71 80.66 76.59 81.53 77.66 81.53 c 78.73 81.53 79.60 80.66 79.60 79.59 c 79.60 78.52 78.73 77.65 77.66 77.65 c 76.59 77.65 75.71 78.52 75.71 79.59 c B 297.00 72.48 m 297.00 73.55 297.88 74.42 298.95 74.42 c 300.02 74.42 300.89 73.55 300.89 72.48 c 300.89 71.41 300.02 70.54 298.95 70.54 c 297.88 70.54 297.00 71.41 297.00 72.48 c B 42.02 69.64 m 42.02 70.71 42.89 71.59 43.96 71.59 c 45.03 71.59 45.90 70.71 45.90 69.64 c 45.90 68.58 45.03 67.70 43.96 67.70 c 42.89 67.70 42.02 68.58 42.02 69.64 c B 297.75 71.06 m 297.75 72.13 298.62 73.00 299.69 73.00 c 300.76 73.00 301.64 72.13 301.64 71.06 c 301.64 69.99 300.76 69.11 299.69 69.11 c 298.62 69.11 297.75 69.99 297.75 71.06 c B 146.39 73.59 m 146.39 74.66 147.27 75.53 148.33 75.53 c 149.40 75.53 150.28 74.66 150.28 73.59 c 150.28 72.52 149.40 71.64 148.33 71.64 c 147.27 71.64 146.39 72.52 146.39 73.59 c B 295.85 73.24 m 295.85 74.31 296.72 75.18 297.79 75.18 c 298.86 75.18 299.74 74.31 299.74 73.24 c 299.74 72.17 298.86 71.29 297.79 71.29 c 296.72 71.29 295.85 72.17 295.85 73.24 c B 124.06 71.21 m 124.06 72.28 124.94 73.15 126.01 73.15 c 127.08 73.15 127.95 72.28 127.95 71.21 c 127.95 70.14 127.08 69.26 126.01 69.26 c 124.94 69.26 124.06 70.14 124.06 71.21 c B 70.03 78.81 m 70.03 79.88 70.91 80.76 71.98 80.76 c 73.05 80.76 73.92 79.88 73.92 78.81 c 73.92 77.74 73.05 76.87 71.98 76.87 c 70.91 76.87 70.03 77.74 70.03 78.81 c B 103.79 76.90 m 103.79 77.97 104.67 78.84 105.74 78.84 c 106.81 78.84 107.68 77.97 107.68 76.90 c 107.68 75.83 106.81 74.96 105.74 74.96 c 104.67 74.96 103.79 75.83 103.79 76.90 c B 50.97 78.01 m 50.97 79.07 51.85 79.95 52.91 79.95 c 53.98 79.95 54.86 79.07 54.86 78.01 c 54.86 76.94 53.98 76.06 52.91 76.06 c 51.85 76.06 50.97 76.94 50.97 78.01 c B 68.21 72.50 m 68.21 73.57 69.09 74.44 70.16 74.44 c 71.23 74.44 72.10 73.57 72.10 72.50 c 72.10 71.43 71.23 70.56 70.16 70.56 c 69.09 70.56 68.21 71.43 68.21 72.50 c B 163.94 71.10 m 163.94 72.17 164.82 73.04 165.89 73.04 c 166.96 73.04 167.83 72.17 167.83 71.10 c 167.83 70.03 166.96 69.15 165.89 69.15 c 164.82 69.15 163.94 70.03 163.94 71.10 c B 260.56 76.29 m 260.56 77.36 261.43 78.23 262.50 78.23 c 263.57 78.23 264.44 77.36 264.44 76.29 c 264.44 75.22 263.57 74.34 262.50 74.34 c 261.43 74.34 260.56 75.22 260.56 76.29 c B 295.96 73.03 m 295.96 74.09 296.84 74.97 297.90 74.97 c 298.97 74.97 299.85 74.09 299.85 73.03 c 299.85 71.96 298.97 71.08 297.90 71.08 c 296.84 71.08 295.96 71.96 295.96 73.03 c B 65.59 76.58 m 65.59 77.64 66.47 78.52 67.54 78.52 c 68.61 78.52 69.48 77.64 69.48 76.58 c 69.48 75.51 68.61 74.63 67.54 74.63 c 66.47 74.63 65.59 75.51 65.59 76.58 c B 142.10 72.49 m 142.10 73.56 142.98 74.44 144.05 74.44 c 145.12 74.44 145.99 73.56 145.99 72.49 c 145.99 71.43 145.12 70.55 144.05 70.55 c 142.98 70.55 142.10 71.43 142.10 72.49 c B 109.10 73.69 m 109.10 74.76 109.98 75.64 111.05 75.64 c 112.11 75.64 112.99 74.76 112.99 73.69 c 112.99 72.62 112.11 71.75 111.05 71.75 c 109.98 71.75 109.10 72.62 109.10 73.69 c B 99.26 78.51 m 99.26 79.58 100.14 80.45 101.21 80.45 c 102.28 80.45 103.15 79.58 103.15 78.51 c 103.15 77.44 102.28 76.56 101.21 76.56 c 100.14 76.56 99.26 77.44 99.26 78.51 c B 162.08 77.41 m 162.08 78.48 162.95 79.36 164.02 79.36 c 165.09 79.36 165.97 78.48 165.97 77.41 c 165.97 76.35 165.09 75.47 164.02 75.47 c 162.95 75.47 162.08 76.35 162.08 77.41 c B 162.37 73.06 m 162.37 74.12 163.24 75.00 164.31 75.00 c 165.38 75.00 166.26 74.12 166.26 73.06 c 166.26 71.99 165.38 71.11 164.31 71.11 c 163.24 71.11 162.37 71.99 162.37 73.06 c B 166.81 69.78 m 166.81 70.84 167.69 71.72 168.75 71.72 c 169.82 71.72 170.70 70.84 170.70 69.78 c 170.70 68.71 169.82 67.83 168.75 67.83 c 167.69 67.83 166.81 68.71 166.81 69.78 c B 118.97 75.04 m 118.97 76.11 119.85 76.98 120.92 76.98 c 121.99 76.98 122.86 76.11 122.86 75.04 c 122.86 73.97 121.99 73.10 120.92 73.10 c 119.85 73.10 118.97 73.97 118.97 75.04 c B 44.01 69.63 m 44.01 70.70 44.88 71.57 45.95 71.57 c 47.02 71.57 47.89 70.70 47.89 69.63 c 47.89 68.56 47.02 67.69 45.95 67.69 c 44.88 67.69 44.01 68.56 44.01 69.63 c B 141.32 71.09 m 141.32 72.16 142.19 73.03 143.26 73.03 c 144.33 73.03 145.20 72.16 145.20 71.09 c 145.20 70.02 144.33 69.15 143.26 69.15 c 142.19 69.15 141.32 70.02 141.32 71.09 c B 63.42 71.72 m 63.42 72.79 64.30 73.66 65.37 73.66 c 66.44 73.66 67.31 72.79 67.31 71.72 c 67.31 70.65 66.44 69.77 65.37 69.77 c 64.30 69.77 63.42 70.65 63.42 71.72 c B 64.89 76.48 m 64.89 77.55 65.77 78.43 66.84 78.43 c 67.91 78.43 68.78 77.55 68.78 76.48 c 68.78 75.41 67.91 74.54 66.84 74.54 c 65.77 74.54 64.89 75.41 64.89 76.48 c B 45.13 77.05 m 45.13 78.12 46.01 78.99 47.08 78.99 c 48.14 78.99 49.02 78.12 49.02 77.05 c 49.02 75.98 48.14 75.10 47.08 75.10 c 46.01 75.10 45.13 75.98 45.13 77.05 c B 106.78 73.23 m 106.78 74.30 107.65 75.18 108.72 75.18 c 109.79 75.18 110.67 74.30 110.67 73.23 c 110.67 72.16 109.79 71.29 108.72 71.29 c 107.65 71.29 106.78 72.16 106.78 73.23 c B 135.26 74.61 m 135.26 75.68 136.14 76.55 137.21 76.55 c 138.28 76.55 139.15 75.68 139.15 74.61 c 139.15 73.54 138.28 72.66 137.21 72.66 c 136.14 72.66 135.26 73.54 135.26 74.61 c B 93.69 77.35 m 93.69 78.42 94.56 79.30 95.63 79.30 c 96.70 79.30 97.58 78.42 97.58 77.35 c 97.58 76.28 96.70 75.41 95.63 75.41 c 94.56 75.41 93.69 76.28 93.69 77.35 c B 154.66 79.53 m 154.66 80.60 155.53 81.47 156.60 81.47 c 157.67 81.47 158.55 80.60 158.55 79.53 c 158.55 78.46 157.67 77.58 156.60 77.58 c 155.53 77.58 154.66 78.46 154.66 79.53 c B 106.58 75.43 m 106.58 76.50 107.46 77.38 108.53 77.38 c 109.60 77.38 110.47 76.50 110.47 75.43 c 110.47 74.36 109.60 73.49 108.53 73.49 c 107.46 73.49 106.58 74.36 106.58 75.43 c B 72.48 69.60 m 72.48 70.66 73.35 71.54 74.42 71.54 c 75.49 71.54 76.36 70.66 76.36 69.60 c 76.36 68.53 75.49 67.65 74.42 67.65 c 73.35 67.65 72.48 68.53 72.48 69.60 c B 140.92 76.28 m 140.92 77.35 141.79 78.22 142.86 78.22 c 143.93 78.22 144.80 77.35 144.80 76.28 c 144.80 75.21 143.93 74.33 142.86 74.33 c 141.79 74.33 140.92 75.21 140.92 76.28 c B 126.18 72.57 m 126.18 73.64 127.05 74.51 128.12 74.51 c 129.19 74.51 130.07 73.64 130.07 72.57 c 130.07 71.50 129.19 70.62 128.12 70.62 c 127.05 70.62 126.18 71.50 126.18 72.57 c B 55.51 74.77 m 55.51 75.84 56.39 76.71 57.45 76.71 c 58.52 76.71 59.40 75.84 59.40 74.77 c 59.40 73.70 58.52 72.83 57.45 72.83 c 56.39 72.83 55.51 73.70 55.51 74.77 c B 55.00 78.88 m 55.00 79.95 55.88 80.83 56.95 80.83 c 58.02 80.83 58.89 79.95 58.89 78.88 c 58.89 77.81 58.02 76.94 56.95 76.94 c 55.88 76.94 55.00 77.81 55.00 78.88 c B 90.23 70.67 m 90.23 71.74 91.10 72.62 92.17 72.62 c 93.24 72.62 94.11 71.74 94.11 70.67 c 94.11 69.60 93.24 68.73 92.17 68.73 c 91.10 68.73 90.23 69.60 90.23 70.67 c B 141.81 69.26 m 141.81 70.33 142.69 71.21 143.76 71.21 c 144.83 71.21 145.70 70.33 145.70 69.26 c 145.70 68.19 144.83 67.32 143.76 67.32 c 142.69 67.32 141.81 68.19 141.81 69.26 c B 259.19 78.72 m 259.19 79.79 260.07 80.66 261.14 80.66 c 262.20 80.66 263.08 79.79 263.08 78.72 c 263.08 77.65 262.20 76.77 261.14 76.77 c 260.07 76.77 259.19 77.65 259.19 78.72 c B 138.28 73.85 m 138.28 74.92 139.15 75.80 140.22 75.80 c 141.29 75.80 142.17 74.92 142.17 73.85 c 142.17 72.79 141.29 71.91 140.22 71.91 c 139.15 71.91 138.28 72.79 138.28 73.85 c B 140.29 76.70 m 140.29 77.77 141.17 78.64 142.24 78.64 c 143.31 78.64 144.18 77.77 144.18 76.70 c 144.18 75.63 143.31 74.75 142.24 74.75 c 141.17 74.75 140.29 75.63 140.29 76.70 c B 103.41 77.83 m 103.41 78.90 104.29 79.77 105.36 79.77 c 106.43 79.77 107.30 78.90 107.30 77.83 c 107.30 76.76 106.43 75.88 105.36 75.88 c 104.29 75.88 103.41 76.76 103.41 77.83 c B 151.25 77.90 m 151.25 78.97 152.13 79.85 153.20 79.85 c 154.26 79.85 155.14 78.97 155.14 77.90 c 155.14 76.83 154.26 75.96 153.20 75.96 c 152.13 75.96 151.25 76.83 151.25 77.90 c B 34.80 74.59 m 34.80 75.66 35.67 76.53 36.74 76.53 c 37.81 76.53 38.68 75.66 38.68 74.59 c 38.68 73.52 37.81 72.64 36.74 72.64 c 35.67 72.64 34.80 73.52 34.80 74.59 c B 34.85 75.25 m 34.85 76.32 35.73 77.19 36.79 77.19 c 37.86 77.19 38.74 76.32 38.74 75.25 c 38.74 74.18 37.86 73.30 36.79 73.30 c 35.73 73.30 34.85 74.18 34.85 75.25 c B 30.06 79.57 m 30.06 80.64 30.93 81.51 32.00 81.51 c 33.07 81.51 33.94 80.64 33.94 79.57 c 33.94 78.50 33.07 77.62 32.00 77.62 c 30.93 77.62 30.06 78.50 30.06 79.57 c B 48.74 70.52 m 48.74 71.59 49.62 72.46 50.69 72.46 c 51.76 72.46 52.63 71.59 52.63 70.52 c 52.63 69.45 51.76 68.58 50.69 68.58 c 49.62 68.58 48.74 69.45 48.74 70.52 c B 52.70 71.10 m 52.70 72.17 53.58 73.05 54.65 73.05 c 55.71 73.05 56.59 72.17 56.59 71.10 c 56.59 70.03 55.71 69.16 54.65 69.16 c 53.58 69.16 52.70 70.03 52.70 71.10 c B 54.21 72.09 m 54.21 73.16 55.09 74.04 56.16 74.04 c 57.23 74.04 58.10 73.16 58.10 72.09 c 58.10 71.03 57.23 70.15 56.16 70.15 c 55.09 70.15 54.21 71.03 54.21 72.09 c B 54.76 69.89 m 54.76 70.96 55.63 71.83 56.70 71.83 c 57.77 71.83 58.64 70.96 58.64 69.89 c 58.64 68.82 57.77 67.95 56.70 67.95 c 55.63 67.95 54.76 68.82 54.76 69.89 c B 59.31 69.15 m 59.31 70.22 60.19 71.09 61.26 71.09 c 62.32 71.09 63.20 70.22 63.20 69.15 c 63.20 68.08 62.32 67.20 61.26 67.20 c 60.19 67.20 59.31 68.08 59.31 69.15 c B 38.10 69.52 m 38.10 70.59 38.97 71.46 40.04 71.46 c 41.11 71.46 41.99 70.59 41.99 69.52 c 41.99 68.45 41.11 67.58 40.04 67.58 c 38.97 67.58 38.10 68.45 38.10 69.52 c B 163.76 78.87 m 163.76 79.94 164.64 80.82 165.70 80.82 c 166.77 80.82 167.65 79.94 167.65 78.87 c 167.65 77.80 166.77 76.93 165.70 76.93 c 164.64 76.93 163.76 77.80 163.76 78.87 c B 75.52 75.83 m 75.52 76.90 76.40 77.78 77.47 77.78 c 78.54 77.78 79.41 76.90 79.41 75.83 c 79.41 74.76 78.54 73.89 77.47 73.89 c 76.40 73.89 75.52 74.76 75.52 75.83 c B 76.48 71.56 m 76.48 72.63 77.36 73.50 78.42 73.50 c 79.49 73.50 80.37 72.63 80.37 71.56 c 80.37 70.49 79.49 69.61 78.42 69.61 c 77.36 69.61 76.48 70.49 76.48 71.56 c B 76.75 72.64 m 76.75 73.71 77.62 74.58 78.69 74.58 c 79.76 74.58 80.63 73.71 80.63 72.64 c 80.63 71.57 79.76 70.70 78.69 70.70 c 77.62 70.70 76.75 71.57 76.75 72.64 c B 77.01 74.18 m 77.01 75.25 77.88 76.12 78.95 76.12 c 80.02 76.12 80.89 75.25 80.89 74.18 c 80.89 73.11 80.02 72.23 78.95 72.23 c 77.88 72.23 77.01 73.11 77.01 74.18 c B 176.52 78.86 m 176.52 79.93 177.40 80.81 178.46 80.81 c 179.53 80.81 180.41 79.93 180.41 78.86 c 180.41 77.79 179.53 76.92 178.46 76.92 c 177.40 76.92 176.52 77.79 176.52 78.86 c B 179.17 78.08 m 179.17 79.14 180.04 80.02 181.11 80.02 c 182.18 80.02 183.06 79.14 183.06 78.08 c 183.06 77.01 182.18 76.13 181.11 76.13 c 180.04 76.13 179.17 77.01 179.17 78.08 c B 180.14 73.88 m 180.14 74.95 181.01 75.82 182.08 75.82 c 183.15 75.82 184.03 74.95 184.03 73.88 c 184.03 72.81 183.15 71.93 182.08 71.93 c 181.01 71.93 180.14 72.81 180.14 73.88 c B 96.06 77.33 m 96.06 78.40 96.94 79.28 98.01 79.28 c 99.08 79.28 99.95 78.40 99.95 77.33 c 99.95 76.27 99.08 75.39 98.01 75.39 c 96.94 75.39 96.06 76.27 96.06 77.33 c B 97.38 69.23 m 97.38 70.30 98.25 71.18 99.32 71.18 c 100.39 71.18 101.26 70.30 101.26 69.23 c 101.26 68.17 100.39 67.29 99.32 67.29 c 98.25 67.29 97.38 68.17 97.38 69.23 c B 97.79 79.12 m 97.79 80.19 98.66 81.07 99.73 81.07 c 100.80 81.07 101.68 80.19 101.68 79.12 c 101.68 78.05 100.80 77.18 99.73 77.18 c 98.66 77.18 97.79 78.05 97.79 79.12 c B 101.75 72.23 m 101.75 73.30 102.62 74.18 103.69 74.18 c 104.76 74.18 105.64 73.30 105.64 72.23 c 105.64 71.16 104.76 70.29 103.69 70.29 c 102.62 70.29 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183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.78 182.62 m 97.78 183.31 98.34 183.87 99.03 183.87 c 99.71 183.87 100.27 183.31 100.27 182.62 c 100.27 181.93 99.71 181.37 99.03 181.37 c 98.34 181.37 97.78 181.93 97.78 182.62 c B 97.86 182.62 m 97.86 183.31 98.43 183.87 99.11 183.87 c 99.80 183.87 100.36 183.31 100.36 182.62 c 100.36 181.93 99.80 181.37 99.11 181.37 c 98.43 181.37 97.86 181.93 97.86 182.62 c B 97.89 182.62 m 97.89 183.31 98.45 183.87 99.13 183.87 c 99.82 183.87 100.38 183.31 100.38 182.62 c 100.38 181.93 99.82 181.37 99.13 181.37 c 98.45 181.37 97.89 181.93 97.89 182.62 c B 97.99 182.62 m 97.99 183.31 98.55 183.87 99.23 183.87 c 99.92 183.87 100.48 183.31 100.48 182.62 c 100.48 181.93 99.92 181.37 99.23 181.37 c 98.55 181.37 97.99 181.93 97.99 182.62 c B 98.01 182.62 m 98.01 183.31 98.57 183.87 99.26 183.87 c 99.94 183.87 100.50 183.31 100.50 182.62 c 100.50 181.93 99.94 181.37 99.26 181.37 c 98.57 181.37 98.01 181.93 98.01 182.62 c B 98.06 182.62 m 98.06 183.31 98.63 183.87 99.31 183.87 c 100.00 183.87 100.56 183.31 100.56 182.62 c 100.56 181.93 100.00 181.37 99.31 181.37 c 98.63 181.37 98.06 181.93 98.06 182.62 c B 98.15 182.62 m 98.15 183.31 98.71 183.87 99.40 183.87 c 100.09 183.87 100.65 183.31 100.65 182.62 c 100.65 181.93 100.09 181.37 99.40 181.37 c 98.71 181.37 98.15 181.93 98.15 182.62 c B 98.31 182.62 m 98.31 183.31 98.87 183.87 99.56 183.87 c 100.25 183.87 100.81 183.31 100.81 182.62 c 100.81 181.93 100.25 181.37 99.56 181.37 c 98.87 181.37 98.31 181.93 98.31 182.62 c B 98.45 182.62 m 98.45 183.31 99.01 183.87 99.70 183.87 c 100.39 183.87 100.95 183.31 100.95 182.62 c 100.95 181.93 100.39 181.37 99.70 181.37 c 99.01 181.37 98.45 181.93 98.45 182.62 c B 98.51 182.62 m 98.51 183.31 99.07 183.87 99.76 183.87 c 100.44 183.87 101.00 183.31 101.00 182.62 c 101.00 181.93 100.44 181.37 99.76 181.37 c 99.07 181.37 98.51 181.93 98.51 182.62 c B 98.58 182.62 m 98.58 183.31 99.14 183.87 99.82 183.87 c 100.51 183.87 101.07 183.31 101.07 182.62 c 101.07 181.93 100.51 181.37 99.82 181.37 c 99.14 181.37 98.58 181.93 98.58 182.62 c B 98.67 182.62 m 98.67 183.31 99.23 183.87 99.92 183.87 c 100.60 183.87 101.16 183.31 101.16 182.62 c 101.16 181.93 100.60 181.37 99.92 181.37 c 99.23 181.37 98.67 181.93 98.67 182.62 c B 98.84 182.62 m 98.84 183.31 99.40 183.87 100.09 183.87 c 100.77 183.87 101.33 183.31 101.33 182.62 c 101.33 181.93 100.77 181.37 100.09 181.37 c 99.40 181.37 98.84 181.93 98.84 182.62 c B 98.89 182.62 m 98.89 183.31 99.45 183.87 100.14 183.87 c 100.82 183.87 101.38 183.31 101.38 182.62 c 101.38 181.93 100.82 181.37 100.14 181.37 c 99.45 181.37 98.89 181.93 98.89 182.62 c B 99.01 182.62 m 99.01 183.31 99.57 183.87 100.25 183.87 c 100.94 183.87 101.50 183.31 101.50 182.62 c 101.50 181.93 100.94 181.37 100.25 181.37 c 99.57 181.37 99.01 181.93 99.01 182.62 c B 99.02 182.62 m 99.02 183.31 99.58 183.87 100.27 183.87 c 100.95 183.87 101.51 183.31 101.51 182.62 c 101.51 181.93 100.95 181.37 100.27 181.37 c 99.58 181.37 99.02 181.93 99.02 182.62 c B 99.08 182.62 m 99.08 183.31 99.64 183.87 100.33 183.87 c 101.01 183.87 101.57 183.31 101.57 182.62 c 101.57 181.93 101.01 181.37 100.33 181.37 c 99.64 181.37 99.08 181.93 99.08 182.62 c B 99.12 182.62 m 99.12 183.31 99.68 183.87 100.37 183.87 c 101.06 183.87 101.62 183.31 101.62 182.62 c 101.62 181.93 101.06 181.37 100.37 181.37 c 99.68 181.37 99.12 181.93 99.12 182.62 c B 99.18 182.62 m 99.18 183.31 99.74 183.87 100.42 183.87 c 101.11 183.87 101.67 183.31 101.67 182.62 c 101.67 181.93 101.11 181.37 100.42 181.37 c 99.74 181.37 99.18 181.93 99.18 182.62 c B 99.32 182.62 m 99.32 183.31 99.88 183.87 100.57 183.87 c 101.25 183.87 101.82 183.31 101.82 182.62 c 101.82 181.93 101.25 181.37 100.57 181.37 c 99.88 181.37 99.32 181.93 99.32 182.62 c B 99.45 182.62 m 99.45 183.31 100.01 183.87 100.70 183.87 c 101.39 183.87 101.95 183.31 101.95 182.62 c 101.95 181.93 101.39 181.37 100.70 181.37 c 100.01 181.37 99.45 181.93 99.45 182.62 c B 99.54 182.62 m 99.54 183.31 100.10 183.87 100.79 183.87 c 101.47 183.87 102.04 183.31 102.04 182.62 c 102.04 181.93 101.47 181.37 100.79 181.37 c 100.10 181.37 99.54 181.93 99.54 182.62 c B 99.60 182.62 m 99.60 183.31 100.17 183.87 100.85 183.87 c 101.54 183.87 102.10 183.31 102.10 182.62 c 102.10 181.93 101.54 181.37 100.85 181.37 c 100.17 181.37 99.60 181.93 99.60 182.62 c B 99.86 182.62 m 99.86 183.31 100.42 183.87 101.11 183.87 c 101.80 183.87 102.36 183.31 102.36 182.62 c 102.36 181.93 101.80 181.37 101.11 181.37 c 100.42 181.37 99.86 181.93 99.86 182.62 c B 100.00 182.62 m 100.00 183.31 100.56 183.87 101.25 183.87 c 101.93 183.87 102.49 183.31 102.49 182.62 c 102.49 181.93 101.93 181.37 101.25 181.37 c 100.56 181.37 100.00 181.93 100.00 182.62 c B 100.06 182.62 m 100.06 183.31 100.62 183.87 101.30 183.87 c 101.99 183.87 102.55 183.31 102.55 182.62 c 102.55 181.93 101.99 181.37 101.30 181.37 c 100.62 181.37 100.06 181.93 100.06 182.62 c B 100.11 182.62 m 100.11 183.31 100.67 183.87 101.36 183.87 c 102.05 183.87 102.61 183.31 102.61 182.62 c 102.61 181.93 102.05 181.37 101.36 181.37 c 100.67 181.37 100.11 181.93 100.11 182.62 c B 100.34 182.62 m 100.34 183.31 100.90 183.87 101.58 183.87 c 102.27 183.87 102.83 183.31 102.83 182.62 c 102.83 181.93 102.27 181.37 101.58 181.37 c 100.90 181.37 100.34 181.93 100.34 182.62 c B 100.38 182.62 m 100.38 183.31 100.94 183.87 101.63 183.87 c 102.31 183.87 102.87 183.31 102.87 182.62 c 102.87 181.93 102.31 181.37 101.63 181.37 c 100.94 181.37 100.38 181.93 100.38 182.62 c B 100.53 182.62 m 100.53 183.31 101.09 183.87 101.78 183.87 c 102.46 183.87 103.03 183.31 103.03 182.62 c 103.03 181.93 102.46 181.37 101.78 181.37 c 101.09 181.37 100.53 181.93 100.53 182.62 c B 100.81 182.62 m 100.81 183.31 101.38 183.87 102.06 183.87 c 102.75 183.87 103.31 183.31 103.31 182.62 c 103.31 181.93 102.75 181.37 102.06 181.37 c 101.38 181.37 100.81 181.93 100.81 182.62 c B 100.95 182.62 m 100.95 183.31 101.52 183.87 102.20 183.87 c 102.89 183.87 103.45 183.31 103.45 182.62 c 103.45 181.93 102.89 181.37 102.20 181.37 c 101.52 181.37 100.95 181.93 100.95 182.62 c B 101.03 182.62 m 101.03 183.31 101.59 183.87 102.28 183.87 c 102.96 183.87 103.53 183.31 103.53 182.62 c 103.53 181.93 102.96 181.37 102.28 181.37 c 101.59 181.37 101.03 181.93 101.03 182.62 c B 101.12 182.62 m 101.12 183.31 101.68 183.87 102.36 183.87 c 103.05 183.87 103.61 183.31 103.61 182.62 c 103.61 181.93 103.05 181.37 102.36 181.37 c 101.68 181.37 101.12 181.93 101.12 182.62 c B 101.18 182.62 m 101.18 183.31 101.74 183.87 102.43 183.87 c 103.11 183.87 103.67 183.31 103.67 182.62 c 103.67 181.93 103.11 181.37 102.43 181.37 c 101.74 181.37 101.18 181.93 101.18 182.62 c B 101.36 182.62 m 101.36 183.31 101.92 183.87 102.60 183.87 c 103.29 183.87 103.85 183.31 103.85 182.62 c 103.85 181.93 103.29 181.37 102.60 181.37 c 101.92 181.37 101.36 181.93 101.36 182.62 c B 101.41 182.62 m 101.41 183.31 101.97 183.87 102.66 183.87 c 103.34 183.87 103.91 183.31 103.91 182.62 c 103.91 181.93 103.34 181.37 102.66 181.37 c 101.97 181.37 101.41 181.93 101.41 182.62 c B 101.60 182.62 m 101.60 183.31 102.16 183.87 102.84 183.87 c 103.53 183.87 104.09 183.31 104.09 182.62 c 104.09 181.93 103.53 181.37 102.84 181.37 c 102.16 181.37 101.60 181.93 101.60 182.62 c B 101.96 182.62 m 101.96 183.31 102.52 183.87 103.20 183.87 c 103.89 183.87 104.45 183.31 104.45 182.62 c 104.45 181.93 103.89 181.37 103.20 181.37 c 102.52 181.37 101.96 181.93 101.96 182.62 c B 102.25 182.62 m 102.25 183.31 102.81 183.87 103.50 183.87 c 104.18 183.87 104.74 183.31 104.74 182.62 c 104.74 181.93 104.18 181.37 103.50 181.37 c 102.81 181.37 102.25 181.93 102.25 182.62 c B 102.57 182.62 m 102.57 183.31 103.13 183.87 103.82 183.87 c 104.50 183.87 105.07 183.31 105.07 182.62 c 105.07 181.93 104.50 181.37 103.82 181.37 c 103.13 181.37 102.57 181.93 102.57 182.62 c B 102.85 182.62 m 102.85 183.31 103.41 183.87 104.09 183.87 c 104.78 183.87 105.34 183.31 105.34 182.62 c 105.34 181.93 104.78 181.37 104.09 181.37 c 103.41 181.37 102.85 181.93 102.85 182.62 c B 102.88 182.62 m 102.88 183.31 103.44 183.87 104.13 183.87 c 104.81 183.87 105.37 183.31 105.37 182.62 c 105.37 181.93 104.81 181.37 104.13 181.37 c 103.44 181.37 102.88 181.93 102.88 182.62 c B 103.02 182.62 m 103.02 183.31 103.58 183.87 104.27 183.87 c 104.95 183.87 105.51 183.31 105.51 182.62 c 105.51 181.93 104.95 181.37 104.27 181.37 c 103.58 181.37 103.02 181.93 103.02 182.62 c B 103.28 182.62 m 103.28 183.31 103.84 183.87 104.53 183.87 c 105.22 183.87 105.78 183.31 105.78 182.62 c 105.78 181.93 105.22 181.37 104.53 181.37 c 103.84 181.37 103.28 181.93 103.28 182.62 c B 103.46 182.62 m 103.46 183.31 104.02 183.87 104.71 183.87 c 105.39 183.87 105.95 183.31 105.95 182.62 c 105.95 181.93 105.39 181.37 104.71 181.37 c 104.02 181.37 103.46 181.93 103.46 182.62 c B 103.54 182.62 m 103.54 183.31 104.10 183.87 104.79 183.87 c 105.47 183.87 106.03 183.31 106.03 182.62 c 106.03 181.93 105.47 181.37 104.79 181.37 c 104.10 181.37 103.54 181.93 103.54 182.62 c B 103.66 182.62 m 103.66 183.31 104.22 183.87 104.91 183.87 c 105.59 183.87 106.15 183.31 106.15 182.62 c 106.15 181.93 105.59 181.37 104.91 181.37 c 104.22 181.37 103.66 181.93 103.66 182.62 c B 103.89 182.62 m 103.89 183.31 104.45 183.87 105.13 183.87 c 105.82 183.87 106.38 183.31 106.38 182.62 c 106.38 181.93 105.82 181.37 105.13 181.37 c 104.45 181.37 103.89 181.93 103.89 182.62 c B 104.05 182.62 m 104.05 183.31 104.61 183.87 105.30 183.87 c 105.99 183.87 106.55 183.31 106.55 182.62 c 106.55 181.93 105.99 181.37 105.30 181.37 c 104.61 181.37 104.05 181.93 104.05 182.62 c B 104.33 182.62 m 104.33 183.31 104.89 183.87 105.57 183.87 c 106.26 183.87 106.82 183.31 106.82 182.62 c 106.82 181.93 106.26 181.37 105.57 181.37 c 104.89 181.37 104.33 181.93 104.33 182.62 c B 104.48 182.62 m 104.48 183.31 105.04 183.87 105.72 183.87 c 106.41 183.87 106.97 183.31 106.97 182.62 c 106.97 181.93 106.41 181.37 105.72 181.37 c 105.04 181.37 104.48 181.93 104.48 182.62 c B 104.62 182.62 m 104.62 183.31 105.18 183.87 105.87 183.87 c 106.56 183.87 107.12 183.31 107.12 182.62 c 107.12 181.93 106.56 181.37 105.87 181.37 c 105.18 181.37 104.62 181.93 104.62 182.62 c B 104.85 182.62 m 104.85 183.31 105.41 183.87 106.10 183.87 c 106.79 183.87 107.35 183.31 107.35 182.62 c 107.35 181.93 106.79 181.37 106.10 181.37 c 105.41 181.37 104.85 181.93 104.85 182.62 c B 105.21 182.62 m 105.21 183.31 105.77 183.87 106.45 183.87 c 107.14 183.87 107.70 183.31 107.70 182.62 c 107.70 181.93 107.14 181.37 106.45 181.37 c 105.77 181.37 105.21 181.93 105.21 182.62 c B 105.51 182.62 m 105.51 183.31 106.07 183.87 106.76 183.87 c 107.44 183.87 108.00 183.31 108.00 182.62 c 108.00 181.93 107.44 181.37 106.76 181.37 c 106.07 181.37 105.51 181.93 105.51 182.62 c B 105.84 182.62 m 105.84 183.31 106.40 183.87 107.09 183.87 c 107.78 183.87 108.34 183.31 108.34 182.62 c 108.34 181.93 107.78 181.37 107.09 181.37 c 106.40 181.37 105.84 181.93 105.84 182.62 c B 106.19 182.62 m 106.19 183.31 106.75 183.87 107.44 183.87 c 108.12 183.87 108.68 183.31 108.68 182.62 c 108.68 181.93 108.12 181.37 107.44 181.37 c 106.75 181.37 106.19 181.93 106.19 182.62 c B 106.52 182.62 m 106.52 183.31 107.08 183.87 107.76 183.87 c 108.45 183.87 109.01 183.31 109.01 182.62 c 109.01 181.93 108.45 181.37 107.76 181.37 c 107.08 181.37 106.52 181.93 106.52 182.62 c B 106.90 182.62 m 106.90 183.31 107.46 183.87 108.15 183.87 c 108.83 183.87 109.40 183.31 109.40 182.62 c 109.40 181.93 108.83 181.37 108.15 181.37 c 107.46 181.37 106.90 181.93 106.90 182.62 c B 107.12 182.62 m 107.12 183.31 107.68 183.87 108.37 183.87 c 109.05 183.87 109.62 183.31 109.62 182.62 c 109.62 181.93 109.05 181.37 108.37 181.37 c 107.68 181.37 107.12 181.93 107.12 182.62 c B 107.44 182.62 m 107.44 183.31 108.00 183.87 108.69 183.87 c 109.38 183.87 109.94 183.31 109.94 182.62 c 109.94 181.93 109.38 181.37 108.69 181.37 c 108.00 181.37 107.44 181.93 107.44 182.62 c B 107.74 182.62 m 107.74 183.31 108.30 183.87 108.99 183.87 c 109.67 183.87 110.23 183.31 110.23 182.62 c 110.23 181.93 109.67 181.37 108.99 181.37 c 108.30 181.37 107.74 181.93 107.74 182.62 c B 108.48 182.62 m 108.48 183.31 109.04 183.87 109.73 183.87 c 110.42 183.87 110.98 183.31 110.98 182.62 c 110.98 181.93 110.42 181.37 109.73 181.37 c 109.04 181.37 108.48 181.93 108.48 182.62 c B 108.63 182.62 m 108.63 183.31 109.19 183.87 109.88 183.87 c 110.56 183.87 111.13 183.31 111.13 182.62 c 111.13 181.93 110.56 181.37 109.88 181.37 c 109.19 181.37 108.63 181.93 108.63 182.62 c B 109.08 182.62 m 109.08 183.31 109.64 183.87 110.32 183.87 c 111.01 183.87 111.57 183.31 111.57 182.62 c 111.57 181.93 111.01 181.37 110.32 181.37 c 109.64 181.37 109.08 181.93 109.08 182.62 c B 109.56 182.62 m 109.56 183.31 110.12 183.87 110.81 183.87 c 111.50 183.87 112.06 183.31 112.06 182.62 c 112.06 181.93 111.50 181.37 110.81 181.37 c 110.12 181.37 109.56 181.93 109.56 182.62 c B 109.71 182.62 m 109.71 183.31 110.27 183.87 110.96 183.87 c 111.64 183.87 112.21 183.31 112.21 182.62 c 112.21 181.93 111.64 181.37 110.96 181.37 c 110.27 181.37 109.71 181.93 109.71 182.62 c B 109.89 182.62 m 109.89 183.31 110.45 183.87 111.14 183.87 c 111.83 183.87 112.39 183.31 112.39 182.62 c 112.39 181.93 111.83 181.37 111.14 181.37 c 110.45 181.37 109.89 181.93 109.89 182.62 c B 110.26 182.62 m 110.26 183.31 110.82 183.87 111.51 183.87 c 112.19 183.87 112.76 183.31 112.76 182.62 c 112.76 181.93 112.19 181.37 111.51 181.37 c 110.82 181.37 110.26 181.93 110.26 182.62 c B 110.35 182.62 m 110.35 183.31 110.91 183.87 111.59 183.87 c 112.28 183.87 112.84 183.31 112.84 182.62 c 112.84 181.93 112.28 181.37 111.59 181.37 c 110.91 181.37 110.35 181.93 110.35 182.62 c B 111.08 182.62 m 111.08 183.31 111.64 183.87 112.32 183.87 c 113.01 183.87 113.57 183.31 113.57 182.62 c 113.57 181.93 113.01 181.37 112.32 181.37 c 111.64 181.37 111.08 181.93 111.08 182.62 c B 111.73 182.62 m 111.73 183.31 112.29 183.87 112.98 183.87 c 113.67 183.87 114.23 183.31 114.23 182.62 c 114.23 181.93 113.67 181.37 112.98 181.37 c 112.29 181.37 111.73 181.93 111.73 182.62 c B 112.80 182.62 m 112.80 183.31 113.36 183.87 114.04 183.87 c 114.73 183.87 115.29 183.31 115.29 182.62 c 115.29 181.93 114.73 181.37 114.04 181.37 c 113.36 181.37 112.80 181.93 112.80 182.62 c B 113.64 182.62 m 113.64 183.31 114.20 183.87 114.89 183.87 c 115.57 183.87 116.13 183.31 116.13 182.62 c 116.13 181.93 115.57 181.37 114.89 181.37 c 114.20 181.37 113.64 181.93 113.64 182.62 c B 113.75 182.62 m 113.75 183.31 114.31 183.87 115.00 183.87 c 115.68 183.87 116.24 183.31 116.24 182.62 c 116.24 181.93 115.68 181.37 115.00 181.37 c 114.31 181.37 113.75 181.93 113.75 182.62 c B 114.15 182.62 m 114.15 183.31 114.71 183.87 115.40 183.87 c 116.08 183.87 116.64 183.31 116.64 182.62 c 116.64 181.93 116.08 181.37 115.40 181.37 c 114.71 181.37 114.15 181.93 114.15 182.62 c B 114.60 182.62 m 114.60 183.31 115.16 183.87 115.85 183.87 c 116.53 183.87 117.09 183.31 117.09 182.62 c 117.09 181.93 116.53 181.37 115.85 181.37 c 115.16 181.37 114.60 181.93 114.60 182.62 c B 114.76 182.62 m 114.76 183.31 115.32 183.87 116.01 183.87 c 116.69 183.87 117.25 183.31 117.25 182.62 c 117.25 181.93 116.69 181.37 116.01 181.37 c 115.32 181.37 114.76 181.93 114.76 182.62 c B 114.83 182.62 m 114.83 183.31 115.39 183.87 116.07 183.87 c 116.76 183.87 117.32 183.31 117.32 182.62 c 117.32 181.93 116.76 181.37 116.07 181.37 c 115.39 181.37 114.83 181.93 114.83 182.62 c B 115.09 182.62 m 115.09 183.31 115.65 183.87 116.34 183.87 c 117.02 183.87 117.58 183.31 117.58 182.62 c 117.58 181.93 117.02 181.37 116.34 181.37 c 115.65 181.37 115.09 181.93 115.09 182.62 c B 115.19 182.62 m 115.19 183.31 115.75 183.87 116.44 183.87 c 117.12 183.87 117.69 183.31 117.69 182.62 c 117.69 181.93 117.12 181.37 116.44 181.37 c 115.75 181.37 115.19 181.93 115.19 182.62 c B 115.34 182.62 m 115.34 183.31 115.90 183.87 116.59 183.87 c 117.27 183.87 117.83 183.31 117.83 182.62 c 117.83 181.93 117.27 181.37 116.59 181.37 c 115.90 181.37 115.34 181.93 115.34 182.62 c B 115.72 183.41 m 115.72 184.10 116.28 184.66 116.97 184.66 c 117.65 184.66 118.22 184.10 118.22 183.41 c 118.22 182.73 117.65 182.17 116.97 182.17 c 116.28 182.17 115.72 182.73 115.72 183.41 c B 116.21 183.50 m 116.21 184.18 116.77 184.74 117.45 184.74 c 118.14 184.74 118.70 184.18 118.70 183.50 c 118.70 182.81 118.14 182.25 117.45 182.25 c 116.77 182.25 116.21 182.81 116.21 183.50 c B 116.30 184.09 m 116.30 184.78 116.86 185.34 117.55 185.34 c 118.23 185.34 118.79 184.78 118.79 184.09 c 118.79 183.40 118.23 182.84 117.55 182.84 c 116.86 182.84 116.30 183.40 116.30 184.09 c B 116.71 184.15 m 116.71 184.84 117.27 185.40 117.95 185.40 c 118.64 185.40 119.20 184.84 119.20 184.15 c 119.20 183.46 118.64 182.90 117.95 182.90 c 117.27 182.90 116.71 183.46 116.71 184.15 c B 117.10 184.38 m 117.10 185.07 117.67 185.63 118.35 185.63 c 119.04 185.63 119.60 185.07 119.60 184.38 c 119.60 183.70 119.04 183.14 118.35 183.14 c 117.67 183.14 117.10 183.70 117.10 184.38 c B 117.32 184.38 m 117.32 185.07 117.89 185.63 118.57 185.63 c 119.26 185.63 119.82 185.07 119.82 184.38 c 119.82 183.70 119.26 183.14 118.57 183.14 c 117.89 183.14 117.32 183.70 117.32 184.38 c B 117.33 184.40 m 117.33 185.09 117.89 185.65 118.58 185.65 c 119.27 185.65 119.83 185.09 119.83 184.40 c 119.83 183.72 119.27 183.16 118.58 183.16 c 117.89 183.16 117.33 183.72 117.33 184.40 c B 117.82 184.92 m 117.82 185.61 118.38 186.17 119.07 186.17 c 119.75 186.17 120.31 185.61 120.31 184.92 c 120.31 184.24 119.75 183.68 119.07 183.68 c 118.38 183.68 117.82 184.24 117.82 184.92 c B 118.42 185.24 m 118.42 185.92 118.98 186.49 119.67 186.49 c 120.35 186.49 120.92 185.92 120.92 185.24 c 120.92 184.55 120.35 183.99 119.67 183.99 c 118.98 183.99 118.42 184.55 118.42 185.24 c B 118.75 185.68 m 118.75 186.37 119.31 186.93 120.00 186.93 c 120.68 186.93 121.25 186.37 121.25 185.68 c 121.25 184.99 120.68 184.43 120.00 184.43 c 119.31 184.43 118.75 184.99 118.75 185.68 c B 118.92 186.06 m 118.92 186.75 119.48 187.31 120.16 187.31 c 120.85 187.31 121.41 186.75 121.41 186.06 c 121.41 185.37 120.85 184.81 120.16 184.81 c 119.48 184.81 118.92 185.37 118.92 186.06 c B 119.73 186.24 m 119.73 186.92 120.29 187.48 120.98 187.48 c 121.67 187.48 122.23 186.92 122.23 186.24 c 122.23 185.55 121.67 184.99 120.98 184.99 c 120.29 184.99 119.73 185.55 119.73 186.24 c B 120.85 186.44 m 120.85 187.13 121.41 187.69 122.10 187.69 c 122.78 187.69 123.34 187.13 123.34 186.44 c 123.34 185.76 122.78 185.19 122.10 185.19 c 121.41 185.19 120.85 185.76 120.85 186.44 c B 121.08 186.44 m 121.08 187.13 121.64 187.69 122.33 187.69 c 123.01 187.69 123.57 187.13 123.57 186.44 c 123.57 185.76 123.01 185.19 122.33 185.19 c 121.64 185.19 121.08 185.76 121.08 186.44 c B 121.47 186.53 m 121.47 187.22 122.03 187.78 122.72 187.78 c 123.41 187.78 123.97 187.22 123.97 186.53 c 123.97 185.84 123.41 185.28 122.72 185.28 c 122.03 185.28 121.47 185.84 121.47 186.53 c B 121.63 186.56 m 121.63 187.24 122.19 187.80 122.88 187.80 c 123.56 187.80 124.12 187.24 124.12 186.56 c 124.12 185.87 123.56 185.31 122.88 185.31 c 122.19 185.31 121.63 185.87 121.63 186.56 c B 121.88 186.73 m 121.88 187.42 122.44 187.98 123.13 187.98 c 123.81 187.98 124.37 187.42 124.37 186.73 c 124.37 186.04 123.81 185.48 123.13 185.48 c 122.44 185.48 121.88 186.04 121.88 186.73 c B 122.06 187.32 m 122.06 188.01 122.63 188.57 123.31 188.57 c 124.00 188.57 124.56 188.01 124.56 187.32 c 124.56 186.64 124.00 186.07 123.31 186.07 c 122.63 186.07 122.06 186.64 122.06 187.32 c B 122.56 188.27 m 122.56 188.95 123.12 189.51 123.81 189.51 c 124.50 189.51 125.06 188.95 125.06 188.27 c 125.06 187.58 124.50 187.02 123.81 187.02 c 123.12 187.02 122.56 187.58 122.56 188.27 c B 122.61 189.11 m 122.61 189.79 123.17 190.35 123.85 190.35 c 124.54 190.35 125.10 189.79 125.10 189.11 c 125.10 188.42 124.54 187.86 123.85 187.86 c 123.17 187.86 122.61 188.42 122.61 189.11 c B 123.41 189.17 m 123.41 189.85 123.98 190.41 124.66 190.41 c 125.35 190.41 125.91 189.85 125.91 189.17 c 125.91 188.48 125.35 187.92 124.66 187.92 c 123.98 187.92 123.41 188.48 123.41 189.17 c B 123.91 189.17 m 123.91 189.85 124.47 190.41 125.16 190.41 c 125.84 190.41 126.40 189.85 126.40 189.17 c 126.40 188.48 125.84 187.92 125.16 187.92 c 124.47 187.92 123.91 188.48 123.91 189.17 c B 124.29 189.18 m 124.29 189.87 124.85 190.43 125.53 190.43 c 126.22 190.43 126.78 189.87 126.78 189.18 c 126.78 188.49 126.22 187.93 125.53 187.93 c 124.85 187.93 124.29 188.49 124.29 189.18 c B 125.31 189.97 m 125.31 190.66 125.88 191.22 126.56 191.22 c 127.25 191.22 127.81 190.66 127.81 189.97 c 127.81 189.28 127.25 188.72 126.56 188.72 c 125.88 188.72 125.31 189.28 125.31 189.97 c B 126.04 190.20 m 126.04 190.89 126.60 191.45 127.29 191.45 c 127.97 191.45 128.53 190.89 128.53 190.20 c 128.53 189.51 127.97 188.95 127.29 188.95 c 126.60 188.95 126.04 189.51 126.04 190.20 c B 126.47 191.55 m 126.47 192.23 127.03 192.79 127.71 192.79 c 128.40 192.79 128.96 192.23 128.96 191.55 c 128.96 190.86 128.40 190.30 127.71 190.30 c 127.03 190.30 126.47 190.86 126.47 191.55 c B 126.57 192.29 m 126.57 192.98 127.13 193.54 127.81 193.54 c 128.50 193.54 129.06 192.98 129.06 192.29 c 129.06 191.61 128.50 191.05 127.81 191.05 c 127.13 191.05 126.57 191.61 126.57 192.29 c B 127.19 192.44 m 127.19 193.13 127.75 193.69 128.44 193.69 c 129.13 193.69 129.69 193.13 129.69 192.44 c 129.69 191.75 129.13 191.19 128.44 191.19 c 127.75 191.19 127.19 191.75 127.19 192.44 c B 127.34 192.62 m 127.34 193.30 127.90 193.86 128.59 193.86 c 129.28 193.86 129.84 193.30 129.84 192.62 c 129.84 191.93 129.28 191.37 128.59 191.37 c 127.90 191.37 127.34 191.93 127.34 192.62 c B 128.77 193.47 m 128.77 194.16 129.33 194.72 130.02 194.72 c 130.71 194.72 131.27 194.16 131.27 193.47 c 131.27 192.78 130.71 192.22 130.02 192.22 c 129.33 192.22 128.77 192.78 128.77 193.47 c B 129.50 193.80 m 129.50 194.49 130.06 195.05 130.75 195.05 c 131.43 195.05 131.99 194.49 131.99 193.80 c 131.99 193.11 131.43 192.55 130.75 192.55 c 130.06 192.55 129.50 193.11 129.50 193.80 c B 129.68 194.06 m 129.68 194.75 130.24 195.31 130.93 195.31 c 131.62 195.31 132.18 194.75 132.18 194.06 c 132.18 193.38 131.62 192.81 130.93 192.81 c 130.24 192.81 129.68 193.38 129.68 194.06 c B 130.00 194.16 m 130.00 194.85 130.56 195.41 131.24 195.41 c 131.93 195.41 132.49 194.85 132.49 194.16 c 132.49 193.48 131.93 192.92 131.24 192.92 c 130.56 192.92 130.00 193.48 130.00 194.16 c B 131.28 194.68 m 131.28 195.36 131.84 195.92 132.53 195.92 c 133.22 195.92 133.78 195.36 133.78 194.68 c 133.78 193.99 133.22 193.43 132.53 193.43 c 131.84 193.43 131.28 193.99 131.28 194.68 c B 131.93 197.62 m 131.93 198.30 132.49 198.86 133.18 198.86 c 133.87 198.86 134.43 198.30 134.43 197.62 c 134.43 196.93 133.87 196.37 133.18 196.37 c 132.49 196.37 131.93 196.93 131.93 197.62 c B 133.55 197.81 m 133.55 198.50 134.11 199.06 134.80 199.06 c 135.48 199.06 136.05 198.50 136.05 197.81 c 136.05 197.12 135.48 196.56 134.80 196.56 c 134.11 196.56 133.55 197.12 133.55 197.81 c B 134.00 198.95 m 134.00 199.63 134.56 200.19 135.25 200.19 c 135.93 200.19 136.49 199.63 136.49 198.95 c 136.49 198.26 135.93 197.70 135.25 197.70 c 134.56 197.70 134.00 198.26 134.00 198.95 c B 134.18 199.35 m 134.18 200.04 134.74 200.60 135.43 200.60 c 136.11 200.60 136.68 200.04 136.68 199.35 c 136.68 198.66 136.11 198.10 135.43 198.10 c 134.74 198.10 134.18 198.66 134.18 199.35 c B 134.79 199.36 m 134.79 200.04 135.36 200.61 136.04 200.61 c 136.73 200.61 137.29 200.04 137.29 199.36 c 137.29 198.67 136.73 198.11 136.04 198.11 c 135.36 198.11 134.79 198.67 134.79 199.36 c B 135.81 200.00 m 135.81 200.69 136.37 201.25 137.05 201.25 c 137.74 201.25 138.30 200.69 138.30 200.00 c 138.30 199.32 137.74 198.76 137.05 198.76 c 136.37 198.76 135.81 199.32 135.81 200.00 c B 136.30 201.73 m 136.30 202.42 136.86 202.98 137.54 202.98 c 138.23 202.98 138.79 202.42 138.79 201.73 c 138.79 201.05 138.23 200.48 137.54 200.48 c 136.86 200.48 136.30 201.05 136.30 201.73 c B 137.50 202.03 m 137.50 202.71 138.06 203.27 138.75 203.27 c 139.44 203.27 140.00 202.71 140.00 202.03 c 140.00 201.34 139.44 200.78 138.75 200.78 c 138.06 200.78 137.50 201.34 137.50 202.03 c B 138.97 203.49 m 138.97 204.18 139.53 204.74 140.21 204.74 c 140.90 204.74 141.46 204.18 141.46 203.49 c 141.46 202.80 140.90 202.24 140.21 202.24 c 139.53 202.24 138.97 202.80 138.97 203.49 c B 139.38 204.48 m 139.38 205.17 139.94 205.73 140.63 205.73 c 141.32 205.73 141.88 205.17 141.88 204.48 c 141.88 203.80 141.32 203.24 140.63 203.24 c 139.94 203.24 139.38 203.80 139.38 204.48 c B 141.34 204.67 m 141.34 205.35 141.90 205.91 142.59 205.91 c 143.28 205.91 143.84 205.35 143.84 204.67 c 143.84 203.98 143.28 203.42 142.59 203.42 c 141.90 203.42 141.34 203.98 141.34 204.67 c B 142.59 206.88 m 142.59 207.56 143.15 208.12 143.83 208.12 c 144.52 208.12 145.08 207.56 145.08 206.88 c 145.08 206.19 144.52 205.63 143.83 205.63 c 143.15 205.63 142.59 206.19 142.59 206.88 c B 144.16 207.11 m 144.16 207.79 144.72 208.36 145.40 208.36 c 146.09 208.36 146.65 207.79 146.65 207.11 c 146.65 206.42 146.09 205.86 145.40 205.86 c 144.72 205.86 144.16 206.42 144.16 207.11 c B 145.73 207.17 m 145.73 207.85 146.29 208.42 146.98 208.42 c 147.66 208.42 148.23 207.85 148.23 207.17 c 148.23 206.48 147.66 205.92 146.98 205.92 c 146.29 205.92 145.73 206.48 145.73 207.17 c B 146.61 208.35 m 146.61 209.03 147.17 209.60 147.86 209.60 c 148.54 209.60 149.11 209.03 149.11 208.35 c 149.11 207.66 148.54 207.10 147.86 207.10 c 147.17 207.10 146.61 207.66 146.61 208.35 c B 148.28 208.76 m 148.28 209.45 148.84 210.01 149.53 210.01 c 150.21 210.01 150.77 209.45 150.77 208.76 c 150.77 208.08 150.21 207.52 149.53 207.52 c 148.84 207.52 148.28 208.08 148.28 208.76 c B 152.94 211.72 m 152.94 212.41 153.50 212.97 154.19 212.97 c 154.87 212.97 155.43 212.41 155.43 211.72 c 155.43 211.04 154.87 210.47 154.19 210.47 c 153.50 210.47 152.94 211.04 152.94 211.72 c B 153.64 218.79 m 153.64 219.47 154.21 220.03 154.89 220.03 c 155.58 220.03 156.14 219.47 156.14 218.79 c 156.14 218.10 155.58 217.54 154.89 217.54 c 154.21 217.54 153.64 218.10 153.64 218.79 c B 153.64 228.86 m 153.64 229.55 154.21 230.11 154.89 230.11 c 155.58 230.11 156.14 229.55 156.14 228.86 c 156.14 228.18 155.58 227.61 154.89 227.61 c 154.21 227.61 153.64 228.18 153.64 228.86 c B 153.64 245.11 m 153.64 245.79 154.21 246.36 154.89 246.36 c 155.58 246.36 156.14 245.79 156.14 245.11 c 156.14 244.42 155.58 243.86 154.89 243.86 c 154.21 243.86 153.64 244.42 153.64 245.11 c B Q q 91.25 175.37 91.25 76.99 re W n Q q 96.00 180.12 81.74 67.49 re W n /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 96.00 180.12 m 177.74 247.61 l S 0.75 w [ 3.00 5.00] 0 d 96.00 186.37 m 177.74 253.86 l S 96.00 173.87 m 177.74 241.36 l S Q q /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 96.00 182.62 m 96.00 245.04 l S 96.00 182.62 m 91.25 182.62 l S 96.00 191.54 m 91.25 191.54 l S 96.00 200.45 m 91.25 200.45 l S 96.00 209.37 m 91.25 209.37 l S 96.00 218.29 m 91.25 218.29 l S 96.00 227.20 m 91.25 227.20 l S 96.00 236.12 m 91.25 236.12 l S 96.00 245.04 m 91.25 245.04 l S BT /GS258 gs 0.000 0.000 0.000 rg /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 180.40 Tm (0) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 196.01 Tm (10) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 213.84 Tm (20) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 231.67 Tm (30) Tj ET 96.00 180.12 m 177.74 180.12 l 177.74 247.61 l 96.00 247.61 l 96.00 180.12 l S Q q 187.25 180.12 81.74 67.49 re W n Q q 187.25 180.12 81.74 67.49 re W n /GS257 gs 0.000 0.000 1.000 rg /GS1 gs 0.000 0.000 1.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.03 182.62 m 189.03 183.31 189.59 183.87 190.28 183.87 c 190.96 183.87 191.52 183.31 191.52 182.62 c 191.52 181.93 190.96 181.37 190.28 181.37 c 189.59 181.37 189.03 181.93 189.03 182.62 c B 189.10 182.62 m 189.10 183.31 189.66 183.87 190.35 183.87 c 191.03 183.87 191.59 183.31 191.59 182.62 c 191.59 181.93 191.03 181.37 190.35 181.37 c 189.66 181.37 189.10 181.93 189.10 182.62 c B 189.13 182.62 m 189.13 183.31 189.70 183.87 190.38 183.87 c 191.07 183.87 191.63 183.31 191.63 182.62 c 191.63 181.93 191.07 181.37 190.38 181.37 c 189.70 181.37 189.13 181.93 189.13 182.62 c B 189.13 182.62 m 189.13 183.31 189.70 183.87 190.38 183.87 c 191.07 183.87 191.63 183.31 191.63 182.62 c 191.63 181.93 191.07 181.37 190.38 181.37 c 189.70 181.37 189.13 181.93 189.13 182.62 c B 189.20 182.62 m 189.20 183.31 189.76 183.87 190.45 183.87 c 191.14 183.87 191.70 183.31 191.70 182.62 c 191.70 181.93 191.14 181.37 190.45 181.37 c 189.76 181.37 189.20 181.93 189.20 182.62 c B 189.24 182.62 m 189.24 183.31 189.80 183.87 190.48 183.87 c 191.17 183.87 191.73 183.31 191.73 182.62 c 191.73 181.93 191.17 181.37 190.48 181.37 c 189.80 181.37 189.24 181.93 189.24 182.62 c B 189.24 182.62 m 189.24 183.31 189.81 183.87 190.49 183.87 c 191.18 183.87 191.74 183.31 191.74 182.62 c 191.74 181.93 191.18 181.37 190.49 181.37 c 189.81 181.37 189.24 181.93 189.24 182.62 c B 189.26 182.62 m 189.26 183.31 189.82 183.87 190.51 183.87 c 191.19 183.87 191.76 183.31 191.76 182.62 c 191.76 181.93 191.19 181.37 190.51 181.37 c 189.82 181.37 189.26 181.93 189.26 182.62 c B 189.31 182.62 m 189.31 183.31 189.87 183.87 190.56 183.87 c 191.25 183.87 191.81 183.31 191.81 182.62 c 191.81 181.93 191.25 181.37 190.56 181.37 c 189.87 181.37 189.31 181.93 189.31 182.62 c B 189.32 182.62 m 189.32 183.31 189.88 183.87 190.56 183.87 c 191.25 183.87 191.81 183.31 191.81 182.62 c 191.81 181.93 191.25 181.37 190.56 181.37 c 189.88 181.37 189.32 181.93 189.32 182.62 c B 189.40 182.62 m 189.40 183.31 189.96 183.87 190.65 183.87 c 191.33 183.87 191.90 183.31 191.90 182.62 c 191.90 181.93 191.33 181.37 190.65 181.37 c 189.96 181.37 189.40 181.93 189.40 182.62 c B 189.70 182.62 m 189.70 183.31 190.26 183.87 190.95 183.87 c 191.63 183.87 192.20 183.31 192.20 182.62 c 192.20 181.93 191.63 181.37 190.95 181.37 c 190.26 181.37 189.70 181.93 189.70 182.62 c B 189.72 182.62 m 189.72 183.31 190.28 183.87 190.97 183.87 c 191.66 183.87 192.22 183.31 192.22 182.62 c 192.22 181.93 191.66 181.37 190.97 181.37 c 190.28 181.37 189.72 181.93 189.72 182.62 c B 189.76 182.62 m 189.76 183.31 190.32 183.87 191.00 183.87 c 191.69 183.87 192.25 183.31 192.25 182.62 c 192.25 181.93 191.69 181.37 191.00 181.37 c 190.32 181.37 189.76 181.93 189.76 182.62 c B 189.78 182.62 m 189.78 183.31 190.34 183.87 191.03 183.87 c 191.71 183.87 192.28 183.31 192.28 182.62 c 192.28 181.93 191.71 181.37 191.03 181.37 c 190.34 181.37 189.78 181.93 189.78 182.62 c B 189.82 182.62 m 189.82 183.31 190.39 183.87 191.07 183.87 c 191.76 183.87 192.32 183.31 192.32 182.62 c 192.32 181.93 191.76 181.37 191.07 181.37 c 190.39 181.37 189.82 181.93 189.82 182.62 c B 189.86 182.62 m 189.86 183.31 190.42 183.87 191.11 183.87 c 191.79 183.87 192.35 183.31 192.35 182.62 c 192.35 181.93 191.79 181.37 191.11 181.37 c 190.42 181.37 189.86 181.93 189.86 182.62 c B 189.92 182.62 m 189.92 183.31 190.48 183.87 191.16 183.87 c 191.85 183.87 192.41 183.31 192.41 182.62 c 192.41 181.93 191.85 181.37 191.16 181.37 c 190.48 181.37 189.92 181.93 189.92 182.62 c B 190.09 182.62 m 190.09 183.31 190.65 183.87 191.33 183.87 c 192.02 183.87 192.58 183.31 192.58 182.62 c 192.58 181.93 192.02 181.37 191.33 181.37 c 190.65 181.37 190.09 181.93 190.09 182.62 c B 190.09 182.62 m 190.09 183.31 190.65 183.87 191.34 183.87 c 192.02 183.87 192.59 183.31 192.59 182.62 c 192.59 181.93 192.02 181.37 191.34 181.37 c 190.65 181.37 190.09 181.93 190.09 182.62 c B 190.14 182.62 m 190.14 183.31 190.70 183.87 191.38 183.87 c 192.07 183.87 192.63 183.31 192.63 182.62 c 192.63 181.93 192.07 181.37 191.38 181.37 c 190.70 181.37 190.14 181.93 190.14 182.62 c B 190.20 182.62 m 190.20 183.31 190.76 183.87 191.44 183.87 c 192.13 183.87 192.69 183.31 192.69 182.62 c 192.69 181.93 192.13 181.37 191.44 181.37 c 190.76 181.37 190.20 181.93 190.20 182.62 c B 190.25 182.62 m 190.25 183.31 190.81 183.87 191.50 183.87 c 192.18 183.87 192.75 183.31 192.75 182.62 c 192.75 181.93 192.18 181.37 191.50 181.37 c 190.81 181.37 190.25 181.93 190.25 182.62 c B 190.26 182.62 m 190.26 183.31 190.82 183.87 191.50 183.87 c 192.19 183.87 192.75 183.31 192.75 182.62 c 192.75 181.93 192.19 181.37 191.50 181.37 c 190.82 181.37 190.26 181.93 190.26 182.62 c B 190.27 182.62 m 190.27 183.31 190.83 183.87 191.52 183.87 c 192.20 183.87 192.76 183.31 192.76 182.62 c 192.76 181.93 192.20 181.37 191.52 181.37 c 190.83 181.37 190.27 181.93 190.27 182.62 c B 190.30 182.62 m 190.30 183.31 190.86 183.87 191.55 183.87 c 192.23 183.87 192.79 183.31 192.79 182.62 c 192.79 181.93 192.23 181.37 191.55 181.37 c 190.86 181.37 190.30 181.93 190.30 182.62 c B 190.33 182.62 m 190.33 183.31 190.89 183.87 191.57 183.87 c 192.26 183.87 192.82 183.31 192.82 182.62 c 192.82 181.93 192.26 181.37 191.57 181.37 c 190.89 181.37 190.33 181.93 190.33 182.62 c B 190.42 182.62 m 190.42 183.31 190.99 183.87 191.67 183.87 c 192.36 183.87 192.92 183.31 192.92 182.62 c 192.92 181.93 192.36 181.37 191.67 181.37 c 190.99 181.37 190.42 181.93 190.42 182.62 c B 190.45 182.62 m 190.45 183.31 191.01 183.87 191.70 183.87 c 192.38 183.87 192.94 183.31 192.94 182.62 c 192.94 181.93 192.38 181.37 191.70 181.37 c 191.01 181.37 190.45 181.93 190.45 182.62 c B 190.57 182.62 m 190.57 183.31 191.13 183.87 191.82 183.87 c 192.50 183.87 193.06 183.31 193.06 182.62 c 193.06 181.93 192.50 181.37 191.82 181.37 c 191.13 181.37 190.57 181.93 190.57 182.62 c B 190.70 182.62 m 190.70 183.31 191.26 183.87 191.95 183.87 c 192.63 183.87 193.19 183.31 193.19 182.62 c 193.19 181.93 192.63 181.37 191.95 181.37 c 191.26 181.37 190.70 181.93 190.70 182.62 c B 190.76 182.62 m 190.76 183.31 191.32 183.87 192.01 183.87 c 192.70 183.87 193.26 183.31 193.26 182.62 c 193.26 181.93 192.70 181.37 192.01 181.37 c 191.32 181.37 190.76 181.93 190.76 182.62 c B 190.79 182.62 m 190.79 183.31 191.35 183.87 192.04 183.87 c 192.72 183.87 193.28 183.31 193.28 182.62 c 193.28 181.93 192.72 181.37 192.04 181.37 c 191.35 181.37 190.79 181.93 190.79 182.62 c B 190.80 182.62 m 190.80 183.31 191.36 183.87 192.04 183.87 c 192.73 183.87 193.29 183.31 193.29 182.62 c 193.29 181.93 192.73 181.37 192.04 181.37 c 191.36 181.37 190.80 181.93 190.80 182.62 c B 190.85 182.62 m 190.85 183.31 191.41 183.87 192.10 183.87 c 192.79 183.87 193.35 183.31 193.35 182.62 c 193.35 181.93 192.79 181.37 192.10 181.37 c 191.41 181.37 190.85 181.93 190.85 182.62 c B 191.02 182.62 m 191.02 183.31 191.58 183.87 192.26 183.87 c 192.95 183.87 193.51 183.31 193.51 182.62 c 193.51 181.93 192.95 181.37 192.26 181.37 c 191.58 181.37 191.02 181.93 191.02 182.62 c B 191.25 182.62 m 191.25 183.31 191.81 183.87 192.49 183.87 c 193.18 183.87 193.74 183.31 193.74 182.62 c 193.74 181.93 193.18 181.37 192.49 181.37 c 191.81 181.37 191.25 181.93 191.25 182.62 c B 191.25 182.62 m 191.25 183.31 191.82 183.87 192.50 183.87 c 193.19 183.87 193.75 183.31 193.75 182.62 c 193.75 181.93 193.19 181.37 192.50 181.37 c 191.82 181.37 191.25 181.93 191.25 182.62 c B 191.31 182.62 m 191.31 183.31 191.87 183.87 192.55 183.87 c 193.24 183.87 193.80 183.31 193.80 182.62 c 193.80 181.93 193.24 181.37 192.55 181.37 c 191.87 181.37 191.31 181.93 191.31 182.62 c B 191.31 182.62 m 191.31 183.31 191.87 183.87 192.56 183.87 c 193.24 183.87 193.80 183.31 193.80 182.62 c 193.80 181.93 193.24 181.37 192.56 181.37 c 191.87 181.37 191.31 181.93 191.31 182.62 c B 191.36 182.62 m 191.36 183.31 191.92 183.87 192.61 183.87 c 193.29 183.87 193.85 183.31 193.85 182.62 c 193.85 181.93 193.29 181.37 192.61 181.37 c 191.92 181.37 191.36 181.93 191.36 182.62 c B 191.36 182.62 m 191.36 183.31 191.93 183.87 192.61 183.87 c 193.30 183.87 193.86 183.31 193.86 182.62 c 193.86 181.93 193.30 181.37 192.61 181.37 c 191.93 181.37 191.36 181.93 191.36 182.62 c B 191.58 182.62 m 191.58 183.31 192.15 183.87 192.83 183.87 c 193.52 183.87 194.08 183.31 194.08 182.62 c 194.08 181.93 193.52 181.37 192.83 181.37 c 192.15 181.37 191.58 181.93 191.58 182.62 c B 191.60 182.62 m 191.60 183.31 192.16 183.87 192.85 183.87 c 193.53 183.87 194.10 183.31 194.10 182.62 c 194.10 181.93 193.53 181.37 192.85 181.37 c 192.16 181.37 191.60 181.93 191.60 182.62 c B 191.64 182.62 m 191.64 183.31 192.20 183.87 192.89 183.87 c 193.57 183.87 194.13 183.31 194.13 182.62 c 194.13 181.93 193.57 181.37 192.89 181.37 c 192.20 181.37 191.64 181.93 191.64 182.62 c B 191.67 182.62 m 191.67 183.31 192.23 183.87 192.92 183.87 c 193.60 183.87 194.16 183.31 194.16 182.62 c 194.16 181.93 193.60 181.37 192.92 181.37 c 192.23 181.37 191.67 181.93 191.67 182.62 c B 191.78 182.62 m 191.78 183.31 192.34 183.87 193.03 183.87 c 193.71 183.87 194.27 183.31 194.27 182.62 c 194.27 181.93 193.71 181.37 193.03 181.37 c 192.34 181.37 191.78 181.93 191.78 182.62 c B 192.08 182.62 m 192.08 183.31 192.64 183.87 193.33 183.87 c 194.01 183.87 194.57 183.31 194.57 182.62 c 194.57 181.93 194.01 181.37 193.33 181.37 c 192.64 181.37 192.08 181.93 192.08 182.62 c B 192.23 182.62 m 192.23 183.31 192.79 183.87 193.48 183.87 c 194.16 183.87 194.72 183.31 194.72 182.62 c 194.72 181.93 194.16 181.37 193.48 181.37 c 192.79 181.37 192.23 181.93 192.23 182.62 c B 192.28 183.41 m 192.28 184.10 192.84 184.66 193.53 184.66 c 194.21 184.66 194.77 184.10 194.77 183.41 c 194.77 182.73 194.21 182.17 193.53 182.17 c 192.84 182.17 192.28 182.73 192.28 183.41 c B 192.41 183.50 m 192.41 184.18 192.97 184.74 193.65 184.74 c 194.34 184.74 194.90 184.18 194.90 183.50 c 194.90 182.81 194.34 182.25 193.65 182.25 c 192.97 182.25 192.41 182.81 192.41 183.50 c B 192.44 184.09 m 192.44 184.78 193.00 185.34 193.69 185.34 c 194.37 185.34 194.93 184.78 194.93 184.09 c 194.93 183.40 194.37 182.84 193.69 182.84 c 193.00 182.84 192.44 183.40 192.44 184.09 c B 192.60 184.15 m 192.60 184.84 193.17 185.40 193.85 185.40 c 194.54 185.40 195.10 184.84 195.10 184.15 c 195.10 183.46 194.54 182.90 193.85 182.90 c 193.17 182.90 192.60 183.46 192.60 184.15 c B 192.61 184.38 m 192.61 185.07 193.17 185.63 193.86 185.63 c 194.54 185.63 195.10 185.07 195.10 184.38 c 195.10 183.70 194.54 183.14 193.86 183.14 c 193.17 183.14 192.61 183.70 192.61 184.38 c B 192.66 184.38 m 192.66 185.07 193.22 185.63 193.91 185.63 c 194.59 185.63 195.15 185.07 195.15 184.38 c 195.15 183.70 194.59 183.14 193.91 183.14 c 193.22 183.14 192.66 183.70 192.66 184.38 c B 192.73 184.40 m 192.73 185.09 193.29 185.65 193.98 185.65 c 194.66 185.65 195.23 185.09 195.23 184.40 c 195.23 183.72 194.66 183.16 193.98 183.16 c 193.29 183.16 192.73 183.72 192.73 184.40 c B 192.85 184.92 m 192.85 185.61 193.41 186.17 194.09 186.17 c 194.78 186.17 195.34 185.61 195.34 184.92 c 195.34 184.24 194.78 183.68 194.09 183.68 c 193.41 183.68 192.85 184.24 192.85 184.92 c B 193.14 185.24 m 193.14 185.92 193.70 186.49 194.38 186.49 c 195.07 186.49 195.63 185.92 195.63 185.24 c 195.63 184.55 195.07 183.99 194.38 183.99 c 193.70 183.99 193.14 184.55 193.14 185.24 c B 193.50 185.68 m 193.50 186.37 194.06 186.93 194.74 186.93 c 195.43 186.93 195.99 186.37 195.99 185.68 c 195.99 184.99 195.43 184.43 194.74 184.43 c 194.06 184.43 193.50 184.99 193.50 185.68 c B 194.14 186.06 m 194.14 186.75 194.70 187.31 195.39 187.31 c 196.07 187.31 196.64 186.75 196.64 186.06 c 196.64 185.37 196.07 184.81 195.39 184.81 c 194.70 184.81 194.14 185.37 194.14 186.06 c B 194.51 186.24 m 194.51 186.92 195.07 187.48 195.76 187.48 c 196.45 187.48 197.01 186.92 197.01 186.24 c 197.01 185.55 196.45 184.99 195.76 184.99 c 195.07 184.99 194.51 185.55 194.51 186.24 c B 194.71 186.44 m 194.71 187.13 195.27 187.69 195.96 187.69 c 196.64 187.69 197.20 187.13 197.20 186.44 c 197.20 185.76 196.64 185.19 195.96 185.19 c 195.27 185.19 194.71 185.76 194.71 186.44 c B 194.75 186.44 m 194.75 187.13 195.31 187.69 196.00 187.69 c 196.68 187.69 197.24 187.13 197.24 186.44 c 197.24 185.76 196.68 185.19 196.00 185.19 c 195.31 185.19 194.75 185.76 194.75 186.44 c B 194.79 186.53 m 194.79 187.22 195.35 187.78 196.04 187.78 c 196.72 187.78 197.28 187.22 197.28 186.53 c 197.28 185.84 196.72 185.28 196.04 185.28 c 195.35 185.28 194.79 185.84 194.79 186.53 c B 195.14 186.56 m 195.14 187.24 195.70 187.80 196.38 187.80 c 197.07 187.80 197.63 187.24 197.63 186.56 c 197.63 185.87 197.07 185.31 196.38 185.31 c 195.70 185.31 195.14 185.87 195.14 186.56 c B 195.30 186.73 m 195.30 187.42 195.86 187.98 196.55 187.98 c 197.23 187.98 197.79 187.42 197.79 186.73 c 197.79 186.04 197.23 185.48 196.55 185.48 c 195.86 185.48 195.30 186.04 195.30 186.73 c B 195.71 187.32 m 195.71 188.01 196.27 188.57 196.96 188.57 c 197.64 188.57 198.21 188.01 198.21 187.32 c 198.21 186.64 197.64 186.07 196.96 186.07 c 196.27 186.07 195.71 186.64 195.71 187.32 c B 196.16 188.27 m 196.16 188.95 196.72 189.51 197.40 189.51 c 198.09 189.51 198.65 188.95 198.65 188.27 c 198.65 187.58 198.09 187.02 197.40 187.02 c 196.72 187.02 196.16 187.58 196.16 188.27 c B 196.29 189.11 m 196.29 189.79 196.85 190.35 197.53 190.35 c 198.22 190.35 198.78 189.79 198.78 189.11 c 198.78 188.42 198.22 187.86 197.53 187.86 c 196.85 187.86 196.29 188.42 196.29 189.11 c B 196.46 189.17 m 196.46 189.85 197.02 190.41 197.70 190.41 c 198.39 190.41 198.95 189.85 198.95 189.17 c 198.95 188.48 198.39 187.92 197.70 187.92 c 197.02 187.92 196.46 188.48 196.46 189.17 c B 196.71 189.17 m 196.71 189.85 197.28 190.41 197.96 190.41 c 198.65 190.41 199.21 189.85 199.21 189.17 c 199.21 188.48 198.65 187.92 197.96 187.92 c 197.28 187.92 196.71 188.48 196.71 189.17 c B 196.79 189.18 m 196.79 189.87 197.36 190.43 198.04 190.43 c 198.73 190.43 199.29 189.87 199.29 189.18 c 199.29 188.49 198.73 187.93 198.04 187.93 c 197.36 187.93 196.79 188.49 196.79 189.18 c B 197.13 189.97 m 197.13 190.66 197.69 191.22 198.38 191.22 c 199.07 191.22 199.63 190.66 199.63 189.97 c 199.63 189.28 199.07 188.72 198.38 188.72 c 197.69 188.72 197.13 189.28 197.13 189.97 c B 198.33 190.20 m 198.33 190.89 198.89 191.45 199.58 191.45 c 200.26 191.45 200.83 190.89 200.83 190.20 c 200.83 189.51 200.26 188.95 199.58 188.95 c 198.89 188.95 198.33 189.51 198.33 190.20 c B 198.39 191.55 m 198.39 192.23 198.96 192.79 199.64 192.79 c 200.33 192.79 200.89 192.23 200.89 191.55 c 200.89 190.86 200.33 190.30 199.64 190.30 c 198.96 190.30 198.39 190.86 198.39 191.55 c B 198.69 192.29 m 198.69 192.98 199.25 193.54 199.94 193.54 c 200.62 193.54 201.18 192.98 201.18 192.29 c 201.18 191.61 200.62 191.05 199.94 191.05 c 199.25 191.05 198.69 191.61 198.69 192.29 c B 199.75 192.44 m 199.75 193.13 200.31 193.69 201.00 193.69 c 201.69 193.69 202.25 193.13 202.25 192.44 c 202.25 191.75 201.69 191.19 201.00 191.19 c 200.31 191.19 199.75 191.75 199.75 192.44 c B 199.88 192.62 m 199.88 193.30 200.44 193.86 201.13 193.86 c 201.81 193.86 202.37 193.30 202.37 192.62 c 202.37 191.93 201.81 191.37 201.13 191.37 c 200.44 191.37 199.88 191.93 199.88 192.62 c B 200.04 193.47 m 200.04 194.16 200.61 194.72 201.29 194.72 c 201.98 194.72 202.54 194.16 202.54 193.47 c 202.54 192.78 201.98 192.22 201.29 192.22 c 200.61 192.22 200.04 192.78 200.04 193.47 c B 200.39 193.80 m 200.39 194.49 200.95 195.05 201.64 195.05 c 202.32 195.05 202.89 194.49 202.89 193.80 c 202.89 193.11 202.32 192.55 201.64 192.55 c 200.95 192.55 200.39 193.11 200.39 193.80 c B 201.14 194.06 m 201.14 194.75 201.70 195.31 202.39 195.31 c 203.07 195.31 203.64 194.75 203.64 194.06 c 203.64 193.38 203.07 192.81 202.39 192.81 c 201.70 192.81 201.14 193.38 201.14 194.06 c B 201.34 194.16 m 201.34 194.85 201.90 195.41 202.59 195.41 c 203.27 195.41 203.83 194.85 203.83 194.16 c 203.83 193.48 203.27 192.92 202.59 192.92 c 201.90 192.92 201.34 193.48 201.34 194.16 c B 201.55 194.68 m 201.55 195.36 202.11 195.92 202.80 195.92 c 203.48 195.92 204.05 195.36 204.05 194.68 c 204.05 193.99 203.48 193.43 202.80 193.43 c 202.11 193.43 201.55 193.99 201.55 194.68 c B 201.61 197.62 m 201.61 198.30 202.17 198.86 202.85 198.86 c 203.54 198.86 204.10 198.30 204.10 197.62 c 204.10 196.93 203.54 196.37 202.85 196.37 c 202.17 196.37 201.61 196.93 201.61 197.62 c B 202.33 197.81 m 202.33 198.50 202.89 199.06 203.57 199.06 c 204.26 199.06 204.82 198.50 204.82 197.81 c 204.82 197.12 204.26 196.56 203.57 196.56 c 202.89 196.56 202.33 197.12 202.33 197.81 c B 204.89 198.95 m 204.89 199.63 205.45 200.19 206.13 200.19 c 206.82 200.19 207.38 199.63 207.38 198.95 c 207.38 198.26 206.82 197.70 206.13 197.70 c 205.45 197.70 204.89 198.26 204.89 198.95 c B 205.00 199.35 m 205.00 200.04 205.56 200.60 206.24 200.60 c 206.93 200.60 207.49 200.04 207.49 199.35 c 207.49 198.66 206.93 198.10 206.24 198.10 c 205.56 198.10 205.00 198.66 205.00 199.35 c B 205.39 199.36 m 205.39 200.04 205.96 200.61 206.64 200.61 c 207.33 200.61 207.89 200.04 207.89 199.36 c 207.89 198.67 207.33 198.11 206.64 198.11 c 205.96 198.11 205.39 198.67 205.39 199.36 c B 206.44 200.00 m 206.44 200.69 207.00 201.25 207.69 201.25 c 208.37 201.25 208.93 200.69 208.93 200.00 c 208.93 199.32 208.37 198.76 207.69 198.76 c 207.00 198.76 206.44 199.32 206.44 200.00 c B 206.59 201.73 m 206.59 202.42 207.15 202.98 207.83 202.98 c 208.52 202.98 209.08 202.42 209.08 201.73 c 209.08 201.05 208.52 200.48 207.83 200.48 c 207.15 200.48 206.59 201.05 206.59 201.73 c B 207.29 202.03 m 207.29 202.71 207.86 203.27 208.54 203.27 c 209.23 203.27 209.79 202.71 209.79 202.03 c 209.79 201.34 209.23 200.78 208.54 200.78 c 207.86 200.78 207.29 201.34 207.29 202.03 c B 207.45 203.49 m 207.45 204.18 208.02 204.74 208.70 204.74 c 209.39 204.74 209.95 204.18 209.95 203.49 c 209.95 202.80 209.39 202.24 208.70 202.24 c 208.02 202.24 207.45 202.80 207.45 203.49 c B 208.28 204.48 m 208.28 205.17 208.84 205.73 209.52 205.73 c 210.21 205.73 210.77 205.17 210.77 204.48 c 210.77 203.80 210.21 203.24 209.52 203.24 c 208.84 203.24 208.28 203.80 208.28 204.48 c B 208.58 204.67 m 208.58 205.35 209.14 205.91 209.83 205.91 c 210.51 205.91 211.08 205.35 211.08 204.67 c 211.08 203.98 210.51 203.42 209.83 203.42 c 209.14 203.42 208.58 203.98 208.58 204.67 c B 209.91 206.88 m 209.91 207.56 210.47 208.12 211.15 208.12 c 211.84 208.12 212.40 207.56 212.40 206.88 c 212.40 206.19 211.84 205.63 211.15 205.63 c 210.47 205.63 209.91 206.19 209.91 206.88 c B 213.29 207.11 m 213.29 207.79 213.85 208.36 214.53 208.36 c 215.22 208.36 215.78 207.79 215.78 207.11 c 215.78 206.42 215.22 205.86 214.53 205.86 c 213.85 205.86 213.29 206.42 213.29 207.11 c B 213.90 207.17 m 213.90 207.85 214.47 208.42 215.15 208.42 c 215.84 208.42 216.40 207.85 216.40 207.17 c 216.40 206.48 215.84 205.92 215.15 205.92 c 214.47 205.92 213.90 206.48 213.90 207.17 c B 216.56 208.35 m 216.56 209.03 217.12 209.60 217.81 209.60 c 218.50 209.60 219.06 209.03 219.06 208.35 c 219.06 207.66 218.50 207.10 217.81 207.10 c 217.12 207.10 216.56 207.66 216.56 208.35 c B 217.78 208.76 m 217.78 209.45 218.34 210.01 219.03 210.01 c 219.71 210.01 220.28 209.45 220.28 208.76 c 220.28 208.08 219.71 207.52 219.03 207.52 c 218.34 207.52 217.78 208.08 217.78 208.76 c B 220.93 211.72 m 220.93 212.41 221.49 212.97 222.18 212.97 c 222.86 212.97 223.42 212.41 223.42 211.72 c 223.42 211.04 222.86 210.47 222.18 210.47 c 221.49 210.47 220.93 211.04 220.93 211.72 c B 224.47 218.79 m 224.47 219.47 225.03 220.03 225.72 220.03 c 226.40 220.03 226.96 219.47 226.96 218.79 c 226.96 218.10 226.40 217.54 225.72 217.54 c 225.03 217.54 224.47 218.10 224.47 218.79 c B 236.09 228.86 m 236.09 229.55 236.65 230.11 237.34 230.11 c 238.02 230.11 238.58 229.55 238.58 228.86 c 238.58 228.18 238.02 227.61 237.34 227.61 c 236.65 227.61 236.09 228.18 236.09 228.86 c B 244.89 245.11 m 244.89 245.79 245.45 246.36 246.14 246.36 c 246.83 246.36 247.39 245.79 247.39 245.11 c 247.39 244.42 246.83 243.86 246.14 243.86 c 245.45 243.86 244.89 244.42 244.89 245.11 c B Q q 182.50 175.37 91.25 76.99 re W n Q q 187.25 180.12 81.74 67.49 re W n /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 187.25 180.12 m 268.99 247.61 l S 0.75 w [ 3.00 5.00] 0 d 187.25 186.37 m 268.99 253.86 l S 187.25 173.87 m 268.99 241.36 l S Q q /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 187.25 180.12 m 268.99 180.12 l 268.99 247.61 l 187.25 247.61 l 187.25 180.12 l S Q q 4.75 103.13 81.74 67.49 re W n Q q 0.00 98.38 91.25 76.99 re W n BT /GS258 gs 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 35.96 67.01 Tm (Index) Tj ET Q q 4.75 103.13 81.74 67.49 re W n BT /GS258 gs 0.000 0.000 0.000 rg /F2 1 Tf 11.00 0.00 -0.00 11.00 15.67 133.96 Tm (I\(re75/1000\)) Tj ET Q q 96.00 103.13 81.74 67.49 re W n Q q 96.00 103.13 81.74 67.49 re W n /GS257 gs 0.000 0.000 1.000 rg /GS1 gs 0.000 0.000 1.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.78 105.63 m 97.78 106.31 98.34 106.87 99.03 106.87 c 99.71 106.87 100.27 106.31 100.27 105.63 c 100.27 104.94 99.71 104.38 99.03 104.38 c 98.34 104.38 97.78 104.94 97.78 105.63 c B 97.81 105.63 m 97.81 106.31 98.37 106.87 99.06 106.87 c 99.75 106.87 100.31 106.31 100.31 105.63 c 100.31 104.94 99.75 104.38 99.06 104.38 c 98.37 104.38 97.81 104.94 97.81 105.63 c B 97.91 105.63 m 97.91 106.31 98.47 106.87 99.16 106.87 c 99.84 106.87 100.40 106.31 100.40 105.63 c 100.40 104.94 99.84 104.38 99.16 104.38 c 98.47 104.38 97.91 104.94 97.91 105.63 c B 97.95 105.63 m 97.95 106.31 98.51 106.87 99.20 106.87 c 99.89 106.87 100.45 106.31 100.45 105.63 c 100.45 104.94 99.89 104.38 99.20 104.38 c 98.51 104.38 97.95 104.94 97.95 105.63 c B 98.13 105.63 m 98.13 106.31 98.69 106.87 99.37 106.87 c 100.06 106.87 100.62 106.31 100.62 105.63 c 100.62 104.94 100.06 104.38 99.37 104.38 c 98.69 104.38 98.13 104.94 98.13 105.63 c B 98.24 105.63 m 98.24 106.31 98.80 106.87 99.49 106.87 c 100.17 106.87 100.73 106.31 100.73 105.63 c 100.73 104.94 100.17 104.38 99.49 104.38 c 98.80 104.38 98.24 104.94 98.24 105.63 c B 98.28 105.63 m 98.28 106.31 98.84 106.87 99.53 106.87 c 100.21 106.87 100.78 106.31 100.78 105.63 c 100.78 104.94 100.21 104.38 99.53 104.38 c 98.84 104.38 98.28 104.94 98.28 105.63 c B 98.33 105.63 m 98.33 106.31 98.89 106.87 99.58 106.87 c 100.26 106.87 100.82 106.31 100.82 105.63 c 100.82 104.94 100.26 104.38 99.58 104.38 c 98.89 104.38 98.33 104.94 98.33 105.63 c B 98.40 105.63 m 98.40 106.31 98.96 106.87 99.65 106.87 c 100.33 106.87 100.89 106.31 100.89 105.63 c 100.89 104.94 100.33 104.38 99.65 104.38 c 98.96 104.38 98.40 104.94 98.40 105.63 c B 98.42 105.63 m 98.42 106.31 98.98 106.87 99.66 106.87 c 100.35 106.87 100.91 106.31 100.91 105.63 c 100.91 104.94 100.35 104.38 99.66 104.38 c 98.98 104.38 98.42 104.94 98.42 105.63 c B 98.44 105.63 m 98.44 106.31 99.00 106.87 99.69 106.87 c 100.37 106.87 100.93 106.31 100.93 105.63 c 100.93 104.94 100.37 104.38 99.69 104.38 c 99.00 104.38 98.44 104.94 98.44 105.63 c B 98.53 105.63 m 98.53 106.31 99.09 106.87 99.78 106.87 c 100.46 106.87 101.02 106.31 101.02 105.63 c 101.02 104.94 100.46 104.38 99.78 104.38 c 99.09 104.38 98.53 104.94 98.53 105.63 c B 98.60 105.63 m 98.60 106.31 99.16 106.87 99.85 106.87 c 100.53 106.87 101.09 106.31 101.09 105.63 c 101.09 104.94 100.53 104.38 99.85 104.38 c 99.16 104.38 98.60 104.94 98.60 105.63 c B 98.66 105.63 m 98.66 106.31 99.23 106.87 99.91 106.87 c 100.60 106.87 101.16 106.31 101.16 105.63 c 101.16 104.94 100.60 104.38 99.91 104.38 c 99.23 104.38 98.66 104.94 98.66 105.63 c B 98.69 105.63 m 98.69 106.31 99.25 106.87 99.93 106.87 c 100.62 106.87 101.18 106.31 101.18 105.63 c 101.18 104.94 100.62 104.38 99.93 104.38 c 99.25 104.38 98.69 104.94 98.69 105.63 c B 98.70 105.63 m 98.70 106.31 99.26 106.87 99.95 106.87 c 100.64 106.87 101.20 106.31 101.20 105.63 c 101.20 104.94 100.64 104.38 99.95 104.38 c 99.26 104.38 98.70 104.94 98.70 105.63 c B 98.74 105.63 m 98.74 106.31 99.31 106.87 99.99 106.87 c 100.68 106.87 101.24 106.31 101.24 105.63 c 101.24 104.94 100.68 104.38 99.99 104.38 c 99.31 104.38 98.74 104.94 98.74 105.63 c B 98.95 105.63 m 98.95 106.31 99.51 106.87 100.20 106.87 c 100.88 106.87 101.44 106.31 101.44 105.63 c 101.44 104.94 100.88 104.38 100.20 104.38 c 99.51 104.38 98.95 104.94 98.95 105.63 c B 99.19 105.63 m 99.19 106.31 99.75 106.87 100.44 106.87 c 101.13 106.87 101.69 106.31 101.69 105.63 c 101.69 104.94 101.13 104.38 100.44 104.38 c 99.75 104.38 99.19 104.94 99.19 105.63 c B 99.20 105.63 m 99.20 106.31 99.76 106.87 100.45 106.87 c 101.13 106.87 101.69 106.31 101.69 105.63 c 101.69 104.94 101.13 104.38 100.45 104.38 c 99.76 104.38 99.20 104.94 99.20 105.63 c B 99.36 105.63 m 99.36 106.31 99.92 106.87 100.61 106.87 c 101.29 106.87 101.85 106.31 101.85 105.63 c 101.85 104.94 101.29 104.38 100.61 104.38 c 99.92 104.38 99.36 104.94 99.36 105.63 c B 99.40 105.63 m 99.40 106.31 99.96 106.87 100.64 106.87 c 101.33 106.87 101.89 106.31 101.89 105.63 c 101.89 104.94 101.33 104.38 100.64 104.38 c 99.96 104.38 99.40 104.94 99.40 105.63 c B 99.48 105.63 m 99.48 106.31 100.04 106.87 100.73 106.87 c 101.42 106.87 101.98 106.31 101.98 105.63 c 101.98 104.94 101.42 104.38 100.73 104.38 c 100.04 104.38 99.48 104.94 99.48 105.63 c B 99.57 105.63 m 99.57 106.31 100.13 106.87 100.82 106.87 c 101.50 106.87 102.06 106.31 102.06 105.63 c 102.06 104.94 101.50 104.38 100.82 104.38 c 100.13 104.38 99.57 104.94 99.57 105.63 c B 99.68 105.63 m 99.68 106.31 100.24 106.87 100.92 106.87 c 101.61 106.87 102.17 106.31 102.17 105.63 c 102.17 104.94 101.61 104.38 100.92 104.38 c 100.24 104.38 99.68 104.94 99.68 105.63 c B 99.68 105.63 m 99.68 106.31 100.24 106.87 100.92 106.87 c 101.61 106.87 102.17 106.31 102.17 105.63 c 102.17 104.94 101.61 104.38 100.92 104.38 c 100.24 104.38 99.68 104.94 99.68 105.63 c B 100.04 105.63 m 100.04 106.31 100.61 106.87 101.29 106.87 c 101.98 106.87 102.54 106.31 102.54 105.63 c 102.54 104.94 101.98 104.38 101.29 104.38 c 100.61 104.38 100.04 104.94 100.04 105.63 c B 100.15 105.63 m 100.15 106.31 100.71 106.87 101.39 106.87 c 102.08 106.87 102.64 106.31 102.64 105.63 c 102.64 104.94 102.08 104.38 101.39 104.38 c 100.71 104.38 100.15 104.94 100.15 105.63 c B 100.21 105.63 m 100.21 106.31 100.77 106.87 101.45 106.87 c 102.14 106.87 102.70 106.31 102.70 105.63 c 102.70 104.94 102.14 104.38 101.45 104.38 c 100.77 104.38 100.21 104.94 100.21 105.63 c B 100.37 105.63 m 100.37 106.31 100.93 106.87 101.62 106.87 c 102.31 106.87 102.87 106.31 102.87 105.63 c 102.87 104.94 102.31 104.38 101.62 104.38 c 100.93 104.38 100.37 104.94 100.37 105.63 c B 100.41 105.63 m 100.41 106.31 100.97 106.87 101.66 106.87 c 102.34 106.87 102.91 106.31 102.91 105.63 c 102.91 104.94 102.34 104.38 101.66 104.38 c 100.97 104.38 100.41 104.94 100.41 105.63 c B 100.50 105.63 m 100.50 106.31 101.06 106.87 101.74 106.87 c 102.43 106.87 102.99 106.31 102.99 105.63 c 102.99 104.94 102.43 104.38 101.74 104.38 c 101.06 104.38 100.50 104.94 100.50 105.63 c B 100.67 105.63 m 100.67 106.31 101.24 106.87 101.92 106.87 c 102.61 106.87 103.17 106.31 103.17 105.63 c 103.17 104.94 102.61 104.38 101.92 104.38 c 101.24 104.38 100.67 104.94 100.67 105.63 c B 100.70 105.63 m 100.70 106.31 101.26 106.87 101.94 106.87 c 102.63 106.87 103.19 106.31 103.19 105.63 c 103.19 104.94 102.63 104.38 101.94 104.38 c 101.26 104.38 100.70 104.94 100.70 105.63 c B 100.73 105.63 m 100.73 106.31 101.30 106.87 101.98 106.87 c 102.67 106.87 103.23 106.31 103.23 105.63 c 103.23 104.94 102.67 104.38 101.98 104.38 c 101.30 104.38 100.73 104.94 100.73 105.63 c B 101.05 105.63 m 101.05 106.31 101.61 106.87 102.30 106.87 c 102.99 106.87 103.55 106.31 103.55 105.63 c 103.55 104.94 102.99 104.38 102.30 104.38 c 101.61 104.38 101.05 104.94 101.05 105.63 c B 101.13 105.63 m 101.13 106.31 101.69 106.87 102.38 106.87 c 103.07 106.87 103.63 106.31 103.63 105.63 c 103.63 104.94 103.07 104.38 102.38 104.38 c 101.69 104.38 101.13 104.94 101.13 105.63 c B 101.23 105.63 m 101.23 106.31 101.80 106.87 102.48 106.87 c 103.17 106.87 103.73 106.31 103.73 105.63 c 103.73 104.94 103.17 104.38 102.48 104.38 c 101.80 104.38 101.23 104.94 101.23 105.63 c B 101.34 105.63 m 101.34 106.31 101.90 106.87 102.58 106.87 c 103.27 106.87 103.83 106.31 103.83 105.63 c 103.83 104.94 103.27 104.38 102.58 104.38 c 101.90 104.38 101.34 104.94 101.34 105.63 c B 101.42 105.63 m 101.42 106.31 101.98 106.87 102.67 106.87 c 103.35 106.87 103.91 106.31 103.91 105.63 c 103.91 104.94 103.35 104.38 102.67 104.38 c 101.98 104.38 101.42 104.94 101.42 105.63 c B 101.59 105.63 m 101.59 106.31 102.15 106.87 102.84 106.87 c 103.52 106.87 104.09 106.31 104.09 105.63 c 104.09 104.94 103.52 104.38 102.84 104.38 c 102.15 104.38 101.59 104.94 101.59 105.63 c B 101.64 105.63 m 101.64 106.31 102.21 106.87 102.89 106.87 c 103.58 106.87 104.14 106.31 104.14 105.63 c 104.14 104.94 103.58 104.38 102.89 104.38 c 102.21 104.38 101.64 104.94 101.64 105.63 c B 101.76 105.63 m 101.76 106.31 102.32 106.87 103.01 106.87 c 103.70 106.87 104.26 106.31 104.26 105.63 c 104.26 104.94 103.70 104.38 103.01 104.38 c 102.32 104.38 101.76 104.94 101.76 105.63 c B 101.82 105.63 m 101.82 106.31 102.38 106.87 103.07 106.87 c 103.76 106.87 104.32 106.31 104.32 105.63 c 104.32 104.94 103.76 104.38 103.07 104.38 c 102.38 104.38 101.82 104.94 101.82 105.63 c B 101.93 105.63 m 101.93 106.31 102.49 106.87 103.18 106.87 c 103.86 106.87 104.42 106.31 104.42 105.63 c 104.42 104.94 103.86 104.38 103.18 104.38 c 102.49 104.38 101.93 104.94 101.93 105.63 c B 101.98 105.63 m 101.98 106.31 102.54 106.87 103.23 106.87 c 103.91 106.87 104.47 106.31 104.47 105.63 c 104.47 104.94 103.91 104.38 103.23 104.38 c 102.54 104.38 101.98 104.94 101.98 105.63 c B 102.10 105.63 m 102.10 106.31 102.66 106.87 103.34 106.87 c 104.03 106.87 104.59 106.31 104.59 105.63 c 104.59 104.94 104.03 104.38 103.34 104.38 c 102.66 104.38 102.10 104.94 102.10 105.63 c B 102.16 105.63 m 102.16 106.31 102.72 106.87 103.40 106.87 c 104.09 106.87 104.65 106.31 104.65 105.63 c 104.65 104.94 104.09 104.38 103.40 104.38 c 102.72 104.38 102.16 104.94 102.16 105.63 c B 102.27 105.63 m 102.27 106.31 102.83 106.87 103.52 106.87 c 104.20 106.87 104.76 106.31 104.76 105.63 c 104.76 104.94 104.20 104.38 103.52 104.38 c 102.83 104.38 102.27 104.94 102.27 105.63 c B 102.37 105.63 m 102.37 106.31 102.93 106.87 103.62 106.87 c 104.31 106.87 104.87 106.31 104.87 105.63 c 104.87 104.94 104.31 104.38 103.62 104.38 c 102.93 104.38 102.37 104.94 102.37 105.63 c B 102.44 105.63 m 102.44 106.31 103.00 106.87 103.68 106.87 c 104.37 106.87 104.93 106.31 104.93 105.63 c 104.93 104.94 104.37 104.38 103.68 104.38 c 103.00 104.38 102.44 104.94 102.44 105.63 c B 102.84 105.63 m 102.84 106.31 103.40 106.87 104.08 106.87 c 104.77 106.87 105.33 106.31 105.33 105.63 c 105.33 104.94 104.77 104.38 104.08 104.38 c 103.40 104.38 102.84 104.94 102.84 105.63 c B 102.93 105.63 m 102.93 106.31 103.49 106.87 104.17 106.87 c 104.86 106.87 105.42 106.31 105.42 105.63 c 105.42 104.94 104.86 104.38 104.17 104.38 c 103.49 104.38 102.93 104.94 102.93 105.63 c B 103.06 105.63 m 103.06 106.31 103.62 106.87 104.31 106.87 c 105.00 106.87 105.56 106.31 105.56 105.63 c 105.56 104.94 105.00 104.38 104.31 104.38 c 103.62 104.38 103.06 104.94 103.06 105.63 c B 103.47 105.81 m 103.47 106.50 104.03 107.06 104.71 107.06 c 105.40 107.06 105.96 106.50 105.96 105.81 c 105.96 105.13 105.40 104.56 104.71 104.56 c 104.03 104.56 103.47 105.13 103.47 105.81 c B 103.50 106.04 m 103.50 106.72 104.07 107.29 104.75 107.29 c 105.44 107.29 106.00 106.72 106.00 106.04 c 106.00 105.35 105.44 104.79 104.75 104.79 c 104.07 104.79 103.50 105.35 103.50 106.04 c B 103.73 106.16 m 103.73 106.85 104.29 107.41 104.98 107.41 c 105.66 107.41 106.23 106.85 106.23 106.16 c 106.23 105.47 105.66 104.91 104.98 104.91 c 104.29 104.91 103.73 105.47 103.73 106.16 c B 104.05 106.46 m 104.05 107.14 104.61 107.71 105.30 107.71 c 105.98 107.71 106.54 107.14 106.54 106.46 c 106.54 105.77 105.98 105.21 105.30 105.21 c 104.61 105.21 104.05 105.77 104.05 106.46 c B 104.41 106.46 m 104.41 107.14 104.98 107.71 105.66 107.71 c 106.35 107.71 106.91 107.14 106.91 106.46 c 106.91 105.77 106.35 105.21 105.66 105.21 c 104.98 105.21 104.41 105.77 104.41 106.46 c B 104.52 106.52 m 104.52 107.20 105.08 107.76 105.77 107.76 c 106.46 107.76 107.02 107.20 107.02 106.52 c 107.02 105.83 106.46 105.27 105.77 105.27 c 105.08 105.27 104.52 105.83 104.52 106.52 c B 104.66 106.57 m 104.66 107.25 105.22 107.81 105.91 107.81 c 106.60 107.81 107.16 107.25 107.16 106.57 c 107.16 105.88 106.60 105.32 105.91 105.32 c 105.22 105.32 104.66 105.88 104.66 106.57 c B 105.00 106.59 m 105.00 107.27 105.56 107.83 106.25 107.83 c 106.94 107.83 107.50 107.27 107.50 106.59 c 107.50 105.90 106.94 105.34 106.25 105.34 c 105.56 105.34 105.00 105.90 105.00 106.59 c B 105.17 106.87 m 105.17 107.56 105.74 108.12 106.42 108.12 c 107.11 108.12 107.67 107.56 107.67 106.87 c 107.67 106.19 107.11 105.63 106.42 105.63 c 105.74 105.63 105.17 106.19 105.17 106.87 c B 105.41 107.32 m 105.41 108.00 105.97 108.56 106.66 108.56 c 107.35 108.56 107.91 108.00 107.91 107.32 c 107.91 106.63 107.35 106.07 106.66 106.07 c 105.97 106.07 105.41 106.63 105.41 107.32 c B 105.50 107.61 m 105.50 108.30 106.06 108.86 106.75 108.86 c 107.44 108.86 108.00 108.30 108.00 107.61 c 108.00 106.93 107.44 106.37 106.75 106.37 c 106.06 106.37 105.50 106.93 105.50 107.61 c B 105.83 107.61 m 105.83 108.30 106.39 108.86 107.08 108.86 c 107.77 108.86 108.33 108.30 108.33 107.61 c 108.33 106.93 107.77 106.37 107.08 106.37 c 106.39 106.37 105.83 106.93 105.83 107.61 c B 106.12 107.75 m 106.12 108.44 106.68 109.00 107.37 109.00 c 108.06 109.00 108.62 108.44 108.62 107.75 c 108.62 107.06 108.06 106.50 107.37 106.50 c 106.68 106.50 106.12 107.06 106.12 107.75 c B 106.31 107.91 m 106.31 108.60 106.87 109.16 107.55 109.16 c 108.24 109.16 108.80 108.60 108.80 107.91 c 108.80 107.23 108.24 106.67 107.55 106.67 c 106.87 106.67 106.31 107.23 106.31 107.91 c B 106.43 107.95 m 106.43 108.64 106.99 109.20 107.68 109.20 c 108.36 109.20 108.93 108.64 108.93 107.95 c 108.93 107.26 108.36 106.70 107.68 106.70 c 106.99 106.70 106.43 107.26 106.43 107.95 c B 106.72 107.95 m 106.72 108.64 107.28 109.20 107.96 109.20 c 108.65 109.20 109.21 108.64 109.21 107.95 c 109.21 107.27 108.65 106.71 107.96 106.71 c 107.28 106.71 106.72 107.27 106.72 107.95 c B 106.97 107.95 m 106.97 108.64 107.54 109.20 108.22 109.20 c 108.91 109.20 109.47 108.64 109.47 107.95 c 109.47 107.27 108.91 106.71 108.22 106.71 c 107.54 106.71 106.97 107.27 106.97 107.95 c B 107.25 108.40 m 107.25 109.09 107.81 109.65 108.50 109.65 c 109.18 109.65 109.74 109.09 109.74 108.40 c 109.74 107.72 109.18 107.16 108.50 107.16 c 107.81 107.16 107.25 107.72 107.25 108.40 c B 107.45 108.66 m 107.45 109.35 108.02 109.91 108.70 109.91 c 109.39 109.91 109.95 109.35 109.95 108.66 c 109.95 107.98 109.39 107.41 108.70 107.41 c 108.02 107.41 107.45 107.98 107.45 108.66 c B 107.49 108.74 m 107.49 109.43 108.05 109.99 108.74 109.99 c 109.43 109.99 109.99 109.43 109.99 108.74 c 109.99 108.06 109.43 107.50 108.74 107.50 c 108.05 107.50 107.49 108.06 107.49 108.74 c B 107.58 108.82 m 107.58 109.51 108.14 110.07 108.83 110.07 c 109.51 110.07 110.07 109.51 110.07 108.82 c 110.07 108.13 109.51 107.57 108.83 107.57 c 108.14 107.57 107.58 108.13 107.58 108.82 c B 108.05 109.09 m 108.05 109.78 108.61 110.34 109.29 110.34 c 109.98 110.34 110.54 109.78 110.54 109.09 c 110.54 108.40 109.98 107.84 109.29 107.84 c 108.61 107.84 108.05 108.40 108.05 109.09 c B 108.18 109.32 m 108.18 110.00 108.74 110.57 109.42 110.57 c 110.11 110.57 110.67 110.00 110.67 109.32 c 110.67 108.63 110.11 108.07 109.42 108.07 c 108.74 108.07 108.18 108.63 108.18 109.32 c B 108.26 109.77 m 108.26 110.45 108.82 111.02 109.51 111.02 c 110.19 111.02 110.75 110.45 110.75 109.77 c 110.75 109.08 110.19 108.52 109.51 108.52 c 108.82 108.52 108.26 109.08 108.26 109.77 c B 108.41 109.85 m 108.41 110.54 108.98 111.10 109.66 111.10 c 110.35 111.10 110.91 110.54 110.91 109.85 c 110.91 109.16 110.35 108.60 109.66 108.60 c 108.98 108.60 108.41 109.16 108.41 109.85 c B 109.05 109.89 m 109.05 110.57 109.61 111.13 110.30 111.13 c 110.98 111.13 111.54 110.57 111.54 109.89 c 111.54 109.20 110.98 108.64 110.30 108.64 c 109.61 108.64 109.05 109.20 109.05 109.89 c B 109.26 110.06 m 109.26 110.75 109.82 111.31 110.51 111.31 c 111.19 111.31 111.75 110.75 111.75 110.06 c 111.75 109.38 111.19 108.81 110.51 108.81 c 109.82 108.81 109.26 109.38 109.26 110.06 c B 109.46 110.14 m 109.46 110.83 110.03 111.39 110.71 111.39 c 111.40 111.39 111.96 110.83 111.96 110.14 c 111.96 109.46 111.40 108.90 110.71 108.90 c 110.03 108.90 109.46 109.46 109.46 110.14 c B 109.85 111.16 m 109.85 111.85 110.41 112.41 111.10 112.41 c 111.79 112.41 112.35 111.85 112.35 111.16 c 112.35 110.47 111.79 109.91 111.10 109.91 c 110.41 109.91 109.85 110.47 109.85 111.16 c B 110.11 111.32 m 110.11 112.00 110.67 112.56 111.36 112.56 c 112.05 112.56 112.61 112.00 112.61 111.32 c 112.61 110.63 112.05 110.07 111.36 110.07 c 110.67 110.07 110.11 110.63 110.11 111.32 c B 110.40 111.62 m 110.40 112.30 110.96 112.86 111.64 112.86 c 112.33 112.86 112.89 112.30 112.89 111.62 c 112.89 110.93 112.33 110.37 111.64 110.37 c 110.96 110.37 110.40 110.93 110.40 111.62 c B 110.59 111.65 m 110.59 112.33 111.15 112.89 111.83 112.89 c 112.52 112.89 113.08 112.33 113.08 111.65 c 113.08 110.96 112.52 110.40 111.83 110.40 c 111.15 110.40 110.59 110.96 110.59 111.65 c B 110.70 112.08 m 110.70 112.76 111.27 113.32 111.95 113.32 c 112.64 113.32 113.20 112.76 113.20 112.08 c 113.20 111.39 112.64 110.83 111.95 110.83 c 111.27 110.83 110.70 111.39 110.70 112.08 c B 110.91 112.12 m 110.91 112.80 111.47 113.36 112.16 113.36 c 112.84 113.36 113.40 112.80 113.40 112.12 c 113.40 111.43 112.84 110.87 112.16 110.87 c 111.47 110.87 110.91 111.43 110.91 112.12 c B 111.22 112.13 m 111.22 112.81 111.78 113.38 112.46 113.38 c 113.15 113.38 113.71 112.81 113.71 112.13 c 113.71 111.44 113.15 110.88 112.46 110.88 c 111.78 110.88 111.22 111.44 111.22 112.13 c B 111.45 112.23 m 111.45 112.92 112.01 113.48 112.70 113.48 c 113.39 113.48 113.95 112.92 113.95 112.23 c 113.95 111.55 113.39 110.98 112.70 110.98 c 112.01 110.98 111.45 111.55 111.45 112.23 c B 111.69 112.25 m 111.69 112.94 112.25 113.50 112.94 113.50 c 113.62 113.50 114.19 112.94 114.19 112.25 c 114.19 111.57 113.62 111.01 112.94 111.01 c 112.25 111.01 111.69 111.57 111.69 112.25 c B 111.90 112.47 m 111.90 113.16 112.46 113.72 113.14 113.72 c 113.83 113.72 114.39 113.16 114.39 112.47 c 114.39 111.79 113.83 111.23 113.14 111.23 c 112.46 111.23 111.90 111.79 111.90 112.47 c B 112.05 112.53 m 112.05 113.22 112.61 113.78 113.29 113.78 c 113.98 113.78 114.54 113.22 114.54 112.53 c 114.54 111.84 113.98 111.28 113.29 111.28 c 112.61 111.28 112.05 111.84 112.05 112.53 c B 112.12 112.68 m 112.12 113.36 112.68 113.92 113.37 113.92 c 114.06 113.92 114.62 113.36 114.62 112.68 c 114.62 111.99 114.06 111.43 113.37 111.43 c 112.68 111.43 112.12 111.99 112.12 112.68 c B 112.77 112.69 m 112.77 113.38 113.33 113.94 114.02 113.94 c 114.70 113.94 115.26 113.38 115.26 112.69 c 115.26 112.01 114.70 111.45 114.02 111.45 c 113.33 111.45 112.77 112.01 112.77 112.69 c B 113.16 112.93 m 113.16 113.61 113.72 114.18 114.41 114.18 c 115.10 114.18 115.66 113.61 115.66 112.93 c 115.66 112.24 115.10 111.68 114.41 111.68 c 113.72 111.68 113.16 112.24 113.16 112.93 c B 113.38 113.18 m 113.38 113.87 113.94 114.43 114.63 114.43 c 115.31 114.43 115.87 113.87 115.87 113.18 c 115.87 112.50 115.31 111.94 114.63 111.94 c 113.94 111.94 113.38 112.50 113.38 113.18 c B 113.73 113.23 m 113.73 113.92 114.29 114.48 114.98 114.48 c 115.67 114.48 116.23 113.92 116.23 113.23 c 116.23 112.54 115.67 111.98 114.98 111.98 c 114.29 111.98 113.73 112.54 113.73 113.23 c B 114.48 113.31 m 114.48 114.00 115.04 114.56 115.72 114.56 c 116.41 114.56 116.97 114.00 116.97 113.31 c 116.97 112.62 116.41 112.06 115.72 112.06 c 115.04 112.06 114.48 112.62 114.48 113.31 c B 115.26 113.80 m 115.26 114.48 115.82 115.05 116.51 115.05 c 117.19 115.05 117.75 114.48 117.75 113.80 c 117.75 113.11 117.19 112.55 116.51 112.55 c 115.82 112.55 115.26 113.11 115.26 113.80 c B 115.61 113.91 m 115.61 114.60 116.17 115.16 116.86 115.16 c 117.54 115.16 118.10 114.60 118.10 113.91 c 118.10 113.22 117.54 112.66 116.86 112.66 c 116.17 112.66 115.61 113.22 115.61 113.91 c B 115.96 114.45 m 115.96 115.14 116.52 115.70 117.21 115.70 c 117.89 115.70 118.45 115.14 118.45 114.45 c 118.45 113.76 117.89 113.20 117.21 113.20 c 116.52 113.20 115.96 113.76 115.96 114.45 c B 116.14 114.81 m 116.14 115.50 116.70 116.06 117.39 116.06 c 118.08 116.06 118.64 115.50 118.64 114.81 c 118.64 114.13 118.08 113.57 117.39 113.57 c 116.70 113.57 116.14 114.13 116.14 114.81 c B 116.68 115.16 m 116.68 115.85 117.24 116.41 117.92 116.41 c 118.61 116.41 119.17 115.85 119.17 115.16 c 119.17 114.48 118.61 113.92 117.92 113.92 c 117.24 113.92 116.68 114.48 116.68 115.16 c B 116.96 115.63 m 116.96 116.31 117.52 116.87 118.20 116.87 c 118.89 116.87 119.45 116.31 119.45 115.63 c 119.45 114.94 118.89 114.38 118.20 114.38 c 117.52 114.38 116.96 114.94 116.96 115.63 c B 117.54 115.76 m 117.54 116.45 118.10 117.01 118.79 117.01 c 119.47 117.01 120.03 116.45 120.03 115.76 c 120.03 115.08 119.47 114.52 118.79 114.52 c 118.10 114.52 117.54 115.08 117.54 115.76 c B 118.24 116.56 m 118.24 117.25 118.81 117.81 119.49 117.81 c 120.18 117.81 120.74 117.25 120.74 116.56 c 120.74 115.87 120.18 115.31 119.49 115.31 c 118.81 115.31 118.24 115.87 118.24 116.56 c B 118.68 117.75 m 118.68 118.44 119.24 119.00 119.93 119.00 c 120.61 119.00 121.18 118.44 121.18 117.75 c 121.18 117.07 120.61 116.51 119.93 116.51 c 119.24 116.51 118.68 117.07 118.68 117.75 c B 119.02 118.86 m 119.02 119.55 119.58 120.11 120.27 120.11 c 120.95 120.11 121.52 119.55 121.52 118.86 c 121.52 118.17 120.95 117.61 120.27 117.61 c 119.58 117.61 119.02 118.17 119.02 118.86 c B 119.65 119.21 m 119.65 119.89 120.21 120.45 120.89 120.45 c 121.58 120.45 122.14 119.89 122.14 119.21 c 122.14 118.52 121.58 117.96 120.89 117.96 c 120.21 117.96 119.65 118.52 119.65 119.21 c B 119.97 119.34 m 119.97 120.03 120.54 120.59 121.22 120.59 c 121.91 120.59 122.47 120.03 122.47 119.34 c 122.47 118.66 121.91 118.09 121.22 118.09 c 120.54 118.09 119.97 118.66 119.97 119.34 c B 120.15 119.52 m 120.15 120.20 120.71 120.77 121.40 120.77 c 122.09 120.77 122.65 120.20 122.65 119.52 c 122.65 118.83 122.09 118.27 121.40 118.27 c 120.71 118.27 120.15 118.83 120.15 119.52 c B 120.35 119.92 m 120.35 120.60 120.91 121.16 121.59 121.16 c 122.28 121.16 122.84 120.60 122.84 119.92 c 122.84 119.23 122.28 118.67 121.59 118.67 c 120.91 118.67 120.35 119.23 120.35 119.92 c B 120.77 120.03 m 120.77 120.71 121.33 121.28 122.01 121.28 c 122.70 121.28 123.26 120.71 123.26 120.03 c 123.26 119.34 122.70 118.78 122.01 118.78 c 121.33 118.78 120.77 119.34 120.77 120.03 c B 121.24 120.03 m 121.24 120.72 121.80 121.28 122.49 121.28 c 123.17 121.28 123.74 120.72 123.74 120.03 c 123.74 119.34 123.17 118.78 122.49 118.78 c 121.80 118.78 121.24 119.34 121.24 120.03 c B 123.19 120.23 m 123.19 120.92 123.75 121.48 124.44 121.48 c 125.13 121.48 125.69 120.92 125.69 120.23 c 125.69 119.54 125.13 118.98 124.44 118.98 c 123.75 118.98 123.19 119.54 123.19 120.23 c B 123.24 120.68 m 123.24 121.37 123.80 121.93 124.49 121.93 c 125.17 121.93 125.74 121.37 125.74 120.68 c 125.74 120.00 125.17 119.43 124.49 119.43 c 123.80 119.43 123.24 120.00 123.24 120.68 c B 123.38 122.50 m 123.38 123.19 123.94 123.75 124.62 123.75 c 125.31 123.75 125.87 123.19 125.87 122.50 c 125.87 121.81 125.31 121.25 124.62 121.25 c 123.94 121.25 123.38 121.81 123.38 122.50 c B 125.09 122.71 m 125.09 123.39 125.65 123.95 126.34 123.95 c 127.02 123.95 127.58 123.39 127.58 122.71 c 127.58 122.02 127.02 121.46 126.34 121.46 c 125.65 121.46 125.09 122.02 125.09 122.71 c B 126.07 123.16 m 126.07 123.85 126.63 124.41 127.32 124.41 c 128.00 124.41 128.57 123.85 128.57 123.16 c 128.57 122.48 128.00 121.92 127.32 121.92 c 126.63 121.92 126.07 122.48 126.07 123.16 c B 128.96 125.18 m 128.96 125.87 129.52 126.43 130.21 126.43 c 130.89 126.43 131.45 125.87 131.45 125.18 c 131.45 124.50 130.89 123.94 130.21 123.94 c 129.52 123.94 128.96 124.50 128.96 125.18 c B 131.21 126.64 m 131.21 127.33 131.77 127.89 132.46 127.89 c 133.15 127.89 133.71 127.33 133.71 126.64 c 133.71 125.96 133.15 125.40 132.46 125.40 c 131.77 125.40 131.21 125.96 131.21 126.64 c B 132.11 127.63 m 132.11 128.32 132.67 128.88 133.36 128.88 c 134.05 128.88 134.61 128.32 134.61 127.63 c 134.61 126.95 134.05 126.39 133.36 126.39 c 132.67 126.39 132.11 126.95 132.11 127.63 c B 133.88 132.82 m 133.88 133.51 134.44 134.07 135.13 134.07 c 135.81 134.07 136.37 133.51 136.37 132.82 c 136.37 132.14 135.81 131.57 135.13 131.57 c 134.44 131.57 133.88 132.14 133.88 132.82 c B 135.10 134.30 m 135.10 134.99 135.66 135.55 136.35 135.55 c 137.03 135.55 137.59 134.99 137.59 134.30 c 137.59 133.61 137.03 133.05 136.35 133.05 c 135.66 133.05 135.10 133.61 135.10 134.30 c B 136.18 140.00 m 136.18 140.69 136.74 141.25 137.43 141.25 c 138.11 141.25 138.68 140.69 138.68 140.00 c 138.68 139.32 138.11 138.76 137.43 138.76 c 136.74 138.76 136.18 139.32 136.18 140.00 c B 140.72 150.31 m 140.72 150.99 141.29 151.55 141.97 151.55 c 142.66 151.55 143.22 150.99 143.22 150.31 c 143.22 149.62 142.66 149.06 141.97 149.06 c 141.29 149.06 140.72 149.62 140.72 150.31 c B 153.01 168.12 m 153.01 168.80 153.57 169.36 154.26 169.36 c 154.95 169.36 155.51 168.80 155.51 168.12 c 155.51 167.43 154.95 166.87 154.26 166.87 c 153.57 166.87 153.01 167.43 153.01 168.12 c B Q q 91.25 98.38 91.25 76.99 re W n Q q 96.00 103.13 81.74 67.49 re W n /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 96.00 103.13 m 177.74 170.62 l S 0.75 w [ 3.00 5.00] 0 d 96.00 109.38 m 177.74 176.86 l S 96.00 96.88 m 177.74 164.37 l S Q q /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 96.00 105.63 m 96.00 167.76 l S 96.00 105.63 m 91.25 105.63 l S 96.00 118.05 m 91.25 118.05 l S 96.00 130.48 m 91.25 130.48 l S 96.00 142.91 m 91.25 142.91 l S 96.00 155.34 m 91.25 155.34 l S 96.00 167.76 m 91.25 167.76 l S BT /GS258 gs 0.000 0.000 0.000 rg /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 103.40 Tm (0) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 115.83 Tm (5) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 138.46 Tm (15) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 163.31 Tm (25) Tj ET 96.00 103.13 m 177.74 103.13 l 177.74 170.62 l 96.00 170.62 l 96.00 103.13 l S Q q 187.25 103.13 81.74 67.49 re W n Q q 187.25 103.13 81.74 67.49 re W n /GS257 gs 0.000 0.000 1.000 rg /GS1 gs 0.000 0.000 1.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.03 105.63 m 189.03 106.31 189.59 106.87 190.28 106.87 c 190.96 106.87 191.52 106.31 191.52 105.63 c 191.52 104.94 190.96 104.38 190.28 104.38 c 189.59 104.38 189.03 104.94 189.03 105.63 c B 189.16 105.63 m 189.16 106.31 189.72 106.87 190.41 106.87 c 191.10 106.87 191.66 106.31 191.66 105.63 c 191.66 104.94 191.10 104.38 190.41 104.38 c 189.72 104.38 189.16 104.94 189.16 105.63 c B 189.20 105.63 m 189.20 106.31 189.76 106.87 190.45 106.87 c 191.13 106.87 191.70 106.31 191.70 105.63 c 191.70 104.94 191.13 104.38 190.45 104.38 c 189.76 104.38 189.20 104.94 189.20 105.63 c B 189.49 105.63 m 189.49 106.31 190.05 106.87 190.73 106.87 c 191.42 106.87 191.98 106.31 191.98 105.63 c 191.98 104.94 191.42 104.38 190.73 104.38 c 190.05 104.38 189.49 104.94 189.49 105.63 c B 189.53 105.63 m 189.53 106.31 190.09 106.87 190.78 106.87 c 191.46 106.87 192.02 106.31 192.02 105.63 c 192.02 104.94 191.46 104.38 190.78 104.38 c 190.09 104.38 189.53 104.94 189.53 105.63 c B 189.55 105.63 m 189.55 106.31 190.11 106.87 190.79 106.87 c 191.48 106.87 192.04 106.31 192.04 105.63 c 192.04 104.94 191.48 104.38 190.79 104.38 c 190.11 104.38 189.55 104.94 189.55 105.63 c B 189.58 105.63 m 189.58 106.31 190.14 106.87 190.83 106.87 c 191.51 106.87 192.07 106.31 192.07 105.63 c 192.07 104.94 191.51 104.38 190.83 104.38 c 190.14 104.38 189.58 104.94 189.58 105.63 c B 189.60 105.63 m 189.60 106.31 190.16 106.87 190.85 106.87 c 191.53 106.87 192.09 106.31 192.09 105.63 c 192.09 104.94 191.53 104.38 190.85 104.38 c 190.16 104.38 189.60 104.94 189.60 105.63 c B 189.65 105.63 m 189.65 106.31 190.21 106.87 190.90 106.87 c 191.58 106.87 192.14 106.31 192.14 105.63 c 192.14 104.94 191.58 104.38 190.90 104.38 c 190.21 104.38 189.65 104.94 189.65 105.63 c B 189.65 105.63 m 189.65 106.31 190.21 106.87 190.90 106.87 c 191.58 106.87 192.14 106.31 192.14 105.63 c 192.14 104.94 191.58 104.38 190.90 104.38 c 190.21 104.38 189.65 104.94 189.65 105.63 c B 189.69 105.63 m 189.69 106.31 190.25 106.87 190.93 106.87 c 191.62 106.87 192.18 106.31 192.18 105.63 c 192.18 104.94 191.62 104.38 190.93 104.38 c 190.25 104.38 189.69 104.94 189.69 105.63 c B 189.77 105.63 m 189.77 106.31 190.33 106.87 191.01 106.87 c 191.70 106.87 192.26 106.31 192.26 105.63 c 192.26 104.94 191.70 104.38 191.01 104.38 c 190.33 104.38 189.77 104.94 189.77 105.63 c B 189.78 105.63 m 189.78 106.31 190.34 106.87 191.02 106.87 c 191.71 106.87 192.27 106.31 192.27 105.63 c 192.27 104.94 191.71 104.38 191.02 104.38 c 190.34 104.38 189.78 104.94 189.78 105.63 c B 189.79 105.63 m 189.79 106.31 190.35 106.87 191.04 106.87 c 191.73 106.87 192.29 106.31 192.29 105.63 c 192.29 104.94 191.73 104.38 191.04 104.38 c 190.35 104.38 189.79 104.94 189.79 105.63 c B 189.85 105.63 m 189.85 106.31 190.41 106.87 191.09 106.87 c 191.78 106.87 192.34 106.31 192.34 105.63 c 192.34 104.94 191.78 104.38 191.09 104.38 c 190.41 104.38 189.85 104.94 189.85 105.63 c B 189.86 105.63 m 189.86 106.31 190.42 106.87 191.11 106.87 c 191.80 106.87 192.36 106.31 192.36 105.63 c 192.36 104.94 191.80 104.38 191.11 104.38 c 190.42 104.38 189.86 104.94 189.86 105.63 c B 189.91 105.63 m 189.91 106.31 190.47 106.87 191.16 106.87 c 191.85 106.87 192.41 106.31 192.41 105.63 c 192.41 104.94 191.85 104.38 191.16 104.38 c 190.47 104.38 189.91 104.94 189.91 105.63 c B 189.92 105.63 m 189.92 106.31 190.48 106.87 191.17 106.87 c 191.86 106.87 192.42 106.31 192.42 105.63 c 192.42 104.94 191.86 104.38 191.17 104.38 c 190.48 104.38 189.92 104.94 189.92 105.63 c B 189.93 105.63 m 189.93 106.31 190.49 106.87 191.18 106.87 c 191.87 106.87 192.43 106.31 192.43 105.63 c 192.43 104.94 191.87 104.38 191.18 104.38 c 190.49 104.38 189.93 104.94 189.93 105.63 c B 189.99 105.63 m 189.99 106.31 190.55 106.87 191.24 106.87 c 191.93 106.87 192.49 106.31 192.49 105.63 c 192.49 104.94 191.93 104.38 191.24 104.38 c 190.55 104.38 189.99 104.94 189.99 105.63 c B 190.01 105.63 m 190.01 106.31 190.58 106.87 191.26 106.87 c 191.95 106.87 192.51 106.31 192.51 105.63 c 192.51 104.94 191.95 104.38 191.26 104.38 c 190.58 104.38 190.01 104.94 190.01 105.63 c B 190.19 105.63 m 190.19 106.31 190.75 106.87 191.44 106.87 c 192.13 106.87 192.69 106.31 192.69 105.63 c 192.69 104.94 192.13 104.38 191.44 104.38 c 190.75 104.38 190.19 104.94 190.19 105.63 c B 190.20 105.63 m 190.20 106.31 190.76 106.87 191.45 106.87 c 192.13 106.87 192.69 106.31 192.69 105.63 c 192.69 104.94 192.13 104.38 191.45 104.38 c 190.76 104.38 190.20 104.94 190.20 105.63 c B 190.39 105.63 m 190.39 106.31 190.95 106.87 191.63 106.87 c 192.32 106.87 192.88 106.31 192.88 105.63 c 192.88 104.94 192.32 104.38 191.63 104.38 c 190.95 104.38 190.39 104.94 190.39 105.63 c B 190.45 105.63 m 190.45 106.31 191.01 106.87 191.69 106.87 c 192.38 106.87 192.94 106.31 192.94 105.63 c 192.94 104.94 192.38 104.38 191.69 104.38 c 191.01 104.38 190.45 104.94 190.45 105.63 c B 190.47 105.63 m 190.47 106.31 191.03 106.87 191.72 106.87 c 192.41 106.87 192.97 106.31 192.97 105.63 c 192.97 104.94 192.41 104.38 191.72 104.38 c 191.03 104.38 190.47 104.94 190.47 105.63 c B 190.49 105.63 m 190.49 106.31 191.05 106.87 191.74 106.87 c 192.42 106.87 192.98 106.31 192.98 105.63 c 192.98 104.94 192.42 104.38 191.74 104.38 c 191.05 104.38 190.49 104.94 190.49 105.63 c B 190.65 105.63 m 190.65 106.31 191.21 106.87 191.89 106.87 c 192.58 106.87 193.14 106.31 193.14 105.63 c 193.14 104.94 192.58 104.38 191.89 104.38 c 191.21 104.38 190.65 104.94 190.65 105.63 c B 190.71 105.63 m 190.71 106.31 191.27 106.87 191.96 106.87 c 192.64 106.87 193.20 106.31 193.20 105.63 c 193.20 104.94 192.64 104.38 191.96 104.38 c 191.27 104.38 190.71 104.94 190.71 105.63 c B 190.77 105.63 m 190.77 106.31 191.33 106.87 192.02 106.87 c 192.70 106.87 193.26 106.31 193.26 105.63 c 193.26 104.94 192.70 104.38 192.02 104.38 c 191.33 104.38 190.77 104.94 190.77 105.63 c B 190.86 105.81 m 190.86 106.50 191.42 107.06 192.11 107.06 c 192.79 107.06 193.36 106.50 193.36 105.81 c 193.36 105.13 192.79 104.56 192.11 104.56 c 191.42 104.56 190.86 105.13 190.86 105.81 c B 190.93 106.04 m 190.93 106.72 191.49 107.29 192.17 107.29 c 192.86 107.29 193.42 106.72 193.42 106.04 c 193.42 105.35 192.86 104.79 192.17 104.79 c 191.49 104.79 190.93 105.35 190.93 106.04 c B 191.07 106.16 m 191.07 106.85 191.63 107.41 192.31 107.41 c 193.00 107.41 193.56 106.85 193.56 106.16 c 193.56 105.47 193.00 104.91 192.31 104.91 c 191.63 104.91 191.07 105.47 191.07 106.16 c B 191.39 106.46 m 191.39 107.14 191.96 107.71 192.64 107.71 c 193.33 107.71 193.89 107.14 193.89 106.46 c 193.89 105.77 193.33 105.21 192.64 105.21 c 191.96 105.21 191.39 105.77 191.39 106.46 c B 191.45 106.46 m 191.45 107.14 192.01 107.71 192.70 107.71 c 193.39 107.71 193.95 107.14 193.95 106.46 c 193.95 105.77 193.39 105.21 192.70 105.21 c 192.01 105.21 191.45 105.77 191.45 106.46 c B 191.56 106.52 m 191.56 107.20 192.12 107.76 192.81 107.76 c 193.49 107.76 194.06 107.20 194.06 106.52 c 194.06 105.83 193.49 105.27 192.81 105.27 c 192.12 105.27 191.56 105.83 191.56 106.52 c B 191.64 106.57 m 191.64 107.25 192.20 107.81 192.89 107.81 c 193.58 107.81 194.14 107.25 194.14 106.57 c 194.14 105.88 193.58 105.32 192.89 105.32 c 192.20 105.32 191.64 105.88 191.64 106.57 c B 191.71 106.59 m 191.71 107.27 192.27 107.83 192.95 107.83 c 193.64 107.83 194.20 107.27 194.20 106.59 c 194.20 105.90 193.64 105.34 192.95 105.34 c 192.27 105.34 191.71 105.90 191.71 106.59 c B 191.74 106.87 m 191.74 107.56 192.31 108.12 192.99 108.12 c 193.68 108.12 194.24 107.56 194.24 106.87 c 194.24 106.19 193.68 105.63 192.99 105.63 c 192.31 105.63 191.74 106.19 191.74 106.87 c B 191.83 107.32 m 191.83 108.00 192.39 108.56 193.08 108.56 c 193.76 108.56 194.33 108.00 194.33 107.32 c 194.33 106.63 193.76 106.07 193.08 106.07 c 192.39 106.07 191.83 106.63 191.83 107.32 c B 191.93 107.61 m 191.93 108.30 192.49 108.86 193.18 108.86 c 193.87 108.86 194.43 108.30 194.43 107.61 c 194.43 106.93 193.87 106.37 193.18 106.37 c 192.49 106.37 191.93 106.93 191.93 107.61 c B 191.94 107.61 m 191.94 108.30 192.51 108.86 193.19 108.86 c 193.88 108.86 194.44 108.30 194.44 107.61 c 194.44 106.93 193.88 106.37 193.19 106.37 c 192.51 106.37 191.94 106.93 191.94 107.61 c B 191.97 107.75 m 191.97 108.44 192.53 109.00 193.21 109.00 c 193.90 109.00 194.46 108.44 194.46 107.75 c 194.46 107.06 193.90 106.50 193.21 106.50 c 192.53 106.50 191.97 107.06 191.97 107.75 c B 191.98 107.91 m 191.98 108.60 192.54 109.16 193.23 109.16 c 193.92 109.16 194.48 108.60 194.48 107.91 c 194.48 107.23 193.92 106.67 193.23 106.67 c 192.54 106.67 191.98 107.23 191.98 107.91 c B 192.30 107.95 m 192.30 108.64 192.86 109.20 193.55 109.20 c 194.23 109.20 194.79 108.64 194.79 107.95 c 194.79 107.26 194.23 106.70 193.55 106.70 c 192.86 106.70 192.30 107.26 192.30 107.95 c B 192.59 107.95 m 192.59 108.64 193.15 109.20 193.83 109.20 c 194.52 109.20 195.08 108.64 195.08 107.95 c 195.08 107.27 194.52 106.71 193.83 106.71 c 193.15 106.71 192.59 107.27 192.59 107.95 c B 192.67 107.95 m 192.67 108.64 193.23 109.20 193.91 109.20 c 194.60 109.20 195.16 108.64 195.16 107.95 c 195.16 107.27 194.60 106.71 193.91 106.71 c 193.23 106.71 192.67 107.27 192.67 107.95 c B 192.71 108.40 m 192.71 109.09 193.28 109.65 193.96 109.65 c 194.65 109.65 195.21 109.09 195.21 108.40 c 195.21 107.72 194.65 107.16 193.96 107.16 c 193.28 107.16 192.71 107.72 192.71 108.40 c B 192.89 108.66 m 192.89 109.35 193.45 109.91 194.14 109.91 c 194.83 109.91 195.39 109.35 195.39 108.66 c 195.39 107.98 194.83 107.41 194.14 107.41 c 193.45 107.41 192.89 107.98 192.89 108.66 c B 193.01 108.74 m 193.01 109.43 193.57 109.99 194.25 109.99 c 194.94 109.99 195.50 109.43 195.50 108.74 c 195.50 108.06 194.94 107.50 194.25 107.50 c 193.57 107.50 193.01 108.06 193.01 108.74 c B 193.03 108.82 m 193.03 109.51 193.59 110.07 194.28 110.07 c 194.97 110.07 195.53 109.51 195.53 108.82 c 195.53 108.13 194.97 107.57 194.28 107.57 c 193.59 107.57 193.03 108.13 193.03 108.82 c B 193.22 109.09 m 193.22 109.78 193.78 110.34 194.46 110.34 c 195.15 110.34 195.71 109.78 195.71 109.09 c 195.71 108.40 195.15 107.84 194.46 107.84 c 193.78 107.84 193.22 108.40 193.22 109.09 c B 193.23 109.32 m 193.23 110.00 193.79 110.57 194.47 110.57 c 195.16 110.57 195.72 110.00 195.72 109.32 c 195.72 108.63 195.16 108.07 194.47 108.07 c 193.79 108.07 193.23 108.63 193.23 109.32 c B 193.27 109.77 m 193.27 110.45 193.83 111.02 194.52 111.02 c 195.20 111.02 195.76 110.45 195.76 109.77 c 195.76 109.08 195.20 108.52 194.52 108.52 c 193.83 108.52 193.27 109.08 193.27 109.77 c B 193.48 109.85 m 193.48 110.54 194.04 111.10 194.73 111.10 c 195.41 111.10 195.97 110.54 195.97 109.85 c 195.97 109.16 195.41 108.60 194.73 108.60 c 194.04 108.60 193.48 109.16 193.48 109.85 c B 193.62 109.89 m 193.62 110.57 194.18 111.13 194.87 111.13 c 195.55 111.13 196.11 110.57 196.11 109.89 c 196.11 109.20 195.55 108.64 194.87 108.64 c 194.18 108.64 193.62 109.20 193.62 109.89 c B 193.64 110.06 m 193.64 110.75 194.20 111.31 194.89 111.31 c 195.58 111.31 196.14 110.75 196.14 110.06 c 196.14 109.38 195.58 108.81 194.89 108.81 c 194.20 108.81 193.64 109.38 193.64 110.06 c B 193.71 110.14 m 193.71 110.83 194.27 111.39 194.96 111.39 c 195.65 111.39 196.21 110.83 196.21 110.14 c 196.21 109.46 195.65 108.90 194.96 108.90 c 194.27 108.90 193.71 109.46 193.71 110.14 c B 193.92 111.16 m 193.92 111.85 194.48 112.41 195.16 112.41 c 195.85 112.41 196.41 111.85 196.41 111.16 c 196.41 110.47 195.85 109.91 195.16 109.91 c 194.48 109.91 193.92 110.47 193.92 111.16 c B 194.13 111.32 m 194.13 112.00 194.69 112.56 195.37 112.56 c 196.06 112.56 196.62 112.00 196.62 111.32 c 196.62 110.63 196.06 110.07 195.37 110.07 c 194.69 110.07 194.13 110.63 194.13 111.32 c B 194.18 111.62 m 194.18 112.30 194.74 112.86 195.42 112.86 c 196.11 112.86 196.67 112.30 196.67 111.62 c 196.67 110.93 196.11 110.37 195.42 110.37 c 194.74 110.37 194.18 110.93 194.18 111.62 c B 194.51 111.65 m 194.51 112.33 195.08 112.89 195.76 112.89 c 196.45 112.89 197.01 112.33 197.01 111.65 c 197.01 110.96 196.45 110.40 195.76 110.40 c 195.08 110.40 194.51 110.96 194.51 111.65 c B 194.71 112.08 m 194.71 112.76 195.28 113.32 195.96 113.32 c 196.65 113.32 197.21 112.76 197.21 112.08 c 197.21 111.39 196.65 110.83 195.96 110.83 c 195.28 110.83 194.71 111.39 194.71 112.08 c B 194.72 112.12 m 194.72 112.80 195.28 113.36 195.97 113.36 c 196.65 113.36 197.21 112.80 197.21 112.12 c 197.21 111.43 196.65 110.87 195.97 110.87 c 195.28 110.87 194.72 111.43 194.72 112.12 c B 195.30 112.13 m 195.30 112.81 195.86 113.38 196.54 113.38 c 197.23 113.38 197.79 112.81 197.79 112.13 c 197.79 111.44 197.23 110.88 196.54 110.88 c 195.86 110.88 195.30 111.44 195.30 112.13 c B 195.91 112.23 m 195.91 112.92 196.47 113.48 197.15 113.48 c 197.84 113.48 198.40 112.92 198.40 112.23 c 198.40 111.55 197.84 110.98 197.15 110.98 c 196.47 110.98 195.91 111.55 195.91 112.23 c B 195.91 112.25 m 195.91 112.94 196.47 113.50 197.16 113.50 c 197.84 113.50 198.41 112.94 198.41 112.25 c 198.41 111.57 197.84 111.01 197.16 111.01 c 196.47 111.01 195.91 111.57 195.91 112.25 c B 196.11 112.47 m 196.11 113.16 196.67 113.72 197.36 113.72 c 198.04 113.72 198.60 113.16 198.60 112.47 c 198.60 111.79 198.04 111.23 197.36 111.23 c 196.67 111.23 196.11 111.79 196.11 112.47 c B 196.42 112.53 m 196.42 113.22 196.98 113.78 197.67 113.78 c 198.36 113.78 198.92 113.22 198.92 112.53 c 198.92 111.84 198.36 111.28 197.67 111.28 c 196.98 111.28 196.42 111.84 196.42 112.53 c B 196.61 112.68 m 196.61 113.36 197.17 113.92 197.86 113.92 c 198.54 113.92 199.11 113.36 199.11 112.68 c 199.11 111.99 198.54 111.43 197.86 111.43 c 197.17 111.43 196.61 111.99 196.61 112.68 c B 196.66 112.69 m 196.66 113.38 197.22 113.94 197.91 113.94 c 198.59 113.94 199.15 113.38 199.15 112.69 c 199.15 112.01 198.59 111.45 197.91 111.45 c 197.22 111.45 196.66 112.01 196.66 112.69 c B 197.44 112.93 m 197.44 113.61 198.00 114.18 198.68 114.18 c 199.37 114.18 199.93 113.61 199.93 112.93 c 199.93 112.24 199.37 111.68 198.68 111.68 c 198.00 111.68 197.44 112.24 197.44 112.93 c B 197.68 113.18 m 197.68 113.87 198.24 114.43 198.93 114.43 c 199.61 114.43 200.17 113.87 200.17 113.18 c 200.17 112.50 199.61 111.94 198.93 111.94 c 198.24 111.94 197.68 112.50 197.68 113.18 c B 197.88 113.23 m 197.88 113.92 198.44 114.48 199.13 114.48 c 199.81 114.48 200.37 113.92 200.37 113.23 c 200.37 112.54 199.81 111.98 199.13 111.98 c 198.44 111.98 197.88 112.54 197.88 113.23 c B 198.22 113.31 m 198.22 114.00 198.78 114.56 199.47 114.56 c 200.16 114.56 200.72 114.00 200.72 113.31 c 200.72 112.62 200.16 112.06 199.47 112.06 c 198.78 112.06 198.22 112.62 198.22 113.31 c B 198.58 113.80 m 198.58 114.48 199.14 115.05 199.83 115.05 c 200.51 115.05 201.07 114.48 201.07 113.80 c 201.07 113.11 200.51 112.55 199.83 112.55 c 199.14 112.55 198.58 113.11 198.58 113.80 c B 198.74 113.91 m 198.74 114.60 199.30 115.16 199.99 115.16 c 200.67 115.16 201.24 114.60 201.24 113.91 c 201.24 113.22 200.67 112.66 199.99 112.66 c 199.30 112.66 198.74 113.22 198.74 113.91 c B 199.34 114.45 m 199.34 115.14 199.91 115.70 200.59 115.70 c 201.28 115.70 201.84 115.14 201.84 114.45 c 201.84 113.76 201.28 113.20 200.59 113.20 c 199.91 113.20 199.34 113.76 199.34 114.45 c B 199.51 114.81 m 199.51 115.50 200.07 116.06 200.75 116.06 c 201.44 116.06 202.00 115.50 202.00 114.81 c 202.00 114.13 201.44 113.57 200.75 113.57 c 200.07 113.57 199.51 114.13 199.51 114.81 c B 199.66 115.16 m 199.66 115.85 200.22 116.41 200.91 116.41 c 201.60 116.41 202.16 115.85 202.16 115.16 c 202.16 114.48 201.60 113.92 200.91 113.92 c 200.22 113.92 199.66 114.48 199.66 115.16 c B 200.30 115.63 m 200.30 116.31 200.86 116.87 201.55 116.87 c 202.23 116.87 202.79 116.31 202.79 115.63 c 202.79 114.94 202.23 114.38 201.55 114.38 c 200.86 114.38 200.30 114.94 200.30 115.63 c B 200.44 115.76 m 200.44 116.45 201.00 117.01 201.69 117.01 c 202.37 117.01 202.93 116.45 202.93 115.76 c 202.93 115.08 202.37 114.52 201.69 114.52 c 201.00 114.52 200.44 115.08 200.44 115.76 c B 200.51 116.56 m 200.51 117.25 201.07 117.81 201.76 117.81 c 202.44 117.81 203.00 117.25 203.00 116.56 c 203.00 115.87 202.44 115.31 201.76 115.31 c 201.07 115.31 200.51 115.87 200.51 116.56 c B 200.59 117.75 m 200.59 118.44 201.16 119.00 201.84 119.00 c 202.53 119.00 203.09 118.44 203.09 117.75 c 203.09 117.07 202.53 116.51 201.84 116.51 c 201.16 116.51 200.59 117.07 200.59 117.75 c B 201.24 119.21 m 201.24 119.89 201.80 120.45 202.49 120.45 c 203.17 120.45 203.74 119.89 203.74 119.21 c 203.74 118.52 203.17 117.96 202.49 117.96 c 201.80 117.96 201.24 118.52 201.24 119.21 c B 201.40 119.34 m 201.40 120.03 201.96 120.59 202.65 120.59 c 203.34 120.59 203.90 120.03 203.90 119.34 c 203.90 118.66 203.34 118.09 202.65 118.09 c 201.96 118.09 201.40 118.66 201.40 119.34 c B 201.77 119.52 m 201.77 120.20 202.33 120.77 203.02 120.77 c 203.70 120.77 204.26 120.20 204.26 119.52 c 204.26 118.83 203.70 118.27 203.02 118.27 c 202.33 118.27 201.77 118.83 201.77 119.52 c B 201.89 119.92 m 201.89 120.60 202.45 121.16 203.14 121.16 c 203.83 121.16 204.39 120.60 204.39 119.92 c 204.39 119.23 203.83 118.67 203.14 118.67 c 202.45 118.67 201.89 119.23 201.89 119.92 c B 202.27 120.03 m 202.27 120.71 202.83 121.28 203.51 121.28 c 204.20 121.28 204.76 120.71 204.76 120.03 c 204.76 119.34 204.20 118.78 203.51 118.78 c 202.83 118.78 202.27 119.34 202.27 120.03 c B 202.48 120.03 m 202.48 120.72 203.04 121.28 203.73 121.28 c 204.41 121.28 204.98 120.72 204.98 120.03 c 204.98 119.34 204.41 118.78 203.73 118.78 c 203.04 118.78 202.48 119.34 202.48 120.03 c B 202.70 120.23 m 202.70 120.92 203.26 121.48 203.95 121.48 c 204.64 121.48 205.20 120.92 205.20 120.23 c 205.20 119.54 204.64 118.98 203.95 118.98 c 203.26 118.98 202.70 119.54 202.70 120.23 c B 202.81 120.68 m 202.81 121.37 203.38 121.93 204.06 121.93 c 204.75 121.93 205.31 121.37 205.31 120.68 c 205.31 120.00 204.75 119.43 204.06 119.43 c 203.38 119.43 202.81 120.00 202.81 120.68 c B 202.94 122.50 m 202.94 123.19 203.50 123.75 204.19 123.75 c 204.87 123.75 205.43 123.19 205.43 122.50 c 205.43 121.81 204.87 121.25 204.19 121.25 c 203.50 121.25 202.94 121.81 202.94 122.50 c B 203.68 122.71 m 203.68 123.39 204.24 123.95 204.92 123.95 c 205.61 123.95 206.17 123.39 206.17 122.71 c 206.17 122.02 205.61 121.46 204.92 121.46 c 204.24 121.46 203.68 122.02 203.68 122.71 c B 204.63 123.16 m 204.63 123.85 205.19 124.41 205.87 124.41 c 206.56 124.41 207.12 123.85 207.12 123.16 c 207.12 122.48 206.56 121.92 205.87 121.92 c 205.19 121.92 204.63 122.48 204.63 123.16 c B 209.49 126.64 m 209.49 127.33 210.05 127.89 210.74 127.89 c 211.43 127.89 211.99 127.33 211.99 126.64 c 211.99 125.96 211.43 125.40 210.74 125.40 c 210.05 125.40 209.49 125.96 209.49 126.64 c B 210.04 127.63 m 210.04 128.32 210.60 128.88 211.28 128.88 c 211.97 128.88 212.53 128.32 212.53 127.63 c 212.53 126.95 211.97 126.39 211.28 126.39 c 210.60 126.39 210.04 126.95 210.04 127.63 c B 210.89 132.82 m 210.89 133.51 211.46 134.07 212.14 134.07 c 212.83 134.07 213.39 133.51 213.39 132.82 c 213.39 132.14 212.83 131.57 212.14 131.57 c 211.46 131.57 210.89 132.14 210.89 132.82 c B 211.38 134.30 m 211.38 134.99 211.95 135.55 212.63 135.55 c 213.32 135.55 213.88 134.99 213.88 134.30 c 213.88 133.61 213.32 133.05 212.63 133.05 c 211.95 133.05 211.38 133.61 211.38 134.30 c B 222.58 140.00 m 222.58 140.69 223.15 141.25 223.83 141.25 c 224.52 141.25 225.08 140.69 225.08 140.00 c 225.08 139.32 224.52 138.76 223.83 138.76 c 223.15 138.76 222.58 139.32 222.58 140.00 c B 226.35 150.31 m 226.35 150.99 226.91 151.55 227.59 151.55 c 228.28 151.55 228.84 150.99 228.84 150.31 c 228.84 149.62 228.28 149.06 227.59 149.06 c 226.91 149.06 226.35 149.62 226.35 150.31 c B 231.97 168.12 m 231.97 168.80 232.53 169.36 233.22 169.36 c 233.91 169.36 234.47 168.80 234.47 168.12 c 234.47 167.43 233.91 166.87 233.22 166.87 c 232.53 166.87 231.97 167.43 231.97 168.12 c B Q q 182.50 98.38 91.25 76.99 re W n Q q 187.25 103.13 81.74 67.49 re W n /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 187.25 103.13 m 268.99 170.62 l S 0.75 w [ 3.00 5.00] 0 d 187.25 109.38 m 268.99 176.86 l S 187.25 96.88 m 268.99 164.37 l S Q q /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 187.25 103.13 m 268.99 103.13 l 268.99 170.62 l 187.25 170.62 l 187.25 103.13 l S Q q 4.75 26.14 81.74 67.49 re W n Q q 0.00 21.38 91.25 76.99 re W n Q q 4.75 26.14 81.74 67.49 re W n BT /GS258 gs 0.000 0.000 0.000 rg /F2 1 Tf 11.00 0.00 -0.00 11.00 33.70 56.01 Tm (educ) Tj ET Q q 96.00 26.14 81.74 67.49 re W n Q q 96.00 26.14 81.74 67.49 re W n /GS257 gs 0.000 0.000 1.000 rg /GS1 gs 0.000 0.000 1.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 97.78 42.52 m 97.78 43.21 98.34 43.77 99.03 43.77 c 99.71 43.77 100.27 43.21 100.27 42.52 c 100.27 41.84 99.71 41.27 99.03 41.27 c 98.34 41.27 97.78 41.84 97.78 42.52 c B 97.78 42.52 m 97.78 43.21 98.34 43.77 99.03 43.77 c 99.71 43.77 100.27 43.21 100.27 42.52 c 100.27 41.84 99.71 41.27 99.03 41.27 c 98.34 41.27 97.78 41.84 97.78 42.52 c B 101.99 42.52 m 101.99 43.21 102.55 43.77 103.23 43.77 c 103.92 43.77 104.48 43.21 104.48 42.52 c 104.48 41.84 103.92 41.27 103.23 41.27 c 102.55 41.27 101.99 41.84 101.99 42.52 c B 106.19 42.52 m 106.19 43.21 106.75 43.77 107.44 43.77 c 108.12 43.77 108.68 43.21 108.68 42.52 c 108.68 41.84 108.12 41.27 107.44 41.27 c 106.75 41.27 106.19 41.84 106.19 42.52 c B 110.39 45.99 m 110.39 46.68 110.96 47.24 111.64 47.24 c 112.33 47.24 112.89 46.68 112.89 45.99 c 112.89 45.31 112.33 44.75 111.64 44.75 c 110.96 44.75 110.39 45.31 110.39 45.99 c B 110.39 45.99 m 110.39 46.68 110.96 47.24 111.64 47.24 c 112.33 47.24 112.89 46.68 112.89 45.99 c 112.89 45.31 112.33 44.75 111.64 44.75 c 110.96 44.75 110.39 45.31 110.39 45.99 c B 114.60 45.99 m 114.60 46.68 115.16 47.24 115.85 47.24 c 116.53 47.24 117.09 46.68 117.09 45.99 c 117.09 45.31 116.53 44.75 115.85 44.75 c 115.16 44.75 114.60 45.31 114.60 45.99 c B 114.60 49.47 m 114.60 50.15 115.16 50.71 115.85 50.71 c 116.53 50.71 117.09 50.15 117.09 49.47 c 117.09 48.78 116.53 48.22 115.85 48.22 c 115.16 48.22 114.60 48.78 114.60 49.47 c B 118.80 52.94 m 118.80 53.62 119.37 54.18 120.05 54.18 c 120.74 54.18 121.30 53.62 121.30 52.94 c 121.30 52.25 120.74 51.69 120.05 51.69 c 119.37 51.69 118.80 52.25 118.80 52.94 c B 118.80 52.94 m 118.80 53.62 119.37 54.18 120.05 54.18 c 120.74 54.18 121.30 53.62 121.30 52.94 c 121.30 52.25 120.74 51.69 120.05 51.69 c 119.37 51.69 118.80 52.25 118.80 52.94 c B 118.80 56.41 m 118.80 57.09 119.37 57.66 120.05 57.66 c 120.74 57.66 121.30 57.09 121.30 56.41 c 121.30 55.72 120.74 55.16 120.05 55.16 c 119.37 55.16 118.80 55.72 118.80 56.41 c B 118.80 56.41 m 118.80 57.09 119.37 57.66 120.05 57.66 c 120.74 57.66 121.30 57.09 121.30 56.41 c 121.30 55.72 120.74 55.16 120.05 55.16 c 119.37 55.16 118.80 55.72 118.80 56.41 c B 123.01 56.41 m 123.01 57.09 123.57 57.66 124.26 57.66 c 124.94 57.66 125.50 57.09 125.50 56.41 c 125.50 55.72 124.94 55.16 124.26 55.16 c 123.57 55.16 123.01 55.72 123.01 56.41 c B 123.01 56.41 m 123.01 57.09 123.57 57.66 124.26 57.66 c 124.94 57.66 125.50 57.09 125.50 56.41 c 125.50 55.72 124.94 55.16 124.26 55.16 c 123.57 55.16 123.01 55.72 123.01 56.41 c B 123.01 56.41 m 123.01 57.09 123.57 57.66 124.26 57.66 c 124.94 57.66 125.50 57.09 125.50 56.41 c 125.50 55.72 124.94 55.16 124.26 55.16 c 123.57 55.16 123.01 55.72 123.01 56.41 c B 123.01 56.41 m 123.01 57.09 123.57 57.66 124.26 57.66 c 124.94 57.66 125.50 57.09 125.50 56.41 c 125.50 55.72 124.94 55.16 124.26 55.16 c 123.57 55.16 123.01 55.72 123.01 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 127.21 56.41 m 127.21 57.09 127.78 57.66 128.46 57.66 c 129.15 57.66 129.71 57.09 129.71 56.41 c 129.71 55.72 129.15 55.16 128.46 55.16 c 127.78 55.16 127.21 55.72 127.21 56.41 c B 131.42 56.41 m 131.42 57.09 131.98 57.66 132.67 57.66 c 133.35 57.66 133.91 57.09 133.91 56.41 c 133.91 55.72 133.35 55.16 132.67 55.16 c 131.98 55.16 131.42 55.72 131.42 56.41 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 131.42 59.88 m 131.42 60.57 131.98 61.13 132.67 61.13 c 133.35 61.13 133.91 60.57 133.91 59.88 c 133.91 59.19 133.35 58.63 132.67 58.63 c 131.98 58.63 131.42 59.19 131.42 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 59.88 m 135.62 60.57 136.19 61.13 136.87 61.13 c 137.56 61.13 138.12 60.57 138.12 59.88 c 138.12 59.19 137.56 58.63 136.87 58.63 c 136.19 58.63 135.62 59.19 135.62 59.88 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 135.62 63.35 m 135.62 64.04 136.19 64.60 136.87 64.60 c 137.56 64.60 138.12 64.04 138.12 63.35 c 138.12 62.67 137.56 62.10 136.87 62.10 c 136.19 62.10 135.62 62.67 135.62 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 63.35 m 139.83 64.04 140.39 64.60 141.08 64.60 c 141.76 64.60 142.32 64.04 142.32 63.35 c 142.32 62.67 141.76 62.10 141.08 62.10 c 140.39 62.10 139.83 62.67 139.83 63.35 c B 139.83 66.82 m 139.83 67.51 140.39 68.07 141.08 68.07 c 141.76 68.07 142.32 67.51 142.32 66.82 c 142.32 66.14 141.76 65.58 141.08 65.58 c 140.39 65.58 139.83 66.14 139.83 66.82 c B 139.83 66.82 m 139.83 67.51 140.39 68.07 141.08 68.07 c 141.76 68.07 142.32 67.51 142.32 66.82 c 142.32 66.14 141.76 65.58 141.08 65.58 c 140.39 65.58 139.83 66.14 139.83 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 144.03 66.82 m 144.03 67.51 144.60 68.07 145.28 68.07 c 145.97 68.07 146.53 67.51 146.53 66.82 c 146.53 66.14 145.97 65.58 145.28 65.58 c 144.60 65.58 144.03 66.14 144.03 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 66.82 m 148.24 67.51 148.80 68.07 149.49 68.07 c 150.17 68.07 150.73 67.51 150.73 66.82 c 150.73 66.14 150.17 65.58 149.49 65.58 c 148.80 65.58 148.24 66.14 148.24 66.82 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 148.24 70.29 m 148.24 70.98 148.80 71.54 149.49 71.54 c 150.17 71.54 150.73 70.98 150.73 70.29 c 150.73 69.61 150.17 69.05 149.49 69.05 c 148.80 69.05 148.24 69.61 148.24 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 152.44 70.29 m 152.44 70.98 153.01 71.54 153.69 71.54 c 154.38 71.54 154.94 70.98 154.94 70.29 c 154.94 69.61 154.38 69.05 153.69 69.05 c 153.01 69.05 152.44 69.61 152.44 70.29 c B 156.65 70.29 m 156.65 70.98 157.21 71.54 157.90 71.54 c 158.58 71.54 159.14 70.98 159.14 70.29 c 159.14 69.61 158.58 69.05 157.90 69.05 c 157.21 69.05 156.65 69.61 156.65 70.29 c B 156.65 73.77 m 156.65 74.45 157.21 75.01 157.90 75.01 c 158.58 75.01 159.14 74.45 159.14 73.77 c 159.14 73.08 158.58 72.52 157.90 72.52 c 157.21 72.52 156.65 73.08 156.65 73.77 c B 156.65 73.77 m 156.65 74.45 157.21 75.01 157.90 75.01 c 158.58 75.01 159.14 74.45 159.14 73.77 c 159.14 73.08 158.58 72.52 157.90 72.52 c 157.21 72.52 156.65 73.08 156.65 73.77 c B 156.65 73.77 m 156.65 74.45 157.21 75.01 157.90 75.01 c 158.58 75.01 159.14 74.45 159.14 73.77 c 159.14 73.08 158.58 72.52 157.90 72.52 c 157.21 72.52 156.65 73.08 156.65 73.77 c B 156.65 73.77 m 156.65 74.45 157.21 75.01 157.90 75.01 c 158.58 75.01 159.14 74.45 159.14 73.77 c 159.14 73.08 158.58 72.52 157.90 72.52 c 157.21 72.52 156.65 73.08 156.65 73.77 c B 156.65 73.77 m 156.65 74.45 157.21 75.01 157.90 75.01 c 158.58 75.01 159.14 74.45 159.14 73.77 c 159.14 73.08 158.58 72.52 157.90 72.52 c 157.21 72.52 156.65 73.08 156.65 73.77 c B 156.65 73.77 m 156.65 74.45 157.21 75.01 157.90 75.01 c 158.58 75.01 159.14 74.45 159.14 73.77 c 159.14 73.08 158.58 72.52 157.90 72.52 c 157.21 72.52 156.65 73.08 156.65 73.77 c B 160.85 73.77 m 160.85 74.45 161.42 75.01 162.10 75.01 c 162.79 75.01 163.35 74.45 163.35 73.77 c 163.35 73.08 162.79 72.52 162.10 72.52 c 161.42 72.52 160.85 73.08 160.85 73.77 c B 160.85 73.77 m 160.85 74.45 161.42 75.01 162.10 75.01 c 162.79 75.01 163.35 74.45 163.35 73.77 c 163.35 73.08 162.79 72.52 162.10 72.52 c 161.42 72.52 160.85 73.08 160.85 73.77 c B 160.85 77.24 m 160.85 77.92 161.42 78.49 162.10 78.49 c 162.79 78.49 163.35 77.92 163.35 77.24 c 163.35 76.55 162.79 75.99 162.10 75.99 c 161.42 75.99 160.85 76.55 160.85 77.24 c B 160.85 77.24 m 160.85 77.92 161.42 78.49 162.10 78.49 c 162.79 78.49 163.35 77.92 163.35 77.24 c 163.35 76.55 162.79 75.99 162.10 75.99 c 161.42 75.99 160.85 76.55 160.85 77.24 c B 165.06 77.24 m 165.06 77.92 165.62 78.49 166.31 78.49 c 166.99 78.49 167.55 77.92 167.55 77.24 c 167.55 76.55 166.99 75.99 166.31 75.99 c 165.62 75.99 165.06 76.55 165.06 77.24 c B 165.06 77.24 m 165.06 77.92 165.62 78.49 166.31 78.49 c 166.99 78.49 167.55 77.92 167.55 77.24 c 167.55 76.55 166.99 75.99 166.31 75.99 c 165.62 75.99 165.06 76.55 165.06 77.24 c B 165.06 77.24 m 165.06 77.92 165.62 78.49 166.31 78.49 c 166.99 78.49 167.55 77.92 167.55 77.24 c 167.55 76.55 166.99 75.99 166.31 75.99 c 165.62 75.99 165.06 76.55 165.06 77.24 c B 169.26 80.71 m 169.26 81.40 169.83 81.96 170.51 81.96 c 171.20 81.96 171.76 81.40 171.76 80.71 c 171.76 80.02 171.20 79.46 170.51 79.46 c 169.83 79.46 169.26 80.02 169.26 80.71 c B 173.47 84.18 m 173.47 84.87 174.03 85.43 174.72 85.43 c 175.40 85.43 175.96 84.87 175.96 84.18 c 175.96 83.50 175.40 82.93 174.72 82.93 c 174.03 82.93 173.47 83.50 173.47 84.18 c B Q q 91.25 21.38 91.25 76.99 re W n Q q 96.00 26.14 81.74 67.49 re W n /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 96.00 26.14 m 177.74 93.62 l S 0.75 w [ 3.00 5.00] 0 d 96.00 32.38 m 177.74 99.87 l S 96.00 19.89 m 177.74 87.38 l S Q q /GS2 gs 0.000 0.000 0.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 96.00 28.64 m 96.00 80.71 l S 96.00 28.64 m 91.25 28.64 l S 96.00 45.99 m 91.25 45.99 l S 96.00 63.35 m 91.25 63.35 l S 96.00 80.71 m 91.25 80.71 l S BT /GS258 gs 0.000 0.000 0.000 rg /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 26.41 Tm (0) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 43.77 Tm (5) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 58.90 Tm (10) Tj /F2 1 Tf 0.00 8.00 -8.00 0.00 84.60 76.26 Tm (15) Tj ET 96.00 26.14 m 177.74 26.14 l 177.74 93.62 l 96.00 93.62 l 96.00 26.14 l S Q q 187.25 26.14 81.74 67.49 re W n Q q 187.25 26.14 81.74 67.49 re W n /GS257 gs 0.000 0.000 1.000 rg /GS1 gs 0.000 0.000 1.000 RG 0.75 w [] 0 d 1 J 1 j 10.00 M 193.23 42.52 m 193.23 43.21 193.79 43.77 194.48 43.77 c 195.17 43.77 195.73 43.21 195.73 42.52 c 195.73 41.84 195.17 41.27 194.48 41.27 c 193.79 41.27 193.23 41.84 193.23 42.52 c B 201.64 42.52 m 201.64 43.21 202.20 43.77 202.89 43.77 c 203.58 43.77 204.14 43.21 204.14 42.52 c 204.14 41.84 203.58 41.27 202.89 41.27 c 202.20 41.27 201.64 41.84 201.64 42.52 c B 201.64 42.52 m 201.64 43.21 202.20 43.77 202.89 43.77 c 203.58 43.77 204.14 43.21 204.14 42.52 c 204.14 41.84 203.58 41.27 202.89 41.27 c 202.20 41.27 201.64 41.84 201.64 42.52 c B 205.85 42.52 m 205.85 43.21 206.41 43.77 207.10 43.77 c 207.78 43.77 208.34 43.21 208.34 42.52 c 208.34 41.84 207.78 41.27 207.10 41.27 c 206.41 41.27 205.85 41.84 205.85 42.52 c B 205.85 45.99 m 205.85 46.68 206.41 47.24 207.10 47.24 c 207.78 47.24 208.34 46.68 208.34 45.99 c 208.34 45.31 207.78 44.75 207.10 44.75 c 206.41 44.75 205.85 45.31 205.85 45.99 c B 205.85 45.99 m 205.85 46.68 206.41 47.24 207.10 47.24 c 207.78 47.24 208.34 46.68 208.34 45.99 c 208.34 45.31 207.78 44.75 207.10 44.75 c 206.41 44.75 205.85 45.31 205.85 45.99 c B 210.05 45.99 m 210.05 46.68 210.61 47.24 211.30 47.24 c 211.99 47.24 212.55 46.68 212.55 45.99 c 212.55 45.31 211.99 44.75 211.30 44.75 c 210.61 44.75 210.05 45.31 210.05 45.99 c B 210.05 49.47 m 210.05 50.15 210.61 50.71 211.30 50.71 c 211.99 50.71 212.55 50.15 212.55 49.47 c 212.55 48.78 211.99 48.22 211.30 48.22 c 210.61 48.22 210.05 48.78 210.05 49.47 c B 214.26 52.94 m 214.26 53.62 214.82 54.18 215.51 54.18 c 216.19 54.18 216.75 53.62 216.75 52.94 c 216.75 52.25 216.19 51.69 215.51 51.69 c 214.82 51.69 214.26 52.25 214.26 52.94 c B 214.26 52.94 m 214.26 53.62 214.82 54.18 215.51 54.18 c 216.19 54.18 216.75 53.62 216.75 52.94 c 216.75 52.25 216.19 51.69 215.51 51.69 c 214.82 51.69 214.26 52.25 214.26 52.94 c B 214.26 56.41 m 214.26 57.09 214.82 57.66 215.51 57.66 c 216.19 57.66 216.75 57.09 216.75 56.41 c 216.75 55.72 216.19 55.16 215.51 55.16 c 214.82 55.16 214.26 55.72 214.26 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 218.46 56.41 m 218.46 57.09 219.02 57.66 219.71 57.66 c 220.40 57.66 220.96 57.09 220.96 56.41 c 220.96 55.72 220.40 55.16 219.71 55.16 c 219.02 55.16 218.46 55.72 218.46 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 56.41 m 222.67 57.09 223.23 57.66 223.92 57.66 c 224.60 57.66 225.16 57.09 225.16 56.41 c 225.16 55.72 224.60 55.16 223.92 55.16 c 223.23 55.16 222.67 55.72 222.67 56.41 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 222.67 59.88 m 222.67 60.57 223.23 61.13 223.92 61.13 c 224.60 61.13 225.16 60.57 225.16 59.88 c 225.16 59.19 224.60 58.63 223.92 58.63 c 223.23 58.63 222.67 59.19 222.67 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 59.88 m 226.87 60.57 227.43 61.13 228.12 61.13 c 228.81 61.13 229.37 60.57 229.37 59.88 c 229.37 59.19 228.81 58.63 228.12 58.63 c 227.43 58.63 226.87 59.19 226.87 59.88 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 226.87 63.35 m 226.87 64.04 227.43 64.60 228.12 64.60 c 228.81 64.60 229.37 64.04 229.37 63.35 c 229.37 62.67 228.81 62.10 228.12 62.10 c 227.43 62.10 226.87 62.67 226.87 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 63.35 m 231.08 64.04 231.64 64.60 232.32 64.60 c 233.01 64.60 233.57 64.04 233.57 63.35 c 233.57 62.67 233.01 62.10 232.32 62.10 c 231.64 62.10 231.08 62.67 231.08 63.35 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 231.08 66.82 m 231.08 67.51 231.64 68.07 232.32 68.07 c 233.01 68.07 233.57 67.51 233.57 66.82 c 233.57 66.14 233.01 65.58 232.32 65.58 c 231.64 65.58 231.08 66.14 231.08 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 235.28 66.82 m 235.28 67.51 235.84 68.07 236.53 68.07 c 237.22 68.07 237.78 67.51 237.78 66.82 c 237.78 66.14 237.22 65.58 236.53 65.58 c 235.84 65.58 235.28 66.14 235.28 66.82 c B 239.49 66.82 m 239.49 67.51 240.05 68.07 240.73 68.07 c 241.42 68.07 241.98 67.51 241.98 66.82 c 241.98 66.14 241.42 65.58 240.73 65.58 c 240.05 65.58 239.49 66.14 239.49 66.82 c B 239.49 66.82 m 239.49 67.51 240.05 68.07 240.73 68.07 c 241.42 68.07 241.98 67.51 241.98 66.82 c 241.98 66.14 241.42 65.58 240.73 65.58 c 240.05 65.58 239.49 66.14 239.49 66.82 c B 239.49 66.82 m 239.49 67.51 240.05 68.07 240.73 68.07 c 241.42 68.07 241.98 67.51 241.98 66.82 c 241.98 66.14 241.42 65.58 240.73 65.58 c 240.05 65.58 239.49 66.14 239.49 66.82 c B 239.49 66.82 m 239.49 67.51 240.05 68.07 240.73 68.07 c 241.42 68.07 241.98 67.51 241.98 66.82 c 241.98 66.14 241.42 65.58 240.73 65.58 c 240.05 65.58 239.49 66.14 239.49 66.82 c B 239.49 66.82 m 239.49 67.51 240.05 68.07 240.73 68.07 c 241.42 68.07 241.98 67.51 241.98 66.82 c 241.98 66.14 241.42 65.58 240.73 65.58 c 240.05 65.58 239.49 66.14 239.49 66.82 c B 239.49 66.82 m 239.49 67.51 240.05 68.07 240.73 68.07 c 241.42 68.07 241.98 67.51 241.98 66.82 c 241.98 66.14 241.42 65.58 240.73 65.58 c 240.05 65.58 239.49 66.14 239.49 66.82 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 241.98 69.61 241.42 69.05 240.73 69.05 c 240.05 69.05 239.49 69.61 239.49 70.29 c B 239.49 70.29 m 239.49 70.98 240.05 71.54 240.73 71.54 c 241.42 71.54 241.98 70.98 241.98 70.29 c 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00000 n 0000593476 00000 n 0000594647 00000 n 0000594762 00000 n 0000594847 00000 n 0000594917 00000 n 0000594970 00000 n trailer << /Size 269 /Root 267 0 R /Info 268 0 R /ID [ ] >> startxref 595174 %%EOF MatchIt/vignettes/makematchH0000755000176200001440000000010512162551623015602 0ustar liggesusers#!/bin/tcsh latex matchit bibtex matchit latex matchit latex matchit MatchIt/vignettes/intro.tex0000644000176200001440000000661712162551623015505 0ustar liggesusers \section{What \MatchIt\ Does} \MatchIt\ implements the suggestions of \citet*{HoImaKin07} for improving parametric statistical models and reducing model dependence by preprocessing data with semi-parametric and non-parametric matching methods. After appropriately preprocessing with \MatchIt, researchers can use whatever parametric model and software they would have used without \MatchIt, without other modification, and produce inferences that are more robust and less sensitive to modeling assumptions. (In addition, you may wish to use Zelig (\hlink{\url{http://gking.harvard.edu/zelig/}}{http://gking.harvard.edu/zelig/}; \citealt{ImaKinLau06} for subsequent parametric analyses, as it is designed to be convenient in analyzing \MatchIt\ data sets.) \MatchIt\ reduces the dependence of causal inferences on commonly made, but hard-to-justify, statistical modeling assumptions via the largest range of sophisticated matching methods of any software we know of. The program includes most existing approaches to matching and even enables users to access methods implemented in other programs through its single, unified, and easy-to-use interface. In addition, we have written \MatchIt\ so that adding new matching methods to the software is as easy for anyone with the inclination as it is for us. \section{Software Requirements} \label{sec:require} \MatchIt\ works in conjunction with the R programming language and statistical software, and will run on any platform where R is installed (Windows, Unix, or Mac OS X). R is available free for download at the Comprehensive R Archive Network (CRAN) at \hlink{http://cran.r-project.org/}{http://cran.r-project.org/}. \MatchIt\ has been tested on the most recent version of R. A good way to learn R, if you don't know it already, is to learn Zelig (available at \hlink{http://gking.harvard.edu/zelig}{http://gking.harvard.edu/zelig}) which includes a self-contained introduction to R and can be used to analyze the matched data after running \MatchIt. \section{Installing \MatchIt} \label{sec:install} To install \MatchIt\ for all platforms, type at the R command prompt, \begin{verbatim} > install.packages("MatchIt") \end{verbatim} and \MatchIt\ will install itself onto your system automatically. (During the installation process you may either decide to keep or discard the installation files, which will not affect the way \MatchIt\ runs.) \section{Loading \MatchIt} \label{sec:load} You need to install \MatchIt\ only once, but you must load it prior to each use. You can do this at the R prompt: \begin{verbatim} > library(MatchIt) \end{verbatim} Alternatively, you can specify R to load \MatchIt\ automatically at launch by editing the {\tt Rprofile} file located in the R program subdirectory, e.g. \texttt{C:/R/rw2011/etc/}, for Windows systems or the {\tt .Rprofile} file located in the home directory for Unix/Linux and Mac OS X systems, and adding this line: \begin{verbatim} options(defaultPackages = c(getOption("defaultPackages"), "MatchIt")) \end{verbatim} For this change to take effect, you need to restart R. \section{Updating \MatchIt} We recommend that you periodically update \MatchIt\ at the R prompt by typing: \begin{verbatim} > update.packages() > library(MatchIt) \end{verbatim} which will update all the libraries including \MatchIt\ and load the new version of \MatchIt. %%% Local Variables: %%% mode: pdflatex %%% TeX-master: "matchit" %%% TeX-master: t %%% End: MatchIt/vignettes/index.shtml0000755000176200001440000000637412162551623016013 0ustar liggesusers MatchIt Software Website

Daniel Ho, Kosuke Imai, Gary King, Elizabeth Stuart

"At MatchIt, we don't make parametric models, we make parametric models work better."

Version:2.2-3

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig. We're pleased to report that the article on which MatchIt is based won the Warren Miller Prize for the best paper in Political Analysis that year and, separately, has been named a Fast Breaking Paper by Thomson Reuters' ScienceWatch, for being the article with the largest percentage increase in citations among those in the top 1% of total citations across the social sciences in the last two years. (You may be interested in this interview: HTML | PDF)
MatchIt/vignettes/html.sty0000644000176200001440000003031412162551623015324 0ustar liggesusers% LaTeX2HTML Version 95.1 : html.sty % % WARNING: This file requires LaTeX2e. A LaTeX 2.09 version % is also provided, but with restricted functionality. % % This file contains definitions of LaTeX commands which are % processed in a special way by the translator. % For example, there are commands for embedding external hypertext links, % for cross-references between documents or for including % raw HTML. % This file includes the comments.sty file v2.0 by Victor Eijkhout % In most cases these commands do nothing when processed by LaTeX. % Modifications: % % nd = Nikos Drakos % jz = Jelle van Zeijl % hs = Herb Swan % hs 31-JAN-96 - Added support for document segmentation % hs 10-OCT-95 - Added \htmlrule command % jz 22-APR-94 - Added support for htmlref % nd - Created %%%%MG added \NeedsTeXFormat{LaTeX2e} \ProvidesPackage{html} [1996/02/01 v1.0 hypertext commands for latex2html (nd, hs)] %%%%MG % Exit if the style file is already loaded % (suggested by Lee Shombert \ifx \htmlstyloaded\relax \endinput\else\let\htmlstyloaded\relax\fi %%% LINKS TO EXTERNAL DOCUMENTS % % This can be used to provide links to arbitrary documents. % The first argumment should be the text that is going to be % highlighted and the second argument a URL. % The hyperlink will appear as a hyperlink in the HTML % document and as a footnote in the dvi or ps files. % \newcommand{\htmladdnormallinkfoot}[2]{#1\footnote{#2}} % This is an alternative definition of the command above which % will ignore the URL in the dvi or ps files. \newcommand{\htmladdnormallink}[2]{#1} % This command takes as argument a URL pointing to an image. % The image will be embedded in the HTML document but will % be ignored in the dvi and ps files. % \newcommand{\htmladdimg}[1]{} %%% CROSS-REFERENCES BETWEEN (LOCAL OR REMOTE) DOCUMENTS % % This can be used to refer to symbolic labels in other Latex % documents that have already been processed by the translator. % The arguments should be: % #1 : the URL to the directory containing the external document % #2 : the path to the labels.pl file of the external document. % If the external document lives on a remote machine then labels.pl % must be copied on the local machine. % %e.g. \externallabels{http://cbl.leeds.ac.uk/nikos/WWW/doc/tex2html/latex2html} % {/usr/cblelca/nikos/tmp/labels.pl} % The arguments are ignored in the dvi and ps files. % \newcommand{\externallabels}[2]{} % % This complements the \externallabels command above. The argument % should be a label defined in another latex document and will be % ignored in the dvi and ps files. % \newcommand{\externalref}[1]{} % This command adds a horizontal rule and is valid even within % a figure caption. % \newcommand{\htmlrule}{} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % The following commands pertain to document segmentation, and % were added by Herbert Swan (with help from % Michel Goossens ): % % % This command inputs internal latex2html tables so that large % documents can to partitioned into smaller (more manageable) % segments. % \newcommand{\internal}[2][internals]{} % % Define a dummy stub \htmlhead{}. This command causes latex2html % to define the title of the start of a new segment. It is not % normally placed in the user's document. Rather, it is passed to % latex2html via a .ptr file written by \segment. % \newcommand{\htmlhead}[2]{} % % The dummy command \endpreamble is needed by latex2html to % mark the end of the preamble in document segments that do % not contain a \begin{document} % \newcommand{\startdocument}{} % % Allocate a new set of section counters, which will get incremented % for "*" forms of sectioning commands, and for a few miscellaneous % commands. % \newcounter{lpart} \newcounter{lchapter}[part] \ifx\chapter\undefined\newcounter{lsection}[part]\else\newcounter{lsection}[chapter]\fi \newcounter{lsubsection}[section] \newcounter{lsubsubsection}[subsection] \newcounter{lparagraph}[subsubsection] \newcounter{lsubparagraph}[paragraph] \newcounter{lsubsubparagraph}[subparagraph] \newcounter{lequation} % % Redefine "*" forms of sectioning commands to increment their % respective counters. % \let\Hpart=\part \let\Hchapter=\chapter \let\Hsection=\section \let\Hsubsection=\subsection \let\Hsubsubsection=\subsubsection \let\Hparagraph=\paragraph \let\Hsubparagraph=\subparagraph \let\Hsubsubparagraph=\subsubparagraph % % The following definitions are specific to LaTeX2e: % (They must be commented out for LaTeX 2.09) % \def\part{\@ifstar{\stepcounter{lpart}\Hpart*}{\Hpart}} \def\chapter{\@ifstar{\stepcounter{lchapter}\Hchapter*}{\Hchapter}} \def\section{\@ifstar{\stepcounter{lsection}\Hsection*}{\Hsection}} \def\subsection{\@ifstar{\stepcounter{lsubsection}\Hsubsection*}{\Hsubsection}} \def\subsubsection{\@ifstar{\stepcounter{lsubsubsection}\Hsubsubsection*}{\Hsubsubsection}} \def\paragraph{\@ifstar{\stepcounter{lparagraph}\Hparagraph*}{\Hparagraph}} \def\subparagraph{\@ifstar{\stepcounter{lsubparagraph}\Hsubparagraph*}{\Hsubparagraph}} \def\subsubparagraph{\@ifstar{\stepcounter{lsubsubparagraph}\Hsubsubparagraph*}{\Hsubsubparagraph}} % % Define a helper macro to dump a single \secounter command to a file. % \newcommand{\DumpPtr}[2]{% \count255=\arabic{#1} \advance\count255 by \arabic{#2} \immediate\write\ptrfile{% \noexpand\setcounter{#1}{\number\count255}}} % % Define a helper macro to dump all counters to the file. % The value for each counter will be the sum of the l-counter % actual LaTeX section counter. % Also dump an \htmlhead{section-command}{section title} command % to the file. % \def\DumpCounters#1#2#3{\newwrite\ptrfile \immediate\openout\ptrfile = #1.ptr \DumpPtr{part}{lpart} \ifx\Hchapter\undefined\relax\else\DumpPtr{chapter}{lchapter}\fi \DumpPtr{section}{lsection} \DumpPtr{subsection}{lsubsection} \DumpPtr{subsubsection}{lsubsubsection} \DumpPtr{paragraph}{lparagraph} \DumpPtr{subparagraph}{lsubparagraph} \DumpPtr{equation}{lequation} \immediate\write\ptrfile{\noexpand\htmlhead{#2}{#3}} \immediate\closeout\ptrfile} % % Define the \segment{file}{section-command}{section-title} command, % and its helper macros. This command does four things: % 1) Begins a new LaTeX section; % 2) Writes a list of section counters to file.ptr, each % of which represents the sum of the LaTeX section % counters, and the l-counters, defined above; % 3) Write an \htmlhead{section-title} command to file.ptr; % 4) Inputs file.tex. % %%%%MG changed \def\segment{\@ifstar{\@@htmls}{\@@html}} \def\@@htmls#1#2#3{\csname #2\endcsname* {#3}% \DumpCounters{#1}{#2*}{#3}\input{#1}} \def\@@html#1#2#3{\csname #2\endcsname {#3}% \DumpCounters{#1}{#2}{#3}\input{#1}} %%%%MG %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Comment.sty version 2.0, 19 June 1992 % selectively in/exclude pieces of text: the user can define new % comment versions, and each is controlled separately. % This style can be used with plain TeX or LaTeX, and probably % most other packages too. % % Examples of use in LaTeX and TeX follow \endinput % % Author % Victor Eijkhout % Department of Computer Science % University Tennessee at Knoxville % 104 Ayres Hall % Knoxville, TN 37996 % USA % % eijkhout@cs.utk.edu % % Usage: all text included in between % \comment ... \endcomment % or \begin{comment} ... \end{comment} % is discarded. The closing command should appear on a line % of its own. No starting spaces, nothing after it. % This environment should work with arbitrary amounts % of comment. % % Other 'comment' environments are defined by % and are selected/deselected with % \includecomment{versiona} % \excludecoment{versionb} % % These environments are used as % \versiona ... \endversiona % or \begin{versiona} ... \end{versiona} % with the closing command again on a line of its own. % % Basic approach: % to comment something out, scoop up every line in verbatim mode % as macro argument, then throw it away. % For inclusions, both the opening and closing comands % are defined as noop % % Changed \next to \html@next to prevent clashes with other sty files % (mike@emn.fr) % Changed \html@next to \htmlnext so the \makeatletter and % \makeatother commands could be removed (they were causing other % style files - changebar.sty - to crash) (nikos@cbl.leeds.ac.uk) % Changed \htmlnext back to \html@next... \makeatletter \def\makeinnocent#1{\catcode`#1=12 } \def\csarg#1#2{\expandafter#1\csname#2\endcsname} \def\ThrowAwayComment#1{\begingroup \def\CurrentComment{#1}% \let\do\makeinnocent \dospecials \makeinnocent\^^L% and whatever other special cases \endlinechar`\^^M \catcode`\^^M=12 \xComment} {\catcode`\^^M=12 \endlinechar=-1 % \gdef\xComment#1^^M{\def\test{#1} \csarg\ifx{PlainEnd\CurrentComment Test}\test \let\html@next\endgroup \else \csarg\ifx{LaLaEnd\CurrentComment Test}\test \edef\html@next{\endgroup\noexpand\end{\CurrentComment}} \else \let\html@next\xComment \fi \fi \html@next} } \makeatother \def\includecomment #1{\expandafter\def\csname#1\endcsname{}% \expandafter\def\csname end#1\endcsname{}} \def\excludecomment #1{\expandafter\def\csname#1\endcsname{\ThrowAwayComment{#1}}% {\escapechar=-1\relax \csarg\xdef{PlainEnd#1Test}{\string\\end#1}% \csarg\xdef{LaLaEnd#1Test}{\string\\end\string\{#1\string\}}% }} \excludecomment{comment} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% RAW HTML % % Enclose raw HTML between a \begin{rawhtml} and \end{rawhtml}. % The html environment ignores its body % \excludecomment{rawhtml} %%% HTML ONLY % % Enclose LaTeX constructs which will only appear in the % HTML output and will be ignored by LaTeX with % \begin{htmlonly} and \end{htmlonly} % \excludecomment{htmlonly} % Shorter version \newcommand{\html}[1]{} %%% LaTeX ONLY % Enclose LaTeX constructs which will only appear in the % DVI output and will be ignored by latex2html with %\begin{latexonly} and \end{latexonly} % \newenvironment{latexonly}{}{} % Shorter version \newcommand{\latex}[1]{#1} %%% HYPERREF % Suggested by Eric M. Carol % Similar to \ref but accepts conditional text. % The first argument is HTML text which will become ``hyperized'' % (underlined). % The second and third arguments are text which will appear only in the paper % version (DVI file), enclosing the fourth argument which is a reference to a label. % %e.g. \hyperref{using the tracer}{using the tracer (see Section}{)}{trace} % where there is a corresponding \label{trace} % \newcommand{\hyperref}[4]{#2\ref{#4}#3} %%% HTMLREF % Reference in HTML version only. % Mix between \htmladdnormallink and \hyperref. % First arg is text for in both versions, second is label for use in HTML % version. \newcommand{\htmlref}[2]{#1} %%% HTMLIMAGE % This command can be used inside any environment that is converted % into an inlined image (eg a "figure" environment) in order to change % the way the image will be translated. The argument of \htmlimage % is really a string of options separated by commas ie % [scale=],[external],[thumbnail= % The scale option allows control over the size of the final image. % The ``external'' option will cause the image not to be inlined % (images are inlined by default). External images will be accessible % via a hypertext link. % The ``thumbnail'' option will cause a small inlined image to be % placed in the caption. The size of the thumbnail depends on the % reduction factor. The use of the ``thumbnail'' option implies % the ``external'' option. % % Example: % \htmlimage{scale=1.5,external,thumbnail=0.2} % will cause a small thumbnail image 1/5th of the original size to be % placed in the final document, pointing to an external image 1.5 % times bigger than the original. % \newcommand{\htmlimage}[1]{} %%% HTMLADDTONAVIGATION % This command appends its argument to the buttons in the navigation % panel. It is ignored by LaTeX. % % Example: % \htmladdtonavigation{\htmladdnormallink % {\htmladdimg{http://server/path/to/gif}} % {http://server/path}} \newcommand{\htmladdtonavigation}[1]{} \endinputMatchIt/vignettes/graphics.R0000644000176200001440000000365712162551623015554 0ustar liggesusers# File to create sample graphics and output for documentation library(MatchIt) library(lattice) data(lalonde) m.out <- matchit(treat ~ re74+re75+educ+black+hispan+age, data=lalonde, method="nearest") print(summary(m.out)) ps.options(family = c("Times"), pointsize = 8) postscript(file="figs/qqplotnn1.eps", horizontal=FALSE, paper="special", width=2.75, height=2.75) par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5, mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2)) plot(m.out, which.xs=c("re74", "re75", "educ"), interactive=FALSE) dev.off() pdf(file="figs/qqplotnn1.pdf", width=2.75, height=2.75, pointsize=8, family="Times") par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5, mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2)) plot(m.out, which.xs=c("re74", "re75", "educ"), interactive=FALSE) dev.off() postscript(file="figs/qqplotnn2.eps", horizontal=FALSE, paper="special", width=2.75, height=2.75) par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5, mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2)) plot(m.out, which.xs=c("black", "hispan", "educ"), interactive=FALSE) dev.off() pdf(file="figs/qqplotnn2.pdf", width=2.75, height=2.75, pointsize=8, family="Times") par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5, mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white", mfrow=c(1,2)) plot(m.out, which.xs=c("black", "hispan", "educ"), interactive=FALSE) dev.off() postscript(file="figs/jitterplotnn.eps", horizontal=FALSE, paper="special", width=5.5, height=3.5) par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5, mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white") plot(m.out, type="jitter", interactive=FALSE) dev.off() pdf(file="figs/jitterplotnn.pdf", width=5.5, height=3.5, pointsize=8, family="Times") par(mar=c(2, 2, 2, 2) + 0.1, cex.lab=0.7, cex.axis=0.5, mgp=c(1,0.5,0), cex.main=0.8, cex=1, bg="white") plot(m.out, type="jitter", interactive=FALSE) dev.off() MatchIt/vignettes/gkpubs.bib0000644000176200001440000021174612162551623015602 0ustar liggesusers% A bibtex-format file for papers by or coauthored with Gary King % % rules used for abbreviations: % % -if one author: use last name and last 2 digits of the year: King99. % -if multiple authors, use 1st 3 letters of each of UP TO the first three % authors and the last 2 digits of the year: KinTomWit00. % -if necessary add lower-case letters for multiple entries in a year: % King02, King02b (the first one should NOT have an 'a' afterwards) % -No string abbreviations are used % % entries are in separate sections (books, articles, software, data) % in reverse chronical order % % copies of all papers, articles, data, and software, and some books, % are available at http://gking.harvard.edu/ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Books %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @book{KinSchNie09, editor = {Gary King and Kay Schlozman and Norman Nie}, title = {The Future of Political Science: 100 Perspectives}, publisher = {Routledge Press}, address = {New York}, year = {2009} } @book{GirKin08, author = {Federico Girosi and Gary King}, title = {Demographic Forecasting}, publisher = {Princeton University Press}, year = {2008}, address = {Princeton}, note = {{http://gking.harvard.edu/files/smooth/}} } @book{KinRosTan04, editor = {Gary King and Ori Rosen and Martin A. Tanner}, title = {Ecological Inference: New Methodological Strategies}, publisher = {Cambridge University Press}, year = {2004}, address = {New York}, note = {{http://gking.harvard.edu/files/abs/ecinf04-abs.shtml}} } @book{King97, author = {Gary King}, title = {A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data}, publisher = {Princeton University Press}, year = {1997}, address = {Princeton}, note = {{http://gking.harvard.edu/eicamera/kinroot.html}} } @book{KinKeoVer94, author = {Gary King and Robert O. Keohane and Sidney Verba}, title = {Designing Social Inquiry: Scientific Inference in Qualitative Research}, publisher = {Princeton University Press}, year = {1994}, address = {Princeton}, note = {{http://www.pupress.princeton.edu/titles/5458.html}} } @book{King89, author = {Gary King}, title = {Unifying Political Methodology: The Likelihood Theory of Statistical Inference}, publisher = {Michigan University Press}, year = 1989, address = {Ann Arbor} } @book{KinRag88, author = {Gary King and Lyn Ragsdale}, title = {The Elusive Executive: Discovering Statistical Patterns in the Presidency}, publisher = {Congressional Quarterly Press}, year = {1988}, address = {Washington, D.C} } @book{BraHarKin89, author = {Paul Brace and Christine Harrington and Gary King}, title = {The Presidency in American Politics}, publisher = {New York University Press}, year = {1989}, address = {New York and London} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Articles %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{SonKin11, author = {Samir Soneji and Gary King}, title = {Statistical Security for Social Security}, journal = {Demography}, year = 2011, note = {{http://gking.harvard.edu/files/abs/ssc-abs.shtml}} } @Article{, author = {Gary King and Richard Nielsen and Carter Coberley and James Pope and Aaron Wells}, title = {Avoiding Randomization Failure in Program Evaluation}, journal = {Population Health Management}, year = 2011, volume = 14, number = 1, pages = {S11-S22}, note = {{http://gking.harvard.edu/gking/files/mhs.pdf}} } @Article{KinNieCob11, author = {Gary King and Richard Nielsen and Carter Coberley and James Pope and Aaron Wells}, title = {Comparative Effectiveness of Matching Methods for Causal Inference}, journal = { }, year = {2011}, OPTkey = {}, OPTvolume = {}, OPTnumber = {}, OPTpages = {}, OPTmonth = {}, OPTnote = {{http://gking.harvard.edu/files/abs/psparadox-abs.shtml}}, OPTannote = {} } @Article{SteKinShi10, author = {Gretchen Stevens, Gary King, and Kenji Shibuya}, title = {Deaths From Heart Failure: Using Coarsened Exact Matching to Correct Cause of Death Statistics}, journal = {Population Health Metrics}, year = 2010, volume = 8, number = 6, note = {{http://gking.harvard.edu/files/abs/heartfcem-abs.shtml}} } @Article{GriKin10, author = {Justin Grimmer and Gary King}, title = {Quantitative Discovery from Qualitative Information: A General-Purpose Document Clustering Methodology}, journal = { }, year = {2010}, note = {{http://gking.harvard.edu/files/abs/discov-abs.shtml}} } @Article{GriKin10b, author = {Justin Grimmer and Gary King}, title = {A General Purpose Computer-Assisted Document Clustering Methodology}, journal = { }, year = {2010}, note = {{http://gking.harvard.edu/files/abs/discovm-abs.shtml}} } @Article{IacKinPor11, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {Multivariate Matching Methods That are Monotonic Imbalance Bounding}, journal = {Journal of the American Statistical Association}, year = {In press}, note = {{http://gking.harvard.edu/files/abs/cem-math-abs.shtml}} } @article{HopKin10, author = {Daniel Hopkins and Gary King}, title = {Improving Anchoring Vignettes: Designing Surveys to Correct Interpersonal Incomparability}, journal = {Public Opinion Quarterly}, year = {2010}, pages = {1-22}, note = {{http://gking.harvard.edu/files/abs/implement-abs.shtml}} } @Article{KinLuShi10, author = {Gary King and Ying Lu and Kenji Shibuya}, title = {Designing Verbal Autopsy Studies}, journal = {Population Health Metrics}, year = 2010, volume = 8, number = 19, note = {http://gking.harvard.edu/files/abs/desva-abs.shtml} } @article{LazPenAda09, author = {Lazer, David and Pentland, Alex and Adamic, Lada and Aral, Sinan and Barabasi, Albert-Laszlo and Brewer, Devon and Christakis, Nicholas and Contractor, Noshir and Fowler, James and Gutmann, Myron and Jebara, Tony and King, Gary and Macy, Michael and Roy, Deb and Van Alstyne, Marshall}, title = {{SOCIAL SCIENCE: Computational Social Science}}, journal = {Science}, volume = {323}, number = {5915}, pages = {721-723}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/LazPenAda09-abs.shtml}} } @Article{AbrBolGut09, author = {Mark Abrahamson and Kenneth A. Bollen and Myron Gutmann and Gary King and Amy M. Pienta}, title = {Preserving Data for Long Term Analyses}, journal = {Historical Social Research}, year = {2009}, OPTkey = {}, OPTvolume = {}, OPTnumber = {}, OPTpages = {}, OPTmonth = {Summer, forthcoming}, OPTnote = {}, OPTannote = {} } @article{KinGakIma09, Author = {Gary King and Emmanuela Gakidou and Kosuke Imai and Jason Lakin and Ryan T. Moore and Clayton Nall and Nirmala Ravishankar and Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas}, title = {Public Policy for the Poor? A Randomised Assessment of the Mexican Universal Health Insurance Programme}, journal = {The Lancet}, volumne = {373}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/spi-abs.shtml}} } @Article{KinSon09, author = {Gary King and Samir Soneji}, title = {The Future of Death in America}, journal = {}, year = 2009, note = {{http://gking.harvard.edu/files/abs/mort-abs.shtml}} } @Article{IacKinPor11, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {Causal Inference Without Balance Checking: Coarsened Exact Matching}, journal = {Political Analysis}, year = {2011, in press}, note = {{http://gking.harvard.edu/files/abs/cem-plus-abs.shtml}} } @article{ImaKinStu08, author = {Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Misunderstandings Among Experimentalists and Observationalists about Causal Inference}, journal = {Journal of the Royal Statistical Society, {S}eries {A}}, volume = {171, part 2}, year = {2008}, pages = {481--502}, note = {{http://gking.harvard.edu/files/abs/matchse-abs.shtml}} } @article{uImaKinStu08, author = {Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Misunderstandings Among Experimentalists and Observationalists about Causal Inference}, journal = {Journal of the Royal Statistical Society, {S}eries {A}}, volume = {171, part 2}, year = {2008}, pages = {481--502} } @article{KinLu08, author = {Gary King and Ying Lu}, title = {Verbal Autopsy Methods with Multiple Causes of Death}, journal = {Statistical Science}, volume = {23}, number = {1}, year = {2008}, pages = {78--91}, note = {{http://gking.harvard.edu/files/abs/vamc-abs.shtml}} } @article{AltKin07, author = {Micah Altman and Gary King}, title = {A Proposed Standard for the Scholarly Citation of Quantitative Data }, journal = {D-Lib Magazine}, volume = {13}, year = {2007}, month = {March / April}, number = {3/4}, note = {{http://gking.harvard.edu/files/abs/cite-abs.shtml}} } @article{GroKin07, author = {Bernard Grofman and Gary King}, title = {The Future of Partisan Symmetry as a Judicial Test for Partisan Gerrymandering after LULAC v. Perry}, journal = {Election Law Journal}, volume = {6}, year = {2007}, pages = {2-35}, month = {January}, number = {1}, note = {{http://gking.harvard.edu/files/abs/jp-abs.shtml}} } @article{uHoImaKin07, author = {Daniel Ho and Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference}, journal = {Political Analysis}, year = {2007}, volume = {15}, pages = {199--236} } @article{HoImaKin07, author = {Daniel Ho and Kosuke Imai and Gary King and Elizabeth Stuart}, title = {Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference}, journal = {Political Analysis}, year = {2007}, volume = {15}, pages = {199--236}, note = {{http://gking.harvard.edu/files/abs/matchp-abs.shtml}} } @article{HopKin10b, author = {Daniel Hopkins and Gary King}, title = {A Method of Automated Nonparametric Content Analysis for Social Science}, journal = {American Journal of Political Science}, year = {2010}, volume = {54}, number = {1}, month = {January}, pages = {229--247}, note = {http://gking.harvard.edu/files/abs/words-abs.shtml} } @article{ImaKinLau07, author = {Kosuke Imai and Gary King and Olivia Lau}, title = {Toward A Common Framework for Statistical Analysis and Development}, journal = {Journal of Computational Graphics and Statistics}, volume = 17, number = 4, pages = {1--22}, year = 2008, note = {{http://gking.harvard.edu/files/abs/z-abs.shtml}} } @article{ImaKinNal09d, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {Matched Pairs and the Future of Cluster-Randomized Experiments: A Rejoinder}, journal = {Statistical Science}, volume = {24}, number = {1}, pages = {64--72}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/cluster-abs.shtml}} } @article{ImaKinNal09, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation}, journal = {Statistical Science}, volume = {24}, number = {1}, pages = {29--53}, year = {2009}, note = {{http://gking.harvard.edu/files/abs/cluster-abs.shtml}} } @article{uImaKinNal09, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation}, journal = {Statistical Science}, volume = {24}, number = {1}, pages = {29--53}, year = {2009} } @article{King07, author = {Gary King}, title = {An Introduction to the Dataverse Network as an Infrastructure for Data Sharing}, journal = {Sociological Methods and Research}, year = {2007}, volume = {36}, number = {2}, pages = {173--199}, note = {{http://gking.harvard.edu/files/abs/dvn-abs.shtml}} } @inbook{King09b, author = {Gary King}, chapter = {The Changing Evidence Base of Political Science Research}, title = {The Future of Political Science: 100 Perspectives}, publisher = {Routledge Press}, address = {New York}, year = {2009, forthcoming}, editor = {Gary King and Kay Schlozman and Norman Nie}, note = {{http://gking.harvard.edu/files/abs/evbase-abs.shtml}} } @inbook{King09c, author = {Gary King and Kay Schlozman and Norman Nie}, chapter = {An Introduction to the Future of Political Science}, title = {The Future of Political Science: 100 Perspectives}, publisher = {Routledge Press}, address = {New York}, year = {2009, forthcoming}, editor = {Gary King and Kay Schlozman and Norman Nie} } @article{KinGakRav07, Author = {Gary King and Emmanuela Gakidou and Nirmala Ravishankar and Ryan T. Moore and Jason Lakin and Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas}, title = {A `Politically Robust' Experimental Design for Public Policy Evaluation, with Application to the Mexican Universal Health Insurance Program}, journal = {Journal of Policy Analysis and Management}, volume = {26}, year = {2007}, pages = {479-506}, number = {3}, note = {{http://gking.harvard.edu/files/abs/spd-abs.shtml}} } @article{KinWan07, author = {Gary King and Jonathan Wand}, title = {Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes}, journal = {Political Analysis}, volume = {15}, year = {2007}, pages = {46-66}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/c-abs.shtml}} } @article{KinZen07, author = {Gary King and Langche Zeng}, title = {When Can History Be Our Guide? The Pitfalls of Counterfactual Inference}, journal = {International Studies Quarterly}, year = {2007}, pages = {183-210}, month = {March}, note = {{http://gking.harvard.edu/files/abs/counterf-abs.shtml}} } @article{uKinZen07, author = {Gary King and Langche Zeng}, title = {When Can History Be Our Guide? The Pitfalls of Counterfactual Inference}, journal = {International Studies Quarterly}, year = {2007}, pages = {183-210}, month = {March} } @article{KinZen07b, author = {Gary King and Langche Zeng}, title = {Detecting Model Dependence in Statistical Inference: A Response}, journal = {International Studies Quarterly}, volume = {51}, year = {2007}, pages = {231-241}, month = {March}, note = {{http://gking.harvard.edu/files/abs/counterf-abs.shtml}} } @article{WanKinLau07, author = {Jonathan Wand and Gary King and Olivia Lau}, title = {Anchors: Software for Anchoring Vignettes Data}, journal = {Journal of Statistical Software}, year = {2007, forthcoming} } @inbook{EpsHoKin06, author = {Lee Epstein and Daniel E. Ho and Gary King and Jeffrey A. Segal}, title = {Principles and Practice in American Politics: Classic and Contemporary Readings}, chapter = {The Effect of War on the Supreme Court}, year = {2006}, publisher = {Congressional Quarterly Press}, edition = {3rd}, address = {Washington, D.C.}, editor = {Samuel Kernell and Steven S. Smith}, note = {{http://gking.harvard.edu/files/abs/crisis-abs.shtml}} } @article{GakKin06, author = {Emmanuela Gakidou and Gary King}, title = {Death by Survey: Estimating Adult Mortality without Selection Bias from Sibling Survival Data from Sibling Survival Data}, journal = {Demography}, volume = 43, year = 2006, pages = {569--585}, month = {August}, number = 3, note = {{http://gking.harvard.edu/files/abs/deathbys-abs.shtml}} } @article{HonKin10, author = {James Honaker and Gary King}, title = {What to do About Missing Values in Time Series Cross-Section Data}, journal = {American Journal of Political Science}, year = {2010}, volume = {54}, number = {2}, month = {April}, pages = {561--581}, note = {{http://gking.harvard.edu/files/abs/pr-abs.shtml}} } @article{King06, author = {Gary King}, title = {{Publication, Publication}}, journal = {PS: Political Science and Politics}, volume = {39}, year = {2006}, pages = {119--125}, month = {January}, number = {01}, note = {{http://gking.harvard.edu/files/abs/paperspub-abs.shtml}} } @inbook{KinRosTan06, author = {Gary King and Ori Rosen and Martin Tanner}, title = {The New Palgrave Dictionary of Economics}, chapter = {Ecological Inference}, year = {2006}, edition = {2nd}, editor = {Larry Blume and Steven N. Durlauf}, note = {{http://gking.harvard.edu/files/abs/newintro-abs.shtml}} } @article{KinZen06, author = {Gary King and Langche Zeng}, title = {The Dangers of Extreme Counterfactuals}, journal = {Political Analysis}, volume = {14}, year = 2006, pages = {131--159}, number = {2}, note = {{http://gking.harvard.edu/files/abs/counterft-abs.shtml}} } @article{GirKin07, author = {Federico Girosi and Gary King}, title = {Understanding the Lee-Carter Mortality Forecasting Method}, year = 2007, note = {{http://gking.harvard.edu/files/abs/lc-abs.shtml}} } @article{EpsHoKin05, author = {Lee Epstein and Daniel E. Ho and Gary King, and Jeffrey A. Segal}, title = {The Supreme Court During Crisis: How War Affects only Non-War Cases}, journal = {New York University Law Review}, volume = {80}, year = {2005}, pages = {1--116}, month = {April}, number = {1}, note = {{http://gking.harvard.edu/files/abs/crisis-abs.shtml}} } @article{StoKinZen05, author = {Heather Stoll and Gary King and Langchee Zeng}, title = {WhatIf: Software for Evaluating Counterfactuals}, journal = {Journal of Statistical Software}, volume = {15}, year = {2005}, number = {4}, note = {{http://www.jstatsoft.org/index.php?vol=15}} } @article{BecKinZen04, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Theory and Evidence in International Conflict: Response to de Marchi, Gelpi, and Grynaviski}, journal = apsr, volume = {98}, year = {2004}, pages = {379-389}, month = {May}, number = {2}, note = {{http://gking.harvard.edu/files/abs/toe-resp-abs.shtml}} } @inbook{GelKatKin04, author = {Andrew Gelman and Jonathan Katz and Gary King}, title = {Rethinking the Vote: The Politics and Prospects of American Electoreal Reform}, chapter = {Chapter 5, Empirically Evaluating the Electoral College}, year = {2004}, publisher = {Oxford University Press}, pages = {75-88}, address = {New York}, editor = {Ann N. Crigler and Marion R. Just and Edward J. McCaffery}, note = {{http://gking.harvard.edu/files/abs/rethink-abs.shtml}} } @article{GilKin04, author = {Jeff Gill and Gary King}, title = {What to do When Your Hessian is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation}, journal = {Sociological Methods and Research}, volume = {32}, year = {2004}, pages = {54-87}, month = {August}, number = {1}, note = {{http://gking.harvard.edu/files/abs/help-abs.shtml}} } @article{ImaKin04, author = {Kosuke Imai and Gary King}, title = {Did Illegal Overseas Absentee Ballots Decide the 2000 U.S. Presidential Election?}, journal = {Perspectives on Politics}, volume = {2}, year = {2004}, pages = {537--549}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/ballots-abs.shtml}} } @article{King04, author = {Gary King}, title = {EI: A Program for Ecological Inference}, journal = {Journal of Statistical Software}, volume = {11}, year = {2004}, number = {7} } @article{King04b, author = {Gary King}, title = {Finding New Information for Ecological Inference Models: A Comment on Jon Wakefield, "Ecological Inference in 2 X 2 Tables"}, journal = {Journal of the Royal Statistical Society}, volume = {167}, year = {2004}, pages = {437}, number = {Series A} } @inbook{KinRosTan04b, author = {Gary King and Ori Rosen and Martin Tanner}, title = {Ecological Inference: New Methodological Strategies}, chapter = {Information in Ecological Inference: An Introduction}, year = {2004}, publisher = {Cambridge University Press}, address = {New York}, editor = {Gary King and Ori Rosen and Martin Tanner} } @inbook{KinZen04, author = {Gary King and Langche Zeng}, title = {Encyclopedia of Biopharmaceutical Statistics}, chapter = {Inference in Case-Control Studies}, year = {2004}, publisher = {Marcel Dekker}, edition = {2nd}, address = {New York}, editor = {Shein-Chung Chow}, note = {{http://gking.harvard.edu/files/abs/1s-enc-abs.shtml}} } @article{AdoKin03, author = {Christopher Adolph and Gary King}, title = {Analyzing Second Stage Ecological Regressions}, journal = {Political Analysis}, volume = {11}, year = {2003}, pages = {65-76}, month = {Winter}, number = {1} } @article{AdoKinHer03, author = {Christopher Adolph and Gary King, with Michael C. Herron and Kenneth W. Shotts}, title = {A Consensus on Second Stage Analyses in Ecological Inference Models}, journal = {Political Analysis}, volume = {11}, year = {2003}, pages = {86--94}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/akhs-abs.shtml}} } @article{EpsKin03, author = {Lee Epstein and Gary King}, title = {Building An Infrastructure for Empirical Research in the Law [with comments from four law school deans]}, journal = {Journal of Legal Education}, volume = {53}, year = {2003}, pages = {311--320}, number = {311}, note = {{http://gking.harvard.edu/files/abs/infra-abs.shtml}} } @inbook{GakKin03, author = {Emmanuela Gakidou and Gary King}, title = {Health Systems Performance Assessment: Debates, Methods and Empiricism}, chapter = {Chapter 36, Determinants of Inequality in Child Survival: Results from 39 Countries}, publisher = {World Health Organization}, pages = {497-502}, address = {Geneva}, editor = {Chrisopher Murray and David B. Evans} } @inbook{GilKin03, author = {Jeff Gill and Gary King}, title = {Numerical Issues in Statistical Computing for the Social Scientist}, chapter = {Chapter 6, Numerical Issues Involved in Inverting Hessian Matrices}, year = {2003}, publisher = {John Wiley and Sons, Inc.}, pages = {143-176}, address = {Hoboken, NJ}, editor = {Micah Altman and Jeff Gill and Michael P. McDonald} } @article{King03, author = {Gary King}, title = {The Future of Replication}, journal = {International Studies Perspectives}, volume = {4}, year = {2003}, pages = {443--499}, month = {February}, number = 1, note = {{http://gking.harvard.edu/files/abs/replvdc-abs.shtml}} } @article{KinLow03, author = {Gary King and Will Lowe}, title = {An Automated Information Extraction Tool For International Conflict Data with Performance as Good as Human Coders: A Rare Events Evaluation Design}, journal = {International Organization}, volume = {57}, year = {2003}, pages = {617-642}, month = {July}, number = {3}, note = {{http://gking.harvard.edu/files/abs/infoex-abs.shtml}} } @article{KinMurSal04, author = {Gary King and Christopher J.L. Murray and Joshua A. Salomon and Ajay Tandon}, title = {Enhancing the Validity and Cross-cultural Comparability of Measurement in Survey Research}, journal = {American Political Science Review}, volume = {98}, year = {2004}, pages = {191--207}, month = {February}, number = {1}, note = {{http://gking.harvard.edu/files/abs/vign-abs.shtml}} } @article{KinRosTan07, author = {Gary King and Ori Rosen and Martin Tanner and Alexander F. Wagner.}, title = {Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf Hitler}, year = {2007}, note = {{http://gking.harvard.edu/files/abs/naziV-abs.shtml}} } @inbook{KinZen03, author = {Gary King and Langche Zeng}, title = {Inference in Case-Control Studies}, year = {2003}, publisher = {Marcel Dekker}, volume = {2nd edition}, address = {New York}, editor = {Shein-Chung Chow, ed.}, journal = {Encyclopedia of Biopharmaceutical Statistics} } @article{LowKin03, author = {Will Lowe and Gary King }, title = {Some Statistical Methods for Evaluating Information Extraction Systems}, journal = {Proceedings of the 10th Conference of the European Chapter of the Association for Computational Linguistics}, year = {2003}, pages = {19-26} } @article{TomKinZen03, author = {Michael Tomz and Gary King and Langche Zeng}, title = {ReLogit: Rare Events Logistic Regression}, journal = {Journal of Statistical Software}, volume = {8}, year = {2003}, number = {2}, note = {{http://gking.harvard.edu/stats.shtml#relogit}} } @article{TomWitKin03, author = {Michael Tomz and Jason Wittenberg and Gary King}, title = {CLARIFY: Software for Interpreting and Presenting Statistical Results}, journal = {Journal of Statistical Software}, volume = {8}, year = {2003}, number = {1}, note = {{http://gking.harvard.edu/stats.shtml}} } @article{EpsKin02, author = {Lee Epstein and Gary King}, title = {The Rules of Inference}, journal = {University of Chicago Law Review}, volume = {69}, year = {2002}, pages = {1--209}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/rules-abs.shtml}} } @article{EpsKin02b, author = {Lee Epstein and Gary King}, title = {Empirical Research and The Goals of Legal Scholarship: A Response}, journal = {University of Chicago Law Review}, volume = {69}, year = {2002}, pages = {1--209}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/rules-abs.shtml}} } @article{GakKin02, author = {Emmanuela Gakidou and Gary King}, title = {Measuring Total Health Inequality: Adding Individual Variation to Group-Level Differences}, journal = {BioMed Central: International Journal for Equity in Health}, volume = {1}, year = 2002, month = {August}, number = 3, note = {{http://gking.harvard.edu/files/abs/ebb-abs.shtml}} } @article{HonKinKat02, author = {James Honaker and Gary King and Jonathan N. Katz}, title = {A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data}, journal = {Political Analysis}, volume = {10}, year = {2002}, pages = {84--100}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/trip-abs.shtml}} } @article{King02b, author = {Gary King}, title = {Isolating Spatial Autocorrelation, Aggregation Bias, and Distributional Violations in Ecological Inference}, journal = {Political Analysis}, volume = {10}, year = {2002}, pages = {298--300}, month = {Summer}, number = {3}, note = {{http://gking.harvard.edu/files/abs/ac-abs.shtml}} } @article{KinMur02, author = {Gary King and Christopher J.L. Murray}, title = {Rethinking Human Security}, journal = {Political Science Quarterly}, volume = {116}, year = {2002}, pages = {585--610}, month = {Winter}, number = {4}, note = {{http://gking.harvard.edu/files/abs/hs-abs.shtml}} } @article{KinZen02, author = {Gary King and Langche Zeng}, title = {Improving Forecasts of State Failure}, journal = {World Politics}, volume = 53, year = 2002, pages = {623--658}, month = {July}, number = 4 , note = {{http://gking.harvard.edu/files/abs/civil-abs.shtml}} } @article{KinZen02b, author = {Gary King and Langche Zeng}, title = {Estimating Risk and Rate Levels, Ratios, and Differences in Case-Control Studies}, journal = {Statistics in Medicine}, volume = 21, year = 2002, pages = {1409--1427}, note = {{http://gking.harvard.edu/files/abs/1s-abs.shtml}} } @article{MurKinLop02, author = {Christopher J.L. Murray and Gary King and Alan D. Lopez and Niels Tomijima and Etienne Krug}, title = {Armed Conflict as a Public Health Problem}, journal = {BMJ (British Medical Journal)}, volume = {324}, year = {2002}, pages = {346--349}, month = {February 9}, note = {{http://gking.harvard.edu/files/abs/armedph-abs.shtml}} } @article{AltAndDig01a, author = {Micah Altman and Leonid Andreev and Mark Diggory and Gary King and Daniel L. Kiskis and Elizabeth Kolster and M. Krot and Sidney Verba}, title = {A Digital Library for the Dissemination and Replication of Quantitative Social Science Research: The Virtual Data Center}, journal = {Social Science Computer Review}, volume = 19, year = 2001, pages = {458--470}, month = {Winter}, number = 4, note = {{http://gking.harvard.edu/files/abs/vdcwhitepaper-abs.shtml}} } @article{AltAndDig01b, author = {Micah Altman and Leonid Andreev and Mark Diggory and Gary King and Daniel L. Kiskis and Elizabeth Kolster and M. Krot and Sidney Verba}, title = {An Overview of the Virtual Data Center Project and Software}, journal = {JCDL '01: First Joint Conference on Digital Libraries}, year = 2001, pages = {203-204}, note = {{http://gking.harvard.edu/files/abs/jcdl01-abs.shtml}} } @article{AltKinSig01, author = {James E. Alt and Gary King and Curtis Signorino}, title = {Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of Data}, journal = {Political Analysis}, volume = {9}, year = {2001}, pages = {21--44}, month = {Winter}, number = {1}, note = {{http://gking.harvard.edu/files/abs/abcd-abs.shtml}} } @article{King01, author = {Gary King}, title = {Proper Nouns and Methodological Propriety: Pooling Dyads in International Relations Data}, journal = {International Organization}, volume = {55}, year = {2001}, pages = {497--507}, month = {Fall}, number = {2}, note = {{http://gking.harvard.edu/files/abs/pool-abs.shtml}} } @article{KinHonJos01, author = {Gary King and James Honaker and Anne Joseph and Kenneth Scheve}, title = {Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation}, journal = {American Political Science Review}, volume = 95, year = 2001, pages = {49--69}, month = {March}, number = 1 , note = {{http://gking.harvard.edu/files/abs/evil-abs.shtml}} } @article{KinZen01, author = {Gary King and Langche Zeng}, title = {Logistic Regression in Rare Events Data}, journal = {Political Analysis}, volume = 9, year = 2001, pages = {137--163}, month = {Spring}, number = 2 , note = {{http://gking.harvard.edu/files/abs/0s-abs.shtml}} } @article{KinZen01b, author = {Gary King and Langche Zeng}, title = {Explaining Rare Events in International Relations}, journal = {International Organization}, volume = 55, year = 2001, pages = {693--715}, month = {Summer}, number = 3 , note = {{http://gking.harvard.edu/files/abs/baby0s-abs.shtml}} } @article{RosJiaKin01, author = {Ori Rosen and Wenxin Jiang and Gary King and Martin A. Tanner}, title = {Bayesian and Frequentist Inference for Ecological Inference: The $R \times C$ Case}, journal = {Statistica Neerlandica}, volume = 55, year = 2001, pages = {134--156}, number = 2 , note = {{http://gking.harvard.edu/files/abs/rosen-abs.shtml}} } @article{BecKinZen00, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Improving Quantitative Studies of International Conflict}, journal = {American Political Science Review}, volume = 94, year = 2000, pages = {21--36}, month = {March}, number = 1 , note = {{http://gking.harvard.edu/files/abs/improv-abs.shtml}} } @article{King00, author = {Gary King}, title = {Geography, Statistics, and Ecological Inference}, journal = {Annals of the Association of American Geographers}, volume = {90}, year = {2000}, pages = {601--606}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/geog-abs.shtml}} } @article{KinTomWit00, author = {Gary King and Michael Tomz and Jason Wittenberg}, title = {Making the Most of Statistical Analyses: Improving Interpretation and Presentation}, journal = {American Journal of Political Science}, volume = 44, year = 2000, pages = {341--355}, month = {April}, number = 2, note = {{http://gking.harvard.edu/files/abs/making-abs.shtml}} } @article{GelKinLiu99, author = {Andrew Gelman and Gary King and Chuanhai Liu}, title = {Not Asked and Not Answered: Multiple Imputation for Multiple Surveys}, journal = {Journal of the American Statistical Association}, volume = 93, year = 1999, pages = {846--857}, month = {September}, number = 433 , note = {{http://gking.harvard.edu/files/abs/not-abs.shtml}} } @article{GelKinLiu99b, author = {Andrew Gelman and Gary King and Chuanhai Liu}, title = {Rejoinder}, journal = {Journal of the American Statistical Association}, volume = 93, year = 1999, pages = {869--874}, month = {September}, number = 433 , note = {{http://gking.harvard.edu/files/abs/not-abs.shtml}} } @article{KatKin99, author = {Jonathan Katz and Gary King}, title = {A Statistical Model for Multiparty Electoral Data}, journal = {American Political Science Review}, volume = 93, year = 1999, pages = {15--32}, month = {March}, number = {1}, note = {{http://gking.harvard.edu/files/abs/multiparty-abs.shtml}} } @article{King99, author = {Gary King}, title = {The Future of Ecological Inference Research: A Reply to Freedman et al.}, journal = {Journal of the American Statistical Association}, volume = {94}, year = {1999}, pages = {352-355}, month = {March}, number = {445}, note = {{http://gking.harvard.edu/files/abs/reply-abs.shtml}} } @article{KinLav99, author = {Gary King and Michael Laver}, title = {Many Publications, but Still No Evidence}, journal = {Electoral Studies}, volume = {18}, year = {1999}, pages = {597--598}, month = {December}, number = {4}, note = {{http://gking.harvard.edu/files/abs/manypub-abs.shtml}} } @article{KinRosTan99, author = {Gary King and Ori Rosen and Martin A. Tanner}, title = {Binomial-Beta Hierarchical Models for Ecological Inference}, journal = {Sociological Methods and Research}, volume = 28, year = 1999, pages = {61--90}, month = {August}, number = 1 , note = {{http://gking.harvard.edu/files/abs/binom-abs.shtml}} } @article{LewKin99, author = {Jeffrey Lewis and Gary King}, title = {No Evidence on Directional vs. Proximity Voting}, journal = {Political Analysis}, volume = {8}, year = {1999}, pages = {21--33}, month = {August}, number = {1}, note = {{http://gking.harvard.edu/files/abs/spatial-abs.shtml}} } @article{GelKinBos98, author = {Andrew Gelman and Gary King and John Boscardin}, title = {Estimating the Probability of Events that Have Never Occurred: When Is Your Vote Decisive?}, journal = {Journal of the American Statistical Association}, volume = {93}, year = {1998}, pages = {1--9}, month = {March}, number = {441}, note = {{http://gking.harvard.edu/files/abs/estimatprob-abs.shtml}} } @article{KinPal98, author = {Gary King and Bradley Palmquist}, title = {The Record of American Democracy, 1984-1990}, journal = {Sociological Methods and Research}, volume = {26}, year = {1998}, pages = {424--427}, month = {February}, number = {3}, note = {{http://www.hmdc.harvard.edu/ROAD/}} } @article{BenKin96, author = {Kenneth Benoit and Gary King}, title = {A Preview of EI and EzI: Programs for Ecological Inference}, journal = {Social Science Computer Review}, volume = {14}, year = {1996}, pages = {433--438}, month = {Winter}, number = {4}, note = {{http://gking.harvard.edu/files/abs/preview-abs.shtml}} } @inbook{GelKin96, author = {Andrew Gelman and Gary King}, title = {Advantages of Conflictual Redistricting}, year = {1996}, publisher = {Dartmouth Publishing Company}, pages = {207--218 }, address = {Aldershot, England}, editor = {Iain McLean and David Butler, eds}, note = {{http://gking.harvard.edu/files/abs/advant-abs.shtml}}, journal = {Fixing the Boundary: Defining and Redefining Single-Member Electoral Districts} } @inbook{KinBruGel96, author = {Gary King and John Bruce and Andrew Gelman}, title = {Racial Fairness in Legislative Redistricting}, year = {1996}, publisher = {Princeton University Press}, editor = {Paul E. Peterson, ed.}, note = {{http://gking.harvard.edu/files/abs/racial-abs.shtml}}, journal = {Classifying by Race} } @article{King96, author = {Gary King}, title = {Why Context Should Not Count}, journal = {Political Geography }, volume = {15}, year = {1996}, pages = {159--164}, month = {February}, number = {2}, note = {{http://gking.harvard.edu/files/abs/contxt-abs.shtml}} } @article{KinSig96, author = {Gary King and Curtis S. Signorino}, title = {The Generalization in the Generalized Event Count Model}, journal = {Political Analysis}, volume = 6, year = 1996, pages = {225--252}, note = {{http://gking.harvard.edu/files/abs/generaliz-abs.shtml}} } @article{King95, author = {Gary King}, title = {Replication, Replication}, journal = {PS: Political Science and Politics}, volume = {28}, year = 1995, pages = {443--499}, month = {September}, number = 3, note = {{http://gking.harvard.edu/files/abs/replication-abs.shtml}} } @article{King95b, author = {Gary King}, title = {A Revised Proposal, Proposal}, journal = {PS: Political Science and Politics}, volume = {XXVIII}, year = 1995, pages = {494--499}, month = {September}, number = 3, note = {{http://gking.harvard.edu/files/abs/replication-abs.shtml}} } @article{KinKeoVer95, author = {Gary King and Robert O. Keohane and Sidney Verba}, title = {The Importance of Research Design in Political Science}, journal = {American Political Science Review}, volume = {89}, year = {1995}, pages = {454--481 }, month = {June}, number = {2}, note = {{http://gking.harvard.edu/files/abs/kkvresp-abs.shtml}} } @article{VosGelKin95, author = {D. Steven Voss and Andrew Gelman and Gary King}, title = {Pre-Election Survey Methodology: Details From Nine Polling Organizations, 1988 and 1992}, journal = {Public Opinion Quarterly}, volume = {59}, year = {1995}, pages = {98--132}, month = {Spring}, number = {1}, note = {{http://gking.harvard.edu/files/abs/preelection-abs.shtml}} } @article{WinSigKin95, author = {Rainer Winkelmann and Curtis Signorino and Gary King}, title = {A Correction for an Underdispersed Event Count Probability Distribution}, journal = {Political Analysis}, year = {1995}, pages = {215--228}, note = {{http://gking.harvard.edu/files/abs/correction-abs.shtml}} } @article{AltKin94, author = {James E. Alt and Gary King}, title = {Transfers of Governmental Power: The Meaning of Time Dependence}, journal = {Comparative Political Studies}, volume = {27}, year = {1994}, pages = {190--210}, month = {July}, number = {2}, note = {{http://gking.harvard.edu/files/abs/transfers-abs.shtml}} } @article{GelKin94, author = {Andrew Gelman and Gary King}, title = {A Unified Method of Evaluating Electoral Systems and Redistricting Plans}, journal = {American Journal of Political Science}, volume = 38, year = 1994, pages = {514--554}, month = {May}, number = 2, note = {{http://gking.harvard.edu/files/abs/writeit-abs.shtml}} } @article{GelKin94b, author = {Andrew Gelman and Gary King}, title = {Enhancing Democracy Through Legislative Redistricting}, journal = {American Political Science Review}, volume = {88}, year = {1994}, pages = {541--559}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/red-abs.shtml}} } @incollection{GelKin94c, author = {Andrew Gelman and Gary King}, title = {Party Competition and Media Messages in U.S. Presidential Election Campaigns}, booktitle = {The Parties Respond: Changes in the American Party System}, publisher = {Westview Press}, year = 1994, address = {Boulder, Colorado}, editor = {L. Sandy Maisel}, pages = {255-295}, note = {{http://gking.harvard.edu/files/abs/partycomp-abs.shtml}} } @article{GelKin93, author = {Andrew Gelman and Gary King}, title = {Why are American Presidential Election Campaign Polls so Variable when Votes are so Predictable?}, journal = {British Journal of Political Science}, volume = 23, year = 1993, pages = {409--451}, month = {October}, number = 1, note = {{http://gking.harvard.edu/files/abs/variable-abs.shtml}} } @inbook{King93, author = {Gary King}, title = {The Methodology of Presidential Research}, year = {1993}, publisher = {University of Pittsburgh}, pages = {387--412}, address = {Pittsburgh}, editor = {George Edwards, III, John H. Kessel, and Bert A. Rockman, eds.}, note = {{http://gking.harvard.edu/files/abs/methpres-abs.shtml}}, journal = {Researching the Presidency: Vital Questions, New Approaches} } @article{KingBruGil93, author = {Gary King and John M. Bruce and Michael Gilligan}, title = {The Science of Political Science Graduate Admissions}, journal = {PS: Political Science and Politics}, volume = {XXVI}, year = {1993}, pages = {772--778}, month = {December}, number = {4}, note = {{http://gking.harvard.edu/files/abs/admis-abs.shtml}} } @article{KinLav93, author = {Gary King and Michael Laver}, title = {On Party Platforms, Mandates, and Government Spending}, journal = {American Political Science Review}, volume = {87}, year = {1993}, pages = {744--750}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/hoff-abs.shtml}} } @article{KinWal93, author = {Gary King and Daniel J. Walsh}, title = {Good Research and Bad Research: Extending Zimile's Criticism}, journal = {Early Childhood Research Quarterly}, volume = {8}, year = {1993}, pages = {397--401}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/good-abs.shtml}} } @article{King91, author = {Gary King}, title = {'Truth' is Stranger than Prediction, More Questionable Than Causal Inference}, journal = {American Journal of Political Science}, volume = {35}, year = {1991}, pages = {1047--1053}, month = {November}, number = {4}, note = {{http://gking.harvard.edu/files/abs/truth-abs.shtml}} } @article{King91b, author = {Gary King}, title = {Constituency Service and Incumbency Advantage}, journal = {British Journal of Political Science}, volume = {21}, year = {1991}, pages = {119--128}, month = {January}, number = {1}, note = {{http://gking.harvard.edu/files/abs/constit-abs.shtml}} } @article{King91c, author = {Gary King}, title = {On Political Methodology}, journal = {Political Analysis}, volume = {2}, year = {1991}, pages = {1--30}, note = {{http://gking.harvard.edu/files/abs/polmeth-abs.shtml}} } @article{King91d, author = {Gary King}, title = {Stochastic Variation: A Comment on Lewis-Beck and Skalaban's `The R-Square'}, journal = {Political Analysis}, volume = {2}, year = {1991}, pages = {185--200}, note = {{http://gking.harvard.edu/files/abs/stoch-abs.shtml}} } @article{King91e, author = {Gary King}, title = {Calculating Standard Errors of Predicted Values based on Nonlinear Functional Forms}, journal = {The Political Methodologist}, volume = {4}, year = {1991}, month = {Fall}, number = {2} } @article{KinGel91, author = {Gary King and Andrew Gelman}, title = {Systemic Consequences of Incumbency Advantage in the U.S. House}, journal = {American Journal of Political Science}, volume = 35, year = 1991, pages = {110--138}, month = {February}, number = 1 , note = {{http://gking.harvard.edu/files/abs/sysconseq-abs.shtml}} } @article{AnsKin90, author = {Stephen Ansolabehere and Gary King}, title = {Measuring the Consequences of Delegate Selection Rules in Presidential Nominations}, journal = {Journal of Politics}, volume = {52}, year = {1990}, pages = {609--621}, month = {May}, number = {2}, note = {{http://gking.harvard.edu/files/abs/pri-abs.shtml}} } @article{GelKin90, author = {Andrew Gelman and Gary King}, title = {Estimating the Electoral Consequences of Legislative Redistricting}, journal = {Journal of the American Statistical Association}, volume = {85}, year = {1990}, pages = {274--282}, month = {June}, number = {410}, note = {{http://gking.harvard.edu/files/abs/svstat-abs.shtml}} } @article{GelKin90b, author = {Andrew Gelman and Gary King}, title = {Estimating Incumbency Advantage Without Bias}, journal = {American Journal of Political Science}, volume = {34}, year = {1990}, pages = {1142--1164}, month = {November}, number = {4}, note = {{http://gking.harvard.edu/files/abs/inc-abs.shtml}} } @article{KinAltBur90, author = {Gary King and James Alt and Nancy Burns and Michael Laver}, title = {A Unified Model of Cabinet Dissolution in Parliamentary Democracies}, journal = {American Journal of Political Science}, volume = {34}, year = {1990}, pages = {846--871}, month = {August}, number = {3}, note = {{http://gking.harvard.edu/files/abs/coal-abs.shtml}} } @article{King90, author = {Gary King}, title = {Electoral Responsiveness and Partisan Bias in Multiparty Democracies}, journal = {Legislative Studies Quarterly}, volume = {XV}, year = {1990}, pages = {159--181}, month = {May}, number = {2}, note = {{http://gking.harvard.edu/files/abs/electresp-abs.shtml}} } @article{GelKin89, author = {Andrew Gelman and Gary King}, title = {Electoral Responsiveness in U.S. Congressional Elections, 1946-1986}, journal = {Proceedings of the Social Statistics Section, American Statistical Association}, year = {1989}, pages = {208} } @article{King89b, author = {Gary King}, title = {Representation Through Legislative Redistricting: A Stochastic Model}, journal = {American Journal of Political Science}, volume = {33}, year = {1989}, pages = {787--824}, month = {November}, number = {4}, note = {{http://gking.harvard.edu/files/abs/repstoch-abs.shtml}} } @article{King89c, author = {Gary King}, title = {Event Count Models for International Relations: Generalizations and Applications}, journal = {International Studies Quarterly}, volume = {33}, year = {1989}, pages = {123--147}, month = {June}, number = {2}, note = {{http://gking.harvard.edu/files/abs/ISQ33-abs.shtml}} } @article{King89d, author = {Gary King}, title = {Variance Specification in Event Count Models: From Restrictive Assumptions to a Generalized Estimator}, journal = {American Journal of Political Science}, volume = 33, year = 1989, pages = {762--784}, month = {August}, number = 3 , note = {{http://gking.harvard.edu/files/abs/varspecec-abs.shtml}} } @article{King89e, author = {Gary King}, title = {A Seemingly Unrelated Poisson Regression Model}, journal = {Sociological Methods and Research}, volume = {17}, year = {1989}, pages = {235--255}, month = {February}, number = {3}, note = {{http://gking.harvard.edu/files/abs/SMR17-abs.shtml}} } @article{King88, author = {Gary King}, title = {Statistical Models for Political Science Event Counts: Bias in Conventional Procedures and Evidence for The Exponential Poisson Regression Model}, journal = {American Journal of Political Science}, volume = 32, year = 1988, pages = {838-863}, month = {August}, number = 3 , note = {{http://gking.harvard.edu/files/abs/epr-abs.shtml}} } @article{BroKin87, author = {Robert X Browning and Gary King}, title = {Seats, Votes, and Gerrymandering: Measuring Bias and Representation in Legislative Redistricting}, journal = {Law and Policy}, volume = {9}, year = {1987}, pages = {305--322}, month = {July}, number = {3}, note = {{http://gking.harvard.edu/files/abs/LP9-abs.shtml}} } @article{KinBro87, author = {Gary King and Robert X Browning}, title = {Democratic Representation and Partisan Bias in Congressional Elections}, journal = {American Political Science Review}, volume = {81}, year = {1987}, pages = {1252--1273}, month = {December}, number = {4}, note = {{http://gking.harvard.edu/files/abs/sv-abs.shtml}} } @article{King87, author = {Gary King}, title = {Presidential Appointments to the Supreme Court: Adding Systematic Explanation to Probabilistic Description}, journal = {American Politics Quarterly}, volume = {15}, year = {1987}, pages = {373--386}, month = {July}, number = {3}, note = {{http://gking.harvard.edu/files/abs/sct-abs.shtml}} } @article{King86, author = {Gary King}, title = {How Not to Lie With Statistics: Avoiding Common Mistakes in Quantitative Political Science}, journal = {American Journal of Political Science}, volume = {30}, year = {1986}, pages = {666--687}, month = {August}, number = {3}, note = {{http://gking.harvard.edu/files/abs/mist-abs.shtml}} } @article{King86b, author = {Gary King}, title = {The Significance of Roll Calls in Voting Bodies: A Model and Statistical Estimation}, journal = {Social Science Research}, volume = {15}, year = {1986}, pages = {135--152}, month = {June}, note = {{http://gking.harvard.edu/files/abs/SSR15-abs.shtml}} } @article{King86c, author = {Gary King}, title = {Political Parties and Foreign Policy: A Structuralist Approach}, journal = {Political Psychology}, volume = {7}, year = {1986}, pages = {83--101}, month = {March}, number = {1}, note = {{http://gking.harvard.edu/files/abs/PP7-abs.shtml}} } @article{KinMer86, author = {Gary King and Richard Merelman}, title = {The Development of Political Activists: A Model of Early Learning}, journal = {Social Science Quarterly}, volume = {67}, year = {1986}, pages = {473--490}, month = {September}, number = {3}, note = {{http://gking.harvard.edu/files/abs/poliactiv-abs.shtml}} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{IacKinPor11b, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {Replication data for: Causal Inference Without Balance Checking: Coarsened Exact Matching}, journal = { }, year = 2011, note = {http://hdl.handle.net/1902.1/15601 Murray Research Archive [Distributor] V1 [Version]} } @article{HopKin09b, author = {Daniel Hopkins and Gary King}, title = {Replication Data for: A Method of Automated Nonparametric Content Analysis for Social Science}, journal = { }, year = 2009, note = {\underline{UNF:3:xlE5stLgKvpeMvxzlLxzEQ==} hdl:1902.1/12898 Murray Research Archive [Distributor]} } @article{KinGakIma09b, Author = {Gary King and Emmanuela Gakidou and Kosuke Imai and Jason Lakin and Clayton Nall and Ryan T. Moore and Nirmala Ravishankar and Manett Vargas and Martha Mar{\'i}a T{\'e}llez-Rojo and Juan Eugenio Hern{\'a}ndez {\'A}vila and Mauricio Hern{\'a}ndez {\'A}vila and H{\'e}ctor Hern{\'a}ndez Llamas}, title = {Replication Data for: Public Policy for the Poor? A Randomized Ten-Month Evaluation of the Mexican Universal Health Insurance Program}, journal = { }, year = {2009}, note = {\underline{hdl:1902.1/11044} UNF:3:jeUN9XODtYUp2iUbe8gWZQ== Murray Research Archive [Distributor]} } @article{ImaKinNal09c, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {Replication Data for: The Essential Role of Pair-Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation: Rejoinder}, journal = { }, year = {2009}, note = {\underline{hdl:1902.1/12730} UNF:3:CKs4T0iVYxP36LQSMgAkuw== Murray Research Archive [Distributor]} } @article{ImaKinNal09b, author = {Kosuke Imai and Gary King and Clayton Nall}, title = {Replication Data for: The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation}, journal = { }, year = {2009}, note = {\underline{hdl:1902.1/11047} UNF:3:jeUN9XODtYUp2iUbe8gWZQ== Murray Research Archive [Distributor]} } @Article{KinZen08, author = {Gary King and Langche Zeng}, title = {Replication data for: Empirical vs. Theoretical Claims about Extreme Counterfactuals: A Response}, journal = { }, year = 2008, note = {\underline{hdl:1902.1/11903}, Murray Research Archive [Distributor]} } @article{GakKin06b, author = {Emmanuela Gakidou and Gary King}, title = {Replication data for: Death by Survey: Estimating Adult Mortality without Selection Bias from Sibling Survival Data}, year = 2006, note = {{\underline{hdl:1902.1/ZMESWNECZW} Murray Research Archive [Distributor]}} } @article{GirKin06, author = {Federico Girosi and Gary King}, title = {Cause of Death Data}, year = {2006}, note = {{\underline{hdl:1902.1/UOVMCPSWOL} UNF:3:9JU+SmVyHgwRhAKclQ85Cg== Murray Research Archive [Distributor]}} } @article{HoImaKin06, author = {Daniel E. Ho and Kosuke Imai and Gary King and Elizabeth A. Stuart}, title = {Replication Data Set for: Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference}, year = 2006, note = {{\underline{hdl:1902.1/YVDZEQIYDS} Murray Research Archive [distributor]}} } @article{KinAlt06, author = {Gary King and James E. Alt and Nancy Burns and Michael Laver}, title = {Replication data for: A Unified Model of Cabinet Dissolution in Parliamentary Democracies}, year = {2006}, note = {{\underline{hdl:1902.1/RMPXNUSBBS} UNF:3:lfKIeFJKgejkOzXEY1i6lw== Murray Research Archive [Distributor]}} } @article{KinZen06b, author = {Gary King and Langche Zeng}, title = {Replication Data Set for: When Can History be Our Guide? The Pitfalls of Counterfactual Inference}, year = 2006, note = {{\underline{hdl:1902.1/DXRXCFAWPK} Murray Research Archive [distributor]}} } @article{KinZen06c, author = {Gary King and Langche Zeng}, title = {Replication data for: Detecting Model Depedence in Statistical Inference: A Response}, year = {2006}, note = {{\underline{hdl:1902.1/FGSRBXXIYT} UNF:3:K4/CgnMYDMV6izc5RVOZTA== Murray Research Archive [Distributor]}} } @article{KinZen06d, author = {Gary King and Langche Zeng}, title = {Replication data for: When Can History be Our Guide? The Pitfalls of Counterfactual Inference}, year = {2006}, note = {{\underline{hdl:1902.1/DXRXCFAWPK} UNF:3:DaYlT6QSX9r0D50ye+tXpA== Murray Research Archive [Distributor]}} } @article{KinZen06e, author = {Gary King and Langche Zeng}, title = {Replication data for: The Dangers of Extreme Counterfactuals }, year = {2006}, note = {{\underline{hdl:1902.1/UTVMBVNGMX} UNF:3:ytKKNjK+yR8Pq3H0RcV6eg== Murray Research Archive [Distributor]}} } @article{EpsHoKin05b, author = {Lee Epstein and Daniel E. Ho and Gary King and Jeffrey A. Segal}, title = {Replication data for: The Supreme Court During Crisis: How War Affects Only Nonwar Cases}, year = {2005}, note = {{\underline{hdl:1902.1/RESUDVYWPE} UNF:3:ZmbzFbfqogNM0Gb6CcV52A== Murray Research Archive [Distributor]}} } @article{BecKinZen04b, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Replication data for: Gelpi and Grynaviski}, year = {2004}, note = {{\underline{hdl:1902.1/LAAYCJJGDS} UNF:3:N0bEAswAlPPVXCxPOZYyqw== Murray Research Archive [Distributor]}} } @article{King03b, author = {Gary King}, title = {10 Million International Dyadic Events}, year = {2003}, note = {{\underline{hdl:1902.1/FYXLAWZRIA} UNF:3:um06qkr/1tAwpS4roUqAiw== Murray Research Archive [Distributor]}} } @article{KinZen01c, author = {Gary King and Langche Zeng}, title = {Replication data for: Explaining Rare Events in International Relations}, year = {2001}, note = {\underline{hdl:1902.1/OUCBSJKXIC} UNF:3:vyct3c8fMCdWOdp03NUhaA== Murray Research Archive [Distributor]} } @article{KinZen01d, author = {Gary King and Langche Zeng}, title = {Replication data for: Improving Forecats of State Failure}, year = {2001}, note = {{\underline{hdl:1902.1/RPQIODIANR} UNF:3:CEsbEgPxbxExfYuh2NWwWQ== Murray Research Archive [Distributor]}} } @article{BecKinZen00b, author = {Nathaniel Beck and Gary King and Langche Zeng}, title = {Replication data for: Improving Quantitative Studies of International Conflict: A Conjecture}, volume = {2000}, note = {{\underline{hdl:1902.1/SZKONDGOMF} UNF:3:rYRDzT8dCJ/BR7V9u8fObA== Murray Research Archive [Distributor]}} } @article{KinTomWit00b, author = {Gary King and Michael Tomz and Jason Wittenberg}, title = {Replication data for: Making the Most of Statistical Analyses: Improving Interpretation and Presentation}, year = {2000}, note = {{\underline{hdl:1902.1/QTCABXZZRQ} UNF:3:1VaLflZ/LfB+AISX+hBm1w== Murray Research Archive [Distributor]}} } @article{KatKin99b, author = {Jonathan Katz and Gary King}, title = {Replication data for: A Statistical Model of Multiparty Electoral Data}, year = {1999}, note = {{\underline{hdl:1902.1/QIGTWZYTLZ} UNF:3:gwGcKylle0BKJTGv3Zv4OA== Murray Research Archive [Distributor]}} } @article{GelKinBos98b, author = {Andrew Gelman and Gary King and John Boscardin}, title = {Replication data for: Estimating the Probability of Events that have Never Occurred: When is your Vote Decisive}, year = {1998}, note = {{\underline{hdl:1902.1/NOLXXTUHNZ} UNF:3:ORDulVH6qEb4lsCyDn5W3A== Murray Research Archive [Distributor]}} } @article{King97b, author = {Gary King}, title = {Replication data for: A Solution to the Ecological Inference Problem: Reconstructing Individuals Behavior from Aggregate Data}, year = {1997}, note = {{\underline{hdl:1902.1/LWMMKUTYXS} UNF:3:DRWozWd89+vNLO7lY2AHbg== Murray Research Archive [Distributor]}} } @article{GelKin94d, author = {Andrew Gelman and Gary King}, title = {Replication data for: Enhancing Democracy Through Legislative Redistricting}, year = {1994}, note = {{\underline{hdl:1902.1/BNCOWNVERH} UNF:3:ZXahi7PBFxLRb46sVKOAuQ== Murray Research Archive [Distributor]}} } @article{GelKin94e, author = {Andrew Gelman and Gary King}, title = {Replication data for: Unified Methods of Evaluating Electoral Systems and Redistricting Plans: United States House of Representatives adn Ohio State Legislature}, year = {1994}, note = {{\underline{hdl:1902.1/JWFTSFKOBK} UNF:3:Fi01DWj4Sx+0ZEOEo4TOXA== Murray Research Archive [Distributor]}} } @article{King94, author = {Gary King}, title = {Elections to the United States House of Representatives, 1898-1992}, year = {1994}, note = {{\underline{hdl:1902.1/TQDSSPRDDZ} UNF:3:tD8SznMFjKIxWxOqTQaamQ== Murray Research Archive [Distributor]}} } @article{GelKin93b, author = {Andrew Gelman and Gary King}, title = {Replication data for: Why Are American Presidential Election Campaign Polls so Variable When Votes are so Predictable?}, year = {1993}, note = {{\underline{hdl:1902.1/SBBXEUSSCW} Murray Research Archive [Distributor]}} } @article{KinLav93b, author = {Gary King and Michael Laver}, title = {Replication data for: On Party Platforms, Mandates, and Government Spending}, year = {1993}, note = {{\underline{hdl:1902.1/XEHYCJAWQD} UNF:3:cwNXuRQ/6Lp72obLkttmGg== Murray Research Archive [Distributor]}} } @article{King91e, author = {Gary King}, title = {Replication data for: Constituency Service and Incumbency Advantage}, year = {1991}, note = {{\underline{hdl:1902.1/JTMXGSZXIZ} UNF:3:IE4ZSAs8ZzUK+fRXNbVvGw== Murray Research Archive [Distributor]}} } @article{King91f, author = {Gary King}, title = {Replication data for: On Political Methodology}, year = {1991}, note = {{\underline{hdl:1902.1/KHTLSQXAEJ} Murray Research Archive [Distributor]}} } @article{AnsKin90b, author = {Stephen Ansolabehere and Gary King}, title = {Replication data for: Measuring the Consequences of Delegate Selection Rules in Presidential Nominations}, year = {1990}, note = {{\underline{hdl:1902.1/BUJXCEPXQK} UNF:3:OdFPcQcvfO5hc3WJ5ty8vQ== Murray Research Archive [Distributor]}} } @article{KinBen86, author = {Gary King and Gerald Benjamin}, title = {Replication data for: The Stability of Partisan Identification in the U.S. House of Representatives, 1789-1984}, year = {1986}, note = {{\underline{hdl:1902.1/HINHTJQYFO} Murray Research Archive [Distributor]}} } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Software %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{IacKinPor09b, author = {Stefano M. Iacus and Gary King and Giuseppe Porro}, title = {CEM: Coarsened Exact Matching Software}, journal = {Journal of Statistical Software}, volume = 30, issue = 9, year = 2009, note = {{http://gking.harvard.edu/cem}} } @article{WanKinLau07, author = {Jonathan Wand and Gary King and Olivia Lau}, title = {Anchors: Software for Anchoring Vignettes Data}, journal = {Journal of Statistical Software}, year = {2007, forthcoming} } @Article{HonKinBLa10, author = {James Honaker and Gary King and Matthew Blackwell}, title = {Amelia II: A Program for Missing Data}, year = 2010, note = {{http://gking.harvard.edu/amelia}} } @article{ImaKinLau06, author = {Kosuke Imai and Gary King and Olivia Lau}, title = {Zelig: Everyone's Statistical Software}, year = 2006, note = {{http://gking.harvard.edu/zelig}} } @article{TomWitKin05, author = {Michael Tomz and Jason Wittenberg and Gary King}, title = {CLARIFY: Software for Interpreting and Presenting Statistical Results}, year = {1998-2005}, note = {{http://gking.harvard.edu/stats.shtml#clarify}} } @article{HonJosKin98, author = {James Honaker and Anne Joseph and Gary King and Kenneth Scheve and Naunihal Singh.}, title = {AMELIA: A Program for Missing Data}, year = {1998-2002}, note = {{http://gking.harvard.edu/amelia}} } @article{King98, author = {Gary King}, title = {MAXLIK, a set of Gauss programs, annotated for pedagogical purposes, to implement the maximum likelihood models in Unifying Political Methodology: The Likelihood Theory of Statistical Inference}, year = {1998}, note = {{http://gking.harvard.edu/stats.shtml#maxlik}} } @article{King96b, author = {Gary King}, title = {EI: Program for Ecological Inference}, year = {1996-2003}, note = {{http://gking.harvard.edu/stats.shtml#ei}} } @article{GelKin92, author = {Andrew Gelman and Gary King}, title = {JudgeIt: A Program for Evaluating Electoral Systems and Redistricting Plans}, year = {1992-2002}, note = {{http://gking.harvard.edu/stats.shtml#judgeit}} } @article{HoImaKin07a, author = {Daniel E. Ho and Kosuke Imai and Gary King and Elizabeth A. Stuart}, title = {MatchIt: Nonparametric Preprocessing for Parametric Causal Inference}, year = {Forthcoming}, journal = {Journal of Statistical Software}, note = {{http://gking.harvard.edu/matchit}} } @InCollection{Gelman04, author = {Andrew Gelman}, title = {Treatment Effects in Before-After Data}, booktitle = {Applied Bayesian Modeling and Causal Inference from an Incomplete Data Perspective}, publisher = {Wiley}, year = 2004, editor = {Andrew Gelman and Xiao-Li Meng}, chapter = 18, address = {London} } MatchIt/vignettes/gk.bib0000644000176200001440000206654012162551623014712 0ustar liggesusers% A bibtex file for papers by or coauthored with Gary King % % To add references, first please CHECK that your doesn't already % exist in this file and % then add entries only at the end. % % Use these rules for the reference label: % % -if one author: use last name and last 2 digits of the year: Tobler79. % -if multiple authors, use 1st 3 letters of each of UP TO the first three % authors and the last 2 digits of the year: KinTomWit00. % -if necessary add lower-case letters for multiple entries in a year: King02, King02b % (the first one should NOT have an 'a' afterwards) % % -feel free to use the abbreviations at the start, or add to them. % -Use authors full names when known. % % please be sure to commit changes to CVS regularly as a number of % people are all using this at the same time. @STRING{ prq = "Political Research Quarterly"} @STRING{ apsr = "American Political Science Review"} @STRING{ ajps = "American Journal of Political Science"} @STRING{ jop = "Journal of Politics"} @STRING{ bjps = "British Journal of Political Science"} @STRING{ jleo = "Journal of Law, Economics, and Organization"} @STRING{ isa = "Paper presented at the annual meetings of the International Studies Association"} @STRING{ apsa = "Paper presented at the annual meetings of the American Political Science Association"} @STRING{ cp = "Comparative Politics"} @STRING{ io = "International Organization"} @STRING{ midwest = "Paper presented at the Annual Meeting of the Midwest Political Science Association"} @STRING{ mpsa = "midwest"} @STRING{ southern = "Paper presented at the Annual Meeting of the Southern Political Science Association"} @STRING{ icpsr = "Inter-University Consortium for Political and Social Research"} @STRING{ jasa = "Journal of the American Statistical Association"} @STRING{ lsq = "Legislative Studies Quarterly"} @STRING{ isq = "International Studies Quarterly"} @STRING{ tas = "The American Statistician"} @STRING{ jbes = "Journal of Business \& Economic Statistics"} @STRING{ joe = "Journal of Econometrics"} @STRING{ wp = "World Politics"} @STRING{ cup = "Cambridge University Press"} @STRING{ hup = "Harvard University Press"} @STRING{ ny = "New York"} @STRING{ sv = "Springer Verlag"} @STRING{ pup = "Princeton University Press"} @STRING{ ucp = "University of California Press"} @STRING{ ap = "Academic Press"} @STRING{ wb = "The World Bank"} @STRING{ eas = "Europe-Asia Studies"} @STRING{ jet = "Journal of Economic Theory"} @STRING{ jrssA = "Journal of the Royal Statistical Society, A"} @STRING{ jrssb = "Journal of the Royal Statistical Society, B"} @STRING{ poq = "Public Opinion Quarterly"} @STRING{ pnas = "Proceedings of the National Academy of Sciences"} @STRING{ ai = "Artificial Intelligence"} @STRING{ pa = "Political Analysis"} @STRING{ ps = "PS: Political Science and Politics"} @STRING{ smr = "Sociological Methods and Research"} @STRING{ sim = "Statistics in Medicine"} @STRING{ asr = "American Sociological Review"} @STRING{ bmj = "British Medical Journal"} @STRING{ lan = "Lancet"} @STRING{ dem = "Demography"} @STRING{ bull = "Bulletin of WHO"} @STRING{ ssm = "Social Science and Medicine"} @STRING{ mitai = "Artificial Intelligence Laboratory, Massachusetts Institute of Technology"} @STRING{ nc = "Neural Computation"} @article{AbaDruLeb02, author = {Alberto Abadie and David Druckker and Jane Leber Herr and Guido W. Imbens}, title = {Implementing Matching Estimators for Average Treatment Effects in Stata}, journal = {The Stata Journal}, volume = 1, year = 2002, pages = {1--18}, number = 1 } @article{Bin83, author = {David A. Binder}, title = {On the Variance of Asymptoticaly Normal Estimators from Complex Surveys}, year = {1983}, journal = {International Statistical Review}, volume = {51}, number = {3}, pages = {279--292} } @article{HorTho52, author = {D. G. Horvitz and D.J. Thompson}, title = {A Generalization of Sampling without Replacement from a Finite Universe}, year = {1952}, journal = {Journal of the American Statistical Association}, volume = {47}, pages = {663--685} } @article{AbaGelLev08, author = {Kobi Abayomi and Andrew Gelman and Marc Levy}, title = {Diagnostics for Multivariate Imputations}, journal = {Applied Statistics}, volume = {57}, number = {3}, pages = {273--291}, year = {2008} } @misc{AbaImb05, author = {Alberto Abadie and Guido Imbens}, title = {Estimation of the Conditional Variance in Paired Experiments}, year = 2006, howpublished = {KSG Working Paper}, note = {{http://ksghome.harvard.edu/\~{}.aabadie.academic.ksg/cve.pdf}} } @misc{AbaImb09, author = {Alberto Abadie and Guido Imbens}, title = {A Martingale Representation for Matching Estimators}, year = 2009, howpublished = {IZA Discussion Papers number 4073}, note = {{http://ftp.iza.org/dp4073.pdf}} } @article{Imb00, author = {Guido Imbens}, title = {The role of the propensity score in estimating the dose-response functions}, year = {2000}, journal = {Biometrika}, pages = {706--710}, volume = {87}, issue = {3} } @article{AbaImb07b, author = {Alberto Abadie and Guido Imbens}, title = {On the Failure of the Bootstrap for Matching Estimators}, year = {2007}, journal = {Econometrica}, pages = {1537--1557}, volume = {76}, issue = {6} } @article{AbaImb06, author = {Abadie, Alberto and Imbens, Guido W.}, title = {Large Sample Properties of Matching Estimators for Average Treatment Effects}, journal = {Econometrica}, volume = {74}, year = {2006}, pages = {235--267}, number = {1} } @inproceedings{Abu-Mostafa92, author={Y. Abu-Mostafa}, title={A Method for Learning from Hints}, booktitle={Advances in Neural information processings systems 5}, year={1992}, publisher={Morgan Kaufmann Publishers}, address={San Mateo, CA}, editor={S. J. Hanson and Jack D. Cowan and C. Lee Giles} } @article{Abu-Mostafa92, author={Y. Abu-Mostafa}, title={A Method for Learning from Hints}, journal={Advances in Neural information processings systems 5}, volume={1992}, pages={Morgan Kaufmann Publishers}, month={San Mateo CA}, number={S. J. Hanson and Jack D. Cowan and C. Lee Giles} } @book{Achen86, author={Christopher Achen}, title={Statistical Analysis of Quasi-experiments}, publisher={University of California Press}, year={1986}, address={Berkeley} } @techreport{AdaCoaRue00, author={Michelle Adato and David Coady and Marie Ruel}, title={An Operations Evaluation of Progresa from the Perspective of Beneficiaries, Promotoras, School Directors and Health Staff}, institution={International Food Policy Research Institute}, year={2000}, month={August}, type={Final Report}, address={2033 K Street, NW Washington, DC 20006} } @article{AdaGla05, author={Adamic, L.A. and Glance, N.}, title={{The political blogosphere and the 2004 US election: divided they blog}}, journal={Proceedings of the 3rd international workshop on Link discovery}, year={2005}, pages={36--43}, publisher={ACM Press New York, NY, USA} } @article{AgoDyn04, author={Roberto Agodini and Mark Dynarski}, title={Are experiments the only option? {A} look at dropout prevention programs}, journal={Review of Economics and Statistics}, volume= 86, year= 2004, pages={180-194}, month={February}, number= 1 } @unpublished{AgrRajSri03, author={Rakesh Agrawal and Sridhar Rajagopolan and Ramakrishnan Srikant and Yirong Xu}, title={Mining Newsgroups Using Networks Arising from Social Behavior}, note={IBM ALmaden Research Center 650 Harry Rd., San Jose, CA 95120}, year={2003}, month={May} } @book{Aitchison86, author={J. Aitchison}, title={The Statistical Analysis of Compositional Data}, publisher={Chapman and Hall}, year= 1986, address={London} } @article{Albert88, author={James H. Albert}, title={Computational Methods Using a Bayesian Hierarchical Generalized Linear Model}, journal={Journal of the American Statistical Association}, volume={83}, year={1988}, pages={1037-1004}, month={December}, number={404} } @article{AldMcK77, author={John H. Aldrich and Richard D. McKelvey}, title={A Method of Scaling With Applications to the 1968 and 1972 Presidential Elections}, journal= apsr, volume= 71, year= 1977, pages={111-130}, month={March} } @article{AleTab90, author={Alberto Alesina and Guido Tabellini}, title={A Positive Theory of Fiscal Deficits and Government Debt}, journal={The Review of Economic Studies}, volume={57}, year={1990}, pages={403-414}, month={July}, number={3} } @article{Alho00, author={J. M. Alho}, title={Discussion}, journal={North American Actuarial Journal}, volume= 4, year= 2000, pages={91--93}, number= 1 } @article{Alho92, author={J. M. Alho}, title={{Comment on ``Modeling and Forecasting U.S. Mortality'' by R. Lee and L. Carter}}, journal= jasa, volume= 87, year= 1992, pages={673--674}, month={September}, number= 419 } @article{AlSaCr76, author={James Alt and Bo Sarlvik and Ivor Crewe}, title={Individual Differences Scaling and Group Attitude Structures: British Party Imagery in 1974}, journal={Quality and Quantity}, volume= 10, year= 1976, pages={297--320}, month={October} } @book{AltGilMcD03, author={Micah Altman and Jeff Gill and Michael P. McDonald}, title={Numerical Issues in Statistical Computing for the Social Scientist}, publisher={John Wiley and Sons}, year= 2003, address={New York} } @article{Altman85, author={Douglas G. Altman}, title={Comparability of Randomised Groups}, journal={The Statistician}, volume={34}, year={1985}, pages={125-136}, number={1} } @article{Altman98, author={Douglas G. Altman and Jonathan J. Deeks and David L. Sackett}, title={Odds Ratios Should be Avoided When Events are Common}, journal={British Medical Journal}, volume= 317, year= 1998, pages= 1318, month={Nov. 7} } @article{AltMcD03, author={Micah Altman and Michael P. McDonald}, title={Replication with Attention to Numerical Accuracy}, journal={Political Analysis}, volume={11}, year={2003}, pages={302-307}, number={3} } @article{AltRub70, author={Robert P. Althauser and Donald B. Rubin}, title={The computerized construction of a matched sample}, journal={American Journal of Sociology}, volume= 76, year= 1970, pages={325-346}, month={September} } @article{AlvBre95, author={Michael R. Alvarez and John Brehm}, title={American Ambivalence Toward Abortion Policy: A Heteroskedastic probit Method for Assessing Conflicting Values}, journal={American Journal of Political Science}, volume={39}, year={1995}, pages={1055-82}, month={November} } @article{AlvBre97, author={Michael R. Alvarez and John Brehm}, title={Are Americans Ambivalent Towards Racial Policies}, journal={American Journal of Political Science}, volume={41}, year={1997}, pages={345-374}, month={April}, number={2} } @article{AlvGarLan91, author={Michael R.\ Alvarez and Geoffrey Garrett and Peter Lange}, title={Government Partisanship, Labor Organization, and Macroeconomic Performance}, journal= apsr, volume= 85, year= 1991, pages={539--556} } @article{AmoMccZim97, author={A.F. Amos and D.J. McCarty and P. Zimmet}, title={The Rising Global Burden of Diabetes and its Complications: Estimates and Projections to the Year 2010}, journal={Diabetic Medicine}, volume= 14, year= 1997, tpages={S7--S85} } @book{AndBasHum83, author={Andy B. Anderson and Alexander Basilevsky and Derek P.J. Hum}, title={Missing Data: A Review of the Literature}, publisher={Academic Press, Inc}, year={1983}, editor={Peter H. Rossi and James D. Writght and Andy B. Anderson} } @article{AndGib06, author={Krister Andersson and Clark C. Gibson}, title={Decentralized Governance and Environmental Change: Local Institutional Moderation of Defroestation in Bolivia}, journal={Journal of Policy Analysis and Management}, volume={26}, year={2006}, pages={99-123}, number={1} } @article{AndGreMcc05, author={Richard G. Anderson and William H. Greene and B.D. McCullough and H.D. Vinod}, title={The Role of Data \& Program Code Archives in the Future of Economic Research}, year= 2005, month={July}, note={Federal Reserve Bank of St. Louis Research Division} } @article{Andrews91, author={Donald W.K. Andrews}, title={Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation}, journal={Econometrica}, volume={59}, year={1991}, pages={817--858}, month={May}, number={3} } @article{AndZom01, author={A.S. Andreou and G.A. Zombanakis}, title={A Neural Network Measurement of Relative Military Security--The Case of Greece and Cyprus}, journal={Defence and Peace Economics}, volume= 12, year= 2001, pages={303--324}, number= 4, annote={have not read primary source. looks promising, given secondary source comments: all input variables are financial and the output variable--relative security--is population/demographics based. Arms race scenarios are simulated by increasing and decreasing financial covariates.} } @article{AngAngFro94, author={G. De Angelis and R. De Angelis and L. Frova and A. Verdecchia}, title={MIAMOD: A Computer Package to Estimate Chronic Disease Morbidity Using Mortality and Survival Data}, journal={Computer Methods and Programs in Biomedicine}, volume= 44, year= 1994, pages={99--107} } @article{AngImb95, author={Joshua D. Angrist and Guido W. Imbens}, title={Two-Stage Least Squares Estimation of Average Causal Effects in Models withVariable Treatment Intensity}, journal={Journal of the American Statistical Association}, volume={90}, year={1995}, pages={431-442}, month={June}, number={430} } @article{AngImbRub96, author={Angrist, Joshua D. and Imbens, Guido W. and Rubin, Donald B.}, title={Identification of Causal Effects Using Instrumental Variables (with discussion)}, journal={Journal of the American Statistical Association}, volume={91}, year={1996}, pages={444--455}, optnumber={434} } @article{Angress59, author={Werner T. Angress}, title={The Political Role of the Peasantry in the Weimar Republic}, journal={The Review of Politics}, volume= 21, year= 1959, pages={530--549}, number= 3 } @unpublished{AnkBlaCol99, author = {Martha Anker and Robert E. Black and Christopher Coldham and Henry D. Kalter and Maria A. Quigley and David Ross and Robert W. Snow}, title = {A Standard Verbal Autopsy Method for Investigating Causes of Death in Infants and Children}, note = {World Health Organization, Department of communicable Disease Surveillance and Response}, year = {1999}, journal = {World Health Organization} } @book{Anker03, author = {Martha Anker}, title = {Investigating Cause of Death During an Outbreak of Ebola Virus Haemorrhagic Fever: Draft Verbal Autopsy Instrument}, publisher = {World Health Organization}, year = 2003, address = {Geneva} } @article{Anker97, author = {Martha Anker}, title = {The Effect of Misclassification Error on Reported Cause-Specific Mortality Fractions from Verbal Autopsy}, journal = {International Journal of Epidemiology}, volume = {26}, year = {1997}, pages = {1090-1096} } @article{AppBosGra96, author = {A. Appels, et al}, title = {Self-Rated Health and Mortality in a Lithuanian and Dutch Population}, journal = {Social Science and Medicine}, volume = 42, year = 1996, pages = {{681-89}}, number = 5 } @techreport{Arendt03, author = {Jacob N. Arendt}, title = {Social gradients in self-rated health in Denmark - gender differences and health risk factors in dynamic context}, institution = {AKF, Institute of Local Government Studies}, year = 2003, month = {May}, address = {Nyropsgade 37, 1602 Copenhagen V, Denmark} } @book{Arendt73, author = {Arendt, Hannah}, title = {The Origins of Totalitarianism}, publisher = {Harcourt Brace Jovanovich}, year = 1973, address = {New York} } @incollection{Armstrong01, author = {J. Scott Armstrong}, title = {Extrapolation of Time Series and Cross-Sectional Data}, booktitle = {Principles of Forecasting: A Handbook for Researchers and Practitioners}, publisher = {Kluwer}, year = 2001, editor = {J. Scott Armstrong}, pages = {217--243} } @unpublished{Ashworth01, author = {Scott Ashworth}, title = {Reputational Dynamics and Congressional Careers}, note = {Harvard University}, year = 2001, annote = {introduce the single crossing property in political science} } @article{AssPocEno00, author = {Susan F. Assmann and Stuart J. Pocock, Laura E. Enos and Linda E. Kasten}, title = {Subgroup analysis and other (mis)uses of baseline data in clinical trials}, journal = {The Lancet}, volume = {355}, year = {2000}, pages = {1064-1069}, month = {March} } @article{AusMam06, author={Peter C. Austin and Muhammad M. Mamdani}, title={A comparison of propensity score methods: A case-study estimating the effectiveness of post-AMI statin use}, journal={Statistics in Medicine}, volume={25}, year={2006}, pages={2084-2106} } @article{AusMamStu05, author={Peter C. Austin and Muhammad M. Mamdani and Therese A. Stukel and Geoffrey M. Anderson and Jack V. Tu}, title={The use of the propensity score for estimating treatment effects: {A}dministrative versus clinical data}, journal={Statistics in Medicine}, volume={24}, year={2005}, pages={1563-1578} } @article{AvlSchDav98, author={Kirsten Avlund, Kirsten Schultz-Larsen, and Michael Davidson}, title={Tiredness in Daily Activities at Age 70 as a Predictor of Mortality During the Next 10 Years}, journal={Journal of Clinical Epidemiology}, volume= 51, year= 1998, pages={{323-33}} } @article{BacKin04, author={Bachrach, Christine A. and King, Roslind B.}, title={{Data Sharing and Duplication: Is There a Problem?}}, journal={Archives of Pediatric and Adolescent Medicine}, volume= 158, year={2004}, month={September}, number= 9 } @article{BagHopMas02, author={A. Bagust and P.K. Hopkinson and L. Maslove and C.J. Currie}, title={The Projected Health Care Burden of Type 2 Diabetes in the UK from 2000 to 2060}, journal={Diabetic Medicine}, volume= 19, year= 2002, pages={1--5}, number= 4 } @book{Balderston02, author={Theo Balderston}, title={Economics and Politics in the Weimar Republic}, publisher={Cambridge University Press}, year= 2002, address={Cambridge} } @article{BanBan92, author={A.T. Bang and R.A. Bang and the SEARCH team}, title={Diagnosis of causes of childhood deaths in developing countries by verbal autosy: suggested criteria}, journal={Bulletin of the World Health Organization}, volume={70}, year={1992}, pages={499-507}, number={4} } @article{BaqBlaAri98, author={A.H. Baqui and R.E. Black and S.E. Arifeen and K. Hill and S.N. Mitra and A.Al Sabir}, title={Causes of childhood deaths in Bangladesh: results of a nationwide verbal autopsy study}, journal={Bulletin of the World Health Organization}, volume={76}, year={1998}, pages={161}, number={2} } @article{BarFraHil03, author={John Barnard and Constantine E. Frangakis and Jennifer L. Hill and Donald B. Rubin}, title={{Principal Stratification Approach to Broken Randomized Experiments: A Case Study of School Choice Vouchers in New York City.}}, journal={Journal of the American Statistical Association}, volume={98}, year={2003}, pages={299-324}, number={462} } @book{Barkai90, author={Barkai, Avram}, title={Nazi Economics: Ideology, Theory, and Policy}, address={Oxford}, publisher={Berg Press}, year={1990} } @article{BarPonCor00, author={Ivana C. H. C. Barr{\^e}to and L{\'i}gia Kerr Pontes and e Luciano Corr{\^e}a}, title={Vigil{\^a}ncia de {\'o}bitos infantis em sistemas locais de sa{\'u}de: avalia{\c{c}}{\~a}o da aut{\'o}psia verbal e das informa{\c{c}}{\~o}es de agentes de sa{\'u}de}, journal={Rev Panam Salud Publica / Pan Am Journal of Public Health}, volume={7}, year={2000}, pages={303-312}, number={5} } @article{Bartels96, author={Bartels, Larry M.}, title={Uninformed Votes: Information Effects in Presidential Elections}, journal={American Journal of Political Science}, volume= 40, year= 1996, pages={194--230} } @unpublished{Bartels98, author={Larry Bartels}, title={Panel Attrition and Panel Conditioning in American National Election Sudies}, note={Paper prepared for the 1998 meetings of the Society for Political Methodology, San Deigo}, year={1998} } @article{BasEst01, author={S.A. Bashir and J. Esteve}, title={Projecting Cancer Incidence and Mortality Using Bayesian Age-Period-Cohort Models}, journal={Journal of Epidemiology and Biostatistics}, volume= 6, year= 2001, pages={287--296}, number= 3 } @unpublished{BatFerHab06, author={Robert Bates and Karen Feree and James Habyarimana and Macartan Humphreys and Smita Singh}, title={The Africa Research Program}, note={{http://africa.gov.harvard.edu}}, year= 2006 } @article{Bath03, author={Peter A. Bath, PhD}, title={Differences Between Older Men and Women in the Self-Rated Health-Mortality Relationship}, journal={The Gerontologist}, volume= 43, year= 2003, pages={{387-95}} } @article{Baum88, author={Lawrence Baum}, title={Measuring Policy Change in the U.S. Supreme Court}, journal= apsr, volume= 82, year= 1988, pages={905--912}, month={September}, number= 3 } @article{BeaMei89, author={Michael L. Beach and Paul Meier}, title={Choosing Covariates in the Analysis of Clinical Trials}, journal={Controlled Clinical Trials}, volume={10}, year={1989}, pages={161S-175S} } @incollection{Bearce00, author={David Bearce}, title={Economic Sanctions and Neural Networks: Forecasting Effectiveness and Reconsidering Cooperation}, booktitle={Political Complexity: Non Linear Models of Politics}, publisher={University of Michigan Press}, year= 2000, address={Ann Arbor, MI}, editor={Diana Richards}, pages={269--295}, annote={asks whether real-world forecasting needs make NN preferable to traditional (and linear) analysis. Looks at effectiveness of sanctions, using about 100 quantitative cases first examined in 1980s. NNs are shown to forecast twice as well as traditional methods.} } @book{BecChaWil88, author={Richard A. Becker and John M. Chambers and Allan R. Wilks}, title={The New S. language}, publisher={Wadsworth}, year={1988}, address={New York} } @article{BecIch02, author={Sascha O. Becker and Andrea Ichino}, title={Estimation of average treatment effects based on propensity scores}, journal={The Stata Journal}, volume= 2, year= 2002, pages={358-377}, number= 4 } @article{BecIch02, author={Sascha O. Becker and Andrea Ichino}, title={Stata programs for ATT estimation based on propensity score matching}, journal={The Stata Journal}, volume= 2, year= 2002, pages={358--377}, number= 4 } @article{BecJac98, author={Nathaniel Beck and Simon Jackman}, title={Beyond Linearity by Default: Generalized Additive Model}, journal= ajps, volume= 42, year= 1998, pages={596--627}, month={April}, number= 2 } @article{BecKat95, author={Nathaniel Beck and Jonathan Katz}, title={``What to Do (and Not to Do) with Time-Series-Cross-Section Data''}, journal= apsr, volume= 89, year= 1995, pages={634--647} } @article{BecKat96, author={Nathaniel Beck and Jonathan Katz}, title={Nuisance vs. Substance: Specifying and Estimating Time-Series-Cross-Section Model}, journal= pa, volume={VI}, year= 1996, pages={1--36} } @article{BecKatTuc98, author={Nathaniel Beck and Jonathan Katz and Richard Tucker}, title={Taking Time Seriously: Time-Series-Cross-Section Analysis with a Binary Dependent Variable}, journal= apsr, volume= 42, year= 1998, pages={1260-1288} } @article{BedChrJoh96, author={Edward J. Bedrick and Ronald Christensen and Wesley Johnson}, title={A New Perspective on Priors for Generalized Linear models}, journal={Journal of the American Statistical Association}, volume={91}, year={1996}, pages={1450-1460}, number={436} } @techreport{BehTod99, author={Jere R. Behrman and Petra E. Todd}, title={Randomness in the Experimental Samples of Progresa (Education, Health, and Nutrition Program)}, institution={International Food Policy Research Institute}, year={1999}, month={March}, type={Research Report}, address={2033 K Street, NW Washington, DC 20006} } @article{Bell97, author={W.R. Bell}, title={{Comparing and Assessing Time Series Methods for Forecasting Age-Specific Fertility and Mortality Rates}}, journal={Journal of Official Statistics}, volume= 13, year= 1997, pages={279--303}, number= 3 } @article{Bello93, author={Abdul Lateef Bello}, title={Choosing Among Imputation Techniques for Incomplete Multivariate Data: A Simulation Study}, journal={Communications in Statistics A: Theory and Methods}, volume={22}, year={1993}, pages={853-877}, number={3} } @article{BelMon91, author={W.R. Bell and B.C. Monsell}, title={Using Principal Components in time Series modeling and Forecasting of Age-Specific Mortality Rates}, journal={Proceedings of the American Statistical Association, Social Statistics Section}, year= 1991, pages={154--159} } @article{Beltrami1873, author={E. Beltrami}, title={Sulle funzioni bilineari}, journal={Giornale di Matematiche ad Uso degli Studenti Delle Universit{\'a}}, volume= 11, year= 1873, pages={98--106}, note={{An English translation by D. Boley is available as University of Minnesota, Department of Computer Science, Technical Report 90-37, 1990}} } @article{BenBluLus03, author={Yael Benyamini, et al.}, title={Gender differences in the self-rated health-mortality association: Is it poor self-rated health that predicts mortality or excellent self-rated health that predicts survival?}, journal={The Gerontologist}, volume={43}, year={2003}, pages={{396-405}}, number={3} } @incollection{Bendix53, author={Bendix, Reinhard}, title={Social Stratification and Political Power}, booktitle={Class Status and Power}, publisher={The Free Press}, year= 1953, address={Glencoe, IL}, editor={Bendix, Reinhard and Lipset, Seymour Martin} } @article{BenHumEbe04, author={Maureen Reindl Benjamins}, title={Self-Reported Health and Adult Mortality Risk: An Analysis of Cause Specific Mortality}, journal={Social Science and Medicine (Forthcoming 2004)} } @article{benichou95, author={J. Benichou and M. Gail}, title={Methods of Inference for Estimates of Absolute Risk Derived From Population-Based Case-Control Studies}, journal={Biometrics}, volume= 51, year= 1995, pages={182-194} } @article{BenIdl99, author={Yael Benyamini, and Ellen Idler}, title={Community Studies Reporting Association Between Self-Rated Health and Mortality}, journal={Research on Aging}, volume= 21, year= 1999, pages={{392-401}}, number= 3 } @article{BenIdlLev00, author={Yael Benyamini, Ellen Idler, Howard Leventhal, and Elaine A. Leventhal}, title={Positive-Affect and Function as Influences on Self-Assessments of Health: Expanding ou View Beyond Illness and Disability}, journal={Journal of Gerontology: Psychological Sciences}, volume={{55B}}, year= 2000, pages={{P107-116}} } @article{BenLav03, author={Kenneth Benoit and Michael Laver}, title={Estimating Irish party policy positions using computer wordscoring: the 2002 election - a research note}, journal={Irish Political Studies}, volume={18}, year={2003}, pages={97--107}, number={1} } @article{BenLevLev00, author={Yael Benyamini, et al}, title={Gender Differences in Processing Information for Making Self-Assessments of Health }, journal={American Psychosomatic Society}, volume= 62, year= 2000, pages={{354-64}}, number= 2 } @article{BenLevLev99, author={Yael Benyamini, Elaine A. Leventhal, and Howard Leventhal}, title={Self-Assessments of Health. What Do People Know that Predicts their Mortality?}, journal={Reasearch on Aging}, volume= 21, year= 1999, pages={{477-500}}, month={{May}}, number= 3 } @article{BenLip59, author={Bendix, Reinhard and Lipset, Seymour Martin}, title={On the Social Structure of Western Societies: Some Reflections on Comparative Analysis}, journal={Berkeley Journal of Sociology}, volume= 5, year= 1959, pages={1-15} } @article{BenSin99, author={S.K. Benara and Padam Singh}, title={Validity of Causes of Infant Death by Verbal Autopsy}, journal={Indian Journal of Pediatrics}, volume={66}, year={1999}, pages={647-650} } @article{BenSin99, author={S.K. Benara and Padam Singh}, title={Validity of Causes of Infant Death by Verbal Autopsy}, journal={Indian Journal of Pediatrics}, volume={66}, year={1999}, pages={647-650} } @article{BerdeG47, author={Berelson, B. and de Grazia, S.}, title={{Detecting Collaboration in Propaganda}}, journal={Public Opinion Quarterly}, volume={11}, year={1947}, pages={244--253}, number={2} } @proceedings{BerDegLin88, editor={J. M. Bernardo and M.H. Degroot and D.V. Lindley and A.F.M. Smith}, title={Bayesian Statistics 3}, publisher={Clarendon Press, Oxford}, year={1987}, month={June 1-5}, organization={Proceedings of the Third Valencia International Meeting} } @article{Berenson04, author={Robert Berenson}, title={The Medicare Chronic Care Improvement Program}, journal={The Urban Institute}, year={2004}, month={May}, note={{http://www.urban.org/url.cfm?ID=900714}} } @article{Berger04, author={Vance W. Berger}, title={Selection Bias and Baseline Imbalances in Randomized Trials}, journal={Drug Information Journal}, volume={38}, year={2004}, pages={1-2} } @article{Berger05a, author={Vance W. Berger}, title={Quantifying the Magnitude of Baseline Covariate Imbalances Resulting fronmSelection Bias in Randomized Clinical Trials}, journal={Biometrical Journal}, volume={47}, year={2005}, pages={119-127}, number={2} } @book{Berger05b, author={Vance W. Berger}, title={Selection Bias and covariate Imbalances in Randomized Clinical Trials}, publisher={John Wiley \& Sons, Ltd.}, year={2005}, editor={Stephen Senn and Vic Barnett}, series={Statistics in Practice} } @article{Berger94, author={James Berger}, title={An Overview of Robust Bayesian Analysis (With Discussion)}, journal={Test}, volume= 3, year= 1994, pages={5-124} } @article{BerHenSav77, author={E. Berndt and D. Hendry and N.E. Savin}, title={Conflict Among Criteria for Testing Hypotheses in the Multivariate Linear Regression Model}, journal={Econometrica}, volume={45}, year={1977}, pages={1263-1277} } @article{BerKinKon97, author={Shulamit L. Bernard, Jean E. Kincade, Thomas R. Conrad, et al}, title={Predicting Mortality from Community Surveys of Older Adults: The Importance of Self-Rated Functional Ability}, journal={Journal of Gerontology: Social Sciences}, volume={{52B}}, year= 1997, pages={{S155-63}} } @misc{BerKos03, author={Erik Bergstralh and Jon Kosanke}, title={dist, gmatch, and vmatch: SAS Macros}, year= 2003, howpublished={Mayo Clinic, Division of Biostatistics}, note={{http://mayoresearch.mayo.edu/mayo/research/biostat/sasmacros.cfm}} } @book{BerKreOve98, author={Mark de Berg and Marc van Krevald and Mark Overmars and Otfried Schwarzkopf}, title={Computational Geometry: Algorithms and Applications}, publisher={Springer}, year= 1998, address={New York}, edition={2nd, revised edition} } @book{ImbRub10, author={Guido Imbens and Donald Rubin}, title={Causal Inference}, year= {2010}, note={Unpublished manuscript} } @article{Bernstein32, author={F. Bernstein}, title={{\"U}ber eine Methode, die soziologische und bev{\"o}lkerungsstatistische Gliederung von Abstimmungen bei geheimen Wahlverfahren zu ermittlen}, journal={Allgemeines Statistisches Archiv}, volume= 22, year= 1932, pages={253--256} } @article{Besag74, author={Julian Besag}, title={Spatial Interaction and the Statistical Analysis of Lattice Systems (With Discussion)}, journal= jrssb, volume= 36, year= 1974, pages={192-236} } @article{Besag75, author={Julian Besag}, title={Statistical Analysis of Non-Lattice Data}, journal={The Statistician}, volume= 24, year= 1975, pages={179--195}, number= 3 } @article{Besag83, author={Julian E. Besag}, title={Discussion of paper by {P}. {S}witzer}, journal={Bull. Intern. Statist. Inst.}, volume= 50, year= 1983, pages={422-425}, number={Bk. 3} } @article{Besag86, author={Julian Besag}, title={On the Statistical Analysis of Dirty Pictures}, journal={Journal of the Royal Statistical Society B}, volume={48}, year={1986}, pages={259--302}, number={3} } @article{Besancon05, author={Marie L. Besancon}, title={Relative Resources: Inequality in Ethnic Wars, Revolutions, and Genocides}, abstract={Political scientists and economists have exhaustively examined the nexus between economic inequality and political conflict (EI-PC nexus) in aggregated civil wars. This article revisits the nexus and its related theories, empirically and parsimoniously testing the effects of inequality on disaggregated intrastate conflicts. The results buttress the notion that traditionally deprived identity groups are more likely to engage in conflict under more economically equal conditions, while class or revolutionary wars fall under the conditions of greater economic inequality and war. Of the three types of conflicts tested - ethnic conflicts, revolutions, and genocides - economic inequality seems to have the most ambiguous bearing on genocides. Support follows for recent findings that political and social equalities are of greater importance in mitigating ethnic violence and that greed factors might exacerbate violence in all civil conflicts, including genocides. The theoretical argument proposes that the context within which intrastate violence takes place affects the requisite level of relative resources needed for the escalation of violence between groups. The results have policy implications for ethnically divided states that are in the process of equalizing their income differential, but neglect the substantial inclusion of all groups within the political process and the distribution of public goods. The social contracts between the governors and the governed then require careful crafting for a peaceful coexistence of diverse identity groups.}, journal={The Journal of Peace Research}, volume={42}, year={2005}, pages={393-415}, month={July}, number={4} } @article{BesGreHigMen95, author={Julian Besag and Peter Green and David Higdon and Kerrie Mengersen}, title={Bayesian Computation and Stochastic Systems (With Discussion)}, journal={Statistical Science}, volume= 10, year= 1995, pages={3-66}, number= 1 } @article{BesHig99, author={Julian Besag and David M. Higdon}, title={Bayesian Analysis of Agricultural Field Experiments (With Discussion)}, journal= jrssb, volume= 61, year= 1999, pages={691-746}, number={4} } @article{BesKoo95, author={Julian Besag and Charles Kooperberg}, title={On Conditional and Intrinsic Autoregressions}, journal={Biometrika}, volume={82}, year= 1995, pages={733-746}, number={4} } @article{BesYorMol91, author={Julian Besag and Jeremy York and Annie Molli{\'e}}, title={Bayesian Image Restoration with Two Applications in Spatial Statistics (With Discussion)}, journal={Annals of the Institute of Statistical Mathematics}, volume= 43, year= 1991, pages={1-59}, number= 1 } @article{Bicego97, author={G. Bicego}, title={Estimating adult mortality rates in the context of the AIDS epidemic in sub-Saharan Africa: analysis of DHS sibling histories}, journal={Health Transition Review}, volume= 7, year= 1997, pages={7--22}, number={S2} } @book{Biggs93, author={N.L. Biggs}, title={Algebraic Graph Theory}, publisher={Cambridge University Press}, year= 1993, address={Cambridge, UK}, edition={2nd} } @article{Billordo05a, author={Libia Billordo}, title={Publishing in French Political Science Journals}, journal={French Politics}, volume={3}, year={2005}, pages={178-186}, number={2} } @article{Billordo05b, author={Libia Billordo}, title={Methods Training in French Political Science}, journal={French Politics}, volume={3}, year={2005}, pages={352-0357}, number={3} } @article{BinBreEar05, author={J.B. Bingenheimer and R.T. Brennan and F.J. Earls}, title={Firearm violence exposure and serious violent behavior}, journal={Science}, volume={308}, year={2005}, pages={1323-1326}, month={May} } @book{BisFieHol75, author={Y.M. M. Bishop and S.E. Fienberg and P.W. Holland}, title={Multivariate Analysis: Theory and Practice}, publisher={MIT Press}, year= 1975, address={Cambridge, MA} } @book{Bishop95, author={Christopher M. Bishop}, title={Neural Networks for Pattern Recognition}, publisher={Oxford University Press}, year= 1995, address={Oxford} } @article{BisSteWil06, author={Benjamin Bishin and Daniel Stevens and Christian Wilson}, title={{Character Counts: Honesty and Fairness in Election 2000}}, journal={Public Opinion Quarterly}, volume={70}, year={2006}, pages={235-248}, number={2} } @article{BjoKri99, author={Jakob Bue Bjorner and Tage Sondergaard Kristensen}, title={Multi-item Scales for Measuring Global Self Rated Health}, journal={Research on Aging}, volume= 21, year= 1999, pages={{417-39}}, number= 3 } @article{BlaGeo91, author={R.C. Blattberg and E.I. George}, title={Shrinkage Estimation of Price and Promotional Elasticities: Seemingly Unrelated Equations}, journal= jasa, volume= 86, year= 1991, pages={304--315}, month={Jun}, number= 414 } @article{BlaRas04, author={Grant Blank and Karsten B. Rasmussen}, title={The Data Documentation Initiative: The Value and Significance of a Worldwide Standard.}, journal={Social Science Computer Review}, volume={22}, year={2004}, pages={306-318}, number={3} } @article{BlaSmi04, author={Dan A. Black and Jeffrey A. Smith}, title={How robust is the evidence on the effects of college quality? Evidence from matching}, journal={Journal of Econometrics}, volume={121}, year={2004}, pages={99-124}, number={1} } @article{BloHilRic03, author={Howard S. Bloom and Carolyn J. Hill and James A. Riccio}, title={Linking Program Implementation and Effectiveness: Lessons from a Pooled Sample of Welfare-to-Work Experiments}, journal={Journal of Policy Analysis and Management}, volume={22}, year={2003}, pages={551-575}, number={4} } @book{Bloom05, title={Learning More from Social Experiments}, publisher={Russell Sage Foundation}, year={2005}, editor={Howard S. Bloom}, address={New York} } @article{BloRicBla07, author={Howard S. Bloom and Lashawn Richburg-Hayes and Alison Black}, title={Using Covariates to Improve Precision for Studies that Randomize Schools to Evaluate Educational Interventions}, journal={Educational Evaluation and Policy Analysis}, year={2007} } @book{BLS03, author={{Board on Life Sciences}}, title={Sharing Publication-Related Data and Materials: Responsibilities of Authorship in the Life Sciences}, publisher={National Academies Press}, year= 2003, address={Washington, D.C.} } @article{Blumer48, author = {Herbert Blumer}, title = {Public Opinion and Public Opinion Polling}, journal = {American Sociological Review}, volume = {13}, year = {1948}, pages = {542-549}, month = {October}, number = {5} } @incollection{Bohm84, author={Peter Bohm}, title={Are thee Practicable Demand-Revealing Mechanisms?}, booktitle={Public Finance and the Quest for Efficiency}, publisher={Wayne State University Press}, year={1984}, address={Detroit}, editor={H. Hanusch}, pages={127-139} } @article{BonBarMee94, author={Luc Monneux and Jan J. Barendregt and Karin Meeter and Gouke J. Bonsel and Paul J. van der Maas}, title={Estimating Clinical Morbidity Due to Ischemic Heart Disease and Congestive Heart Failure: The Future Rise of Heart Failure}, journal={American Journal of Public Health}, volume= 84, year= 1994, pages={20-28} } @unpublished{BonBonJen01, author={Doug Bond and Joe Bond and J. Craig Jenkins and Churl Oh and Charles Lewis Taylor}, title={Integrated Data for Events Analysis (IDEA): An Event Form Typology for Automated Events Data Development}, note={manuscript, Harvard University}, year= 2001 } @article{Bongaarts89, author={John Bongaarts}, title={A Model of the Spread of HIV Infection and the Demographic Impact of AIDS}, journal={Statistics in Medicine}, volume= 8, year= 1989, pages={103--120} } @techreport{BooMaiSmi02, author={Heather Booth and John Maindonald and Len Smith}, title={{Age-Time Interactions in Mortality Projection: Applying Lee-Carter to Australia}}, institution={The Australian National University}, year= 2002, month={August}, type={Working Papers in Demography} } @article{BooMaiSmi02b, author={Heather Booth and John Maindonald and Len Smith}, title={Applying Lee-Carter Under Conditions of Variable Mortality Decline}, journal={Population Studies}, volume= 56, year= 2002, pages={325--336}, number= 3 } @unpublished{BorBorRal01, author={Roman Borisyuk and Galina Borisyuk and Colin Rallings and Michael Thrasher}, title={Forecasting the 2001 General Election Result: A Neural Network Approach}, note={{http://www.psa.ac.uk/spgrp/epop/forecasting\_genelect2001.htm}}, year={2001}, annote={authors try to forecast election results; they generate fitted values by 'predicting' the winners of past elections. they do break up their data sets into training and test in the spirit of cross-validation; they also compare their results to logit.} } @incollection{Borchardt91, author = {Knut Borchardt}, title = {Economic Causes for the Collapse of the Weimar Republic}, booktitle = {Perspectives on Modern German Economic History and Policy}, publisher = {Cambridge University Press}, year = 1991, address = {New York}, editor = {Knut Borchardt}, pages = {161--184} } @article{borgan95, author={{\O }rnulf Borgan and B. Langgholz and L. Goldstein}, title={Methods for the Analysis of Sampled Cohort Data in the Cox Proportional Hazard Model}, journal={The Annals of Statistics}, volume= 23, year= 1995, number={1749-1778} } @article{BorMayTur04, author={Robert Boruch and Henry May and Herbert Turner and Julia Lavenberg and Anthony Petrosino and Dorothy de Moya and Jeremy Grimshaw and Ellen Foley }, title={Estimating the Effects of Interventions That are Deployed in Many Places: Place-Randomized Trials}, journal={American Behavioral Scientist}, volume={47}, year={2004}, pages={608-633}, number={5} } @book{Boruch97, author={Robert F. Boruch}, title={Randomized Experiments for Planning and Evaluation}, publisher={Sage Publications}, year={1997}, address={Thousand Oaks} } @article{BouChaWel01, author={Andrew Boulle and Daniel Chandramohan and Peter Weller}, title={A Case Study of Using Artificial Neural Networks for Classifying Cause of Death from Verbal Autopsy}, journal={International Journal of Epidemiology}, volume={30}, year={2001}, pages={515-520} } @unpublished{BowHan05, author={Jake Bowers and Ben Hansen}, title={Attributing Effects to A Cluster Randomized Get-Out-The-Vote Campaign: An Application of Randomization Inference Using Full Matching}, note={Departments of Political Science and Statistics: University of Michigan}, year={2005}, month={July} } @book{BoxHunHun78, author={George E.P. Box and William G. Hunger and J. Stuart Hunter}, title={Statistics for Experimenters}, publisher={Wiley-Interscience}, year={1978}, address={New York} } @article{BoyHonNar01, author={James P. Boyle and Amanda A. Honeycutt and K.M. Venkat Narayan and Thomas J. Hoerger and Linda S. Geiss and Hong Chen and Theodore J. Thompson}, title={Projection of Diabetes Burden Through 2050}, journal={Diabetes Care}, volume= 24, year= 2001, pages={1936--1940}, number= 11 } @article{BozBel87, author={J.E. Bozik and W.R. Bell}, title={Forecasting Age Specific Fertility Using Principal Components}, journal={Proceedings of the Americal Statistical Association, Social Statistics Section}, year= 1987, pages={396--401} } @article{bracken98, author={Michael B. and John C. Bracken}, title={Avoidable Systematic Error in Estimating Treatment Effects Must not be Tolerated}, journal={British Medical Journal}, volume= 317, year= 1998, pages={11-56}, month={October 24} } @article{Brady85, author={Henry E. Brady}, title={The Perils of Survey Research: Inter-Personally Incomparable Responses}, journal={Political Methodology}, volume= 11, year= 1985, pages={269--290}, month={June}, number={3--4} } @article{Brady89, author={Henry E. Brady}, title={Factor and Ideal Point Analysis for Interpersonally Incomparable Data}, journal={Psychometrika}, volume= 54, year= 1989, pages={181--202}, month={June}, number= 2 } @inproceedings{BraHil73, author={William Brass and Kenneth Hill}, title={Estimating Adult Mortality in Africa from Orphanhood}, booktitle={Proceedings of the International Population Conference Liege}, year= 1973, organization={International Union for the Scientific Study of Population} } @article{BraTuc01, author={Ted Brader and Joshua Tucker}, title={The Emergence of Mass Partisanship in Russia, 1993-96}, journal={American Journal of Political Science}, volume={45}, year={2001}, pages={69-83} } @book{BreDay80, author={Norman E. Breslow and N.E. Day }, title={Methods in Cancer Research}, publisher={Lyon}, year= 1980 } @book{BreFriOls84, author={Leo Breiman and Jerome H. Friedman and Richard A. Olshen and Charles J. Stone}, title={Classification and Regression Trees}, publisher={Chapman \& Hall}, year={1984}, address={New York, New York} } @book{Brehm93, author={John Brehm}, title={The Phantom Respondents: Opinion Surveys and Political Respresentation}, publisher={University of Michigan Press}, year={1993}, address={Ann Arbor} } @article{Breiman01, author={Leo Breiman}, title={Statistical Modeling: The Two Cultures}, journal={Statistical Science}, volume={16}, year={2001}, pages={199-215}, month={August}, number={3} } @article{Breslow96, author={Norman E. Breslow}, title={Statistics in Epidemiology: The Case-Control Study}, journal= jasa, volume= 91, year= 1996, pages={14--28} } @unpublished{Breyer04, author={L.A. Breyer}, title={The Dbacl Text Classifier}, note={laird@lbreyer.com}, year={04}, month={June} } @unpublished{Breyer04, author={L.A. Breyer}, title={The Dbacl Text Classifier}, note={laird@lbreyer.com}, year={04}, month={June} } @article{BreZie92, author={Hermann Brenner and Hartwig Ziegler}, title={Monitoring and Projecting Cancer Incidence in Saarland, Germany, Based on Age-Cohort Analyses}, journal={Journal of Epidemiology and Community Health}, volume= 46, year= 1992, pages={15--20} } @book{BroDav91, author={Peter J. Brockwell and Richard A. Davis}, title={Time Series: Theory and Methods}, publisher={Springer-Verlag}, year={1991}, edition={2nd} } @article{BroDenVer02, author={N. Brouhns and M. Denuit and J. Vermunt}, title={A Poisson Log-bilinear Regression Approach to the Construction of Projected Lifetables}, journal={Insurance: Mathematics and Economics}, volume= 31, year= 2002, pages={373--393} } @article{BroGra00, author={Ron Brookmeyer and Sarah Gray}, title={Methods for Projecting the Incidence and Prevalence of Chronic Diseases in Ageing Populations: Application to Alzheimer's Disease}, journal={Statistics in Medicine}, volume= 19, year= 2000, pages={1481--1493} } @article{BroHew00, author={M. Brockerhoff and P. Hewett}, title={Inequality of child mortality among ethnic groups in sub-Saharan Africa}, journal= bull, year={2000}, optnumber={1}, optvolume={78}, optpages={30--41} } @article{Bronnum-Hansen02, author={Henrik Bronnum-Hansen}, title={Predicting the Effect of Prevention of Ischaemic Heart Disease}, journal={Scandinavian Journal of Public Health}, volume= 30, year= 2002, pages={5--11} } @article{Bronnum-Hansen99, author={Henrik Bronnum-Hansen}, title={How Good is the Prevent Model for Estimating the Health Benefits of Prevention?}, journal={Journal of Epidemiology and Community Health}, volume= 53, year= 1999, pages={300--305} } @article{BroSchRot06, author={M. Alan Brookhart and Sebastian Schneeweiss and Kenneth J. Rothman and Robert J. 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The articles in this special issue treat demography as both a cause and a consequence of armed conflict, and they carry important policy implications. A study of German-allied countries during World War II addresses the role of refugees and territorial loss in paving the way for genocide. Other articles focusing on the demographic causes of conflict discuss highly contentious issues of whether economic and social inequality, high population pressure on natural resources, and youth bulges and limited migration opportunities can lead to different forms of armed conflict and state failure. The articles on demographic responses to armed conflict analyze the destructiveness of pre-industrial warfare, differences in short- and long-term mortality trends after armed conflict, and migratory responses in war. Another set of articles on demographic responses to war is published simultaneously in the European Journal of Population.}, journal={The Journal of Peace Research}, volume={42}, year={2005}, pages={371-374}, month={July}, number={4} } @book{BSER02, author={{Board on Earth Sciences and Resources}}, title={Geoscience Data and Collections: National Resources in Peril}, publisher={National Academies Press}, year= 2002, address={Washington, D.C.} } @article{Buchheim03, author={Christoph Buchheim}, title={Die Erholung von der Weltwirtschaftskrise 1932/33 in Deutschland}, journal={Jahrbuch fuer Wirtschaftsgeschicht}, year={2003}, pages={13-26}, number={1} } @article{BurFred01, author={B Burstrom and P Fredlund}, title={Self-rated health: Is it as good a predictor of subsequent mortality among adults in lower as well as in higher social classes?}, journal={Journal of Epidemiology and Community Health}, volume= 55, year= 2001, pages={{836-40}} } @article{Burgoon06, author={Brian Burgoon}, title={On Welfare and Terror}, journal={Journal of Conflict Resolution}, volume={50}, year={2006}, pages={176-203}, month={April}, number={2} } @article{Burnham72, author={Walter Dean Burnham}, title={Political Immunisation and Political Confessionalism: The United States and Weimar Germany}, journal={Journal of Interdisciplinary History}, volume= 3, year= 1972, pages={1--30} } @article{Burtless95, author={Gary Burtless}, title={The Case for Randomized Field Trials in Economic and Policy Research}, journal={The Journal of Economic Perspectives}, volume={9}, year={1995}, pages={63-84}, number={2} } @article{ButBurMit87, author={J.S. 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Series A (General)}, volume= 128, year= 1965, pages={234-266}, number= 2 } @article{Cochran68, author={Cochran, William G.}, title={The effectiveness of adjustment by subclassification in removing bias in observational studies}, journal={Biometrics}, volume={24}, year={1968}, pages={295-313} } @article{CocRub73, author={Cochran, William G. and Rubin, Donald B.}, title={Controlling bias in observational studies: A review}, journal={Sankhya: The Indian Journal of Statistics, Series A}, volume={35, Part 4}, year={1973}, pages={417-466} } @article{ColMah93, author = {David Collier and Mahon, Jr., James E.}, title = {Conceptual `Stretching' Revisited}, journal = apsr, volume = 87, year = 1993, pages = {845-855}, month = {December}, number = 4 } @article{ColSul02, author={James E. 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Skegg}, title={Projections of Cervical Cancer Mortality and Incidence in New Zealand: The Possible Impact of Screening}, journal={Journal of Epidemiology and Community Health}, volume= 46, year= 1992, pages={373--377} } @article{CraWoo05, author={Craggs, Richard and Mary McGee Wood}, title={{Evaluating Discourse and Dialogue Coding Schemes}}, journal={Computational Linguistics}, volume={31}, year={2005}, pages={289-295}, number={3} } @article{CroKen02, author={Thomas F. Crossley and Steven Kennedy}, title={The reliability of self-assesses health status}, journal={Journal of Health Economics}, volume= 21, year= 2002, pages={{643-58}}, number= 4 } @article{Crosnoe05, author={Robert Crosnoe}, title={Double Disadvantage or Signs of Resilience? The Elementary School Contexts of Children from Mexican Immigrant Families}, journal={American Educational Research Journal}, volume={42}, year={2005}, pages={269-303}, number={2} } @unpublished{CruHotImb06, author = {Richard K. Crump and V. Joseph Hotz and Guido W. Imbens and Oscar Mitnik}, title = {Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand}, note = {Department of Economics, UC Berkeley}, year = {2006}, month = {September} } @article{CruHotImb09, title = {{Dealing with limited overlap in estimation of average treatment effects}}, author = {Richard K. Crump and V. Joseph Hotz and Guido W. Imbens and Oscar Mitnik}, journal = {Biometrika}, volume = {96}, number = {1}, pages = {187}, year = {2009} } @article{CuaFor95, author={C.M. Cuadras and J. Fortiana}, title={A Continuous Metric Scaling Solution for A Random Variable}, journal={Journal of Multivariate Analysis}, volume= 52, year= 1995, pages={1--14} } @article{CuaForOli97, author={C.M. Cuadras and J. Fortiana and F. Oliva}, title={The Proximity of an Individual to a Population with Applications to Discriminant Analysis}, journal={Journal of Classification}, volume= 14, year= 1997, pages={117-136} } @article{CumMcKWei03, author={Peter Cummings and B. McKnight and NS Weiss}, title={Matched-pair cohort methods in traffic crash research}, journal={Accident Analysis and Prevention}, volume= 35, year= 2003, pages={131--141}, note={{http://depts.washington.edu/hiprc/about/topics/web/bike\_prevmat/}} } @article{CumRoy88, author={W.G. Cumberland and R. M. Royall}, title={Does Simple Random Sampling Provide Adequate Balance?}, journal={Journal of the Royal Statistical Society. Series B (methodological)}, volume={50}, year={1988}, pages={118-124}, number={1} } @article{Dagostino98, author={Ralph B. {D'Agostino, Jr.}}, title={Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non-randomized Control Group}, journal={Statistics in Medicine}, volume= 17, year= 1998, pages={2265--2281} } @article{DagRub00, author={Ralph B. {D'Agostino, Jr.} and Donald B. Rubin}, title={Estimating and using propensity scores with partially missing data}, journal={Journal of the American Statistical Association}, volume= 95 , year= 2000, pages={749-759} } @article{DalBecHuc98, author={Russell J. Dalton and Paul A. Beck and Robert Huckfeldt}, title={Partisan Cues and the Media: Information Flows in the 1992 Presidential election}, journal={American Poltical Science Review}, volume={92}, year={1998}, pages={111-126} } @article{DanGraStu96, author={I. Danel and W. Graham and P Stupp and P. Castillo}, title={Applying the sisterhood method for estimating maternal mortality to a health facility-based sample: a comparison with results from a household-based sample}, journal={International Journal of Epidemiology}, volume= 25, year= 1996, pages={1017--1-22} } @inbook{DanLaf05, author={Isabel Danel and Gerard M. La Forgia}, title={Health Systems Innovation in Central America}, chapter={Contracting for Basic Health Care in Rural Guatemala - Comparison of the Performance of Three Delivery Models}, year={2005}, publisher={The World Bank}, address={Washington, DC}, editor={Gerard M. La Forgia} } @unpublished{DasChe01, author={Sanjiv R. Das and Mike Y. Chen}, title={Yahoo! for Amazon: Opinion Extraction from Small Talk on the Web}, note={Department of Finance Santa Clara University}, year={2001}, month={August} } @article{DasKleiKlei94, author={Erik J. Dasbach, PhD, Ronald Klein, MD, Barbara E. K. Klein, MD, and Scot E. Moss, MA}, title={Self-Rated Health and Mortality in People with Diabetes}, journal={American Journal of Public Health}, volume= 84, year= 1994, pages={{1775-79}} } @article{DavAlbCoo03, author={G.L. Davis and J.E. Albright and S.F. Cook and D.M. Rosenberg}, title={Projecting Future Complications of Chornic Hepatitis C in the United States}, journal={Liver Transplantation}, volume= 9, year= 2003, pages={331--338}, number= 4 } @article{DavAlbCoo03, author={G.L. Davis and J.E. Albright and S.F. Cook and D.M. Rosenberg}, title={Projecting Future Complications of Chornic Hepatitis C in the United States}, journal={Liver Transplantation}, volume= 9, year= 2003, pages={331--338}, number= 4 } @unpublished{DavLawPen03, author={Kushal Dave and Steve Lawrence and David Pennock}, title={Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews}, note={Kushal Dave NEC Laboratories America, 4 Independence Way Princeton, NJ 08540, Kushal@nec-labs.com}, year={2003}, month={May} } @article{DavManLai98, author={Huw Talfryn Oakley Davies and Tavakoli Manouche and Crombie Kinloch Iain}, title={Authors Reply}, journal={British Medical Journal}, volume= 317, year= 1998, pages={1156-7}, month={October 24} } @article{DavNeaWen98, author={G. Davey-Smith and J.D. Neaton and D. Wentworth and R. Stamler and J. 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Laplace}, title={Philosophical Essays on Probaiblities}, publisher={Dover}, year={1951, original: 1820}, address={New York} } @article{LaRBanJar79, author={Asenath LaRue, PhD, Lew Bank, MA, Lissy Jarvick, MD, PhD, and Monte Hetland, BA}, title={Health in Old Age: How Do Physicians' Ratings and Self-Ratings Compare?}, journal={Journal of Gerontology}, volume= 34, year= 1979, pages={{687-91}} } @article{Lassen05, author={David Dreyer Lassen}, title={The Effect of Information on Voter Turnout: Evidence from a Natural Experiement}, journal={American Journal of Political Science}, volume={2005}, year={49}, pages={103-118}, month={January}, number={1} } @article{Lau97, author={Tai-Shing Lau}, title={The Latent Class Model for Multiple Binary Screening Tests}, journal={Statistics in Medicine}, volume={16}, year={1997}, pages={2283-2295} } @article{LauIbr95, author={Purushottam W. Laud and Joseph G. Ibrahim}, title={Predictive Model Selection}, journal= jrssb, volume= 57, year= 1995, pages={247--262}, number= 1 } @article{LauIbr96, author={Purushottam W. Laud and Joseph G. Ibrahim}, title={Predictive Specification of Prior Model Probabilities in Variable Selection}, journal={Biometrika}, volume= 83, year= 1996, pages={267--274}, number= 2 } @article{LauSmiSta00, author={Jennifer L. Lauby and Philip J. Smith and Michael Stark and Bobbie Person and Janet Adams}, title={A Community-Level HIV Prevention Intervention for Inner-City Women: Results of the Women and Infants Demonstration Projects}, journal={American Journal of Public Health}, volume={90}, year={2000}, pages={216-222}, month={February}, number={2} } @article{LavBenGar03, author={Michael Laver and Kenneth Benoit and John Garry}, title={{Extracting Policy Positions from Political Texts Using Words as Data}}, journal={American Political Science Review}, volume={97}, year={2003}, pages={311-331}, number={2} } @book{Leamer78, author={Edward Leamer}, title={Specification Searches}, publisher={Wiley}, year= 1978, address={New York} } @article{Lebow00, author={Richard Ned Lebow}, title={What's so Different About a Counterfactual?}, journal= wp, volume= 52, year= 2000, pages={550--85}, month={July} } @misc{Lechner00, author={Michael Lechner}, title={A note on the common support problem in applied evaluation studies}, year= 2000, howpublished={{http://www.siaw.unisg.ch/lechner}}, note={University of St. Galen} } @incollection{Lechner99, author={Michael Lechner}, title={Identification and Estimation of Causal Effects of Multiple Treatments under the Conditional Independence Assumption}, booktitle={Econometric Evaluation of Labour Market Policies}, publisher={Physica}, address={Heidelberg}, editor={Lechner, M. and Pfeiffer, F.}, year= 2001, pages={43--58} } @article{LedBre59, author={S. Ledermann and J. Breas}, title={{Les Dimensions de la Mortalit{\'e}}}, journal={Population}, volume= 14, year= 1959, pages={637--682}, note={[in French]} } @article{Lee00, author={Ronald D. Lee}, title={The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications}, journal={North American Actuarial Journal}, volume= 4, year= 2000, pages={80--93}, number= 1 } @article{Lee00a, author={Ronald D. Lee}, title={{Long-Term Projections and the US Social Security System}}, journal={Population and Development Review}, volume= 26, year= 2000, pages={137--143}, month={March}, number= 1 } @article{Lee93, author={Ronald D. Lee}, title={{Modeling and Forecasting the Time Series of US Fertility: Age Patterns, Range, and Ultimate Level}}, journal={International Journal of Forecasting}, volume= 9, year= 1993, pages={187--202} } @article{LeeCar92, author = {Ronald D. Lee and Lawrence R. Carter}, title = {{Modeling and Forecasting U.S. Mortality}}, journal = jasa, volume = 87, year = 1992, month = {September}, pages = {659--675}, number = 419 } @article{LeeCar92b, author={Ronald D. Lee and Lawrence R. Carter}, title={Rejoinder}, journal= jasa, volume= 87, year= 1992, pages={674--675}, month={September}, number= 419 } @article{LeeCarTul95, author={Ronald D. Lee and Lawrence Carter and S. Tuljapurkar}, title={Disaggregation in Population Forecasting: Do We Need It? And How to Do it Simply}, journal={Mathematical Population Studies}, volume={5}, year={1995}, pages={217--234}, month={July}, number={3}, annote={Authors describe a model for reducing the dimensionality of the forecasting problem by modeling the evolution over time of the age schedules of vital rates, reducing the problem to forecasting a single parameter for fertility and another for mortality. Authors also show how one can fit the model more simply and prepare integrated forecasts for a collection of regions, and discuss alternate approaches to forecasting the estimated indices of fertility and mortality, including state-space methods.} } @misc{LeeHigBiFerWes00, author={Herbert Lee and David Higdon and Zhuoxin Bi and Marco Ferreira and Mike West}, title={Markov Random Field Models for High-Dimensional Parameters in Simulations of Fluid Flow in Porous Media}, year= 2000, howpublished={Discussion Paper \#00-35, Institute of Statistics and Decision Sciences, Duke University} } @book{LeeJohZel96, author={Jack C. Lee and Wesley O. Johnson and Arnold Zellner}, title={Modelling and Prediction: Honoring Seymour Geisser}, publisher={Springer}, year={1996}, editor={Jack C. Lee and Wesley O. Johnson and Arnold Zellner} } @article{LeeMil01, author={Ronald D. Lee and Timothy Miller}, title={Evaluating the Performance of the Lee-Carter Approach to Modeling and Forecasting Mortality}, journal={Demography}, volume= 38, year= 2001, pages={537--549}, month={November}, number= 4 } @article{LeeRof94, author={Ronald D. Lee and R. Rofman}, title={Modeling and Projecting Mortality in Chile}, journal={Notas Poblacion}, volume={22}, year={1994}, pages={183--213}, month={Jun}, number={59}, annote={Authors extend the Lee-Carter method to deal with various problems of incomplete data common in Third World populations, and then apply the method to forecast mortality in Chile.} } @article{LeeSki99, author={Ronald D. Lee and Jonathan Skinner}, title={Will Aging Baby Boomers Bust the Federal Budget}, journal={Journal of Economic Perspectives}, volume= 13, year= 1999, pages={117--140}, month={Winter}, number= 1 } @article{LeeTul94, author={Ronald D. Lee and S. Tuljapurkar}, title={{Stochastic Population Projections for the U.S.: Beyond High, Medium and Low}}, journal= jasa, volume= 89, year= 1994, pages={1175--1189}, month={December}, number= 428 } @article{LeeTul98, author={Ronald D. Lee and S. Tuljapurkar}, title={{Uncertain Demographic Futures and Social Security Finances}}, journal={American Economic Review: Papers and Proceedings}, year= 1998, pages={237--241}, month={May} } @incollection{LeeTul98a, author={Ronald D. Lee and S. Tuljapurkar}, title={{Stochastic Forecasts for Social Security}}, booktitle={Frontiers in the Economics of Aging}, publisher={University of Chicago Press}, year= 1998, address={Chicago}, editor={David Wise}, pages={393--420} } @techreport{LenFox06, author={Amanda Lenhart and Susannah Fox}, title={{Bloggers: A Portrait of the Internet's New Storytellers}}, institution={Pew Internet and American Life Project}, year= 2006, note={{http://207.21.232.103/pdfs/PIP\%20Bloggers\%20Report\%20July\%2019\%202006.pdf}} } @book{LenHsu99, author={T. Leonard and J.S.J. Hsu}, title={Bayesian Methods}, publisher={Cambridge University Press}, year= 1999, address={Cambridge} } @unpublished{LeuSia03, author={E. Leuven and B. Sianesi}, title={psmatch2}, note={{Stata module to perform full {M}ahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Available at: http://www1.fee.uva.nl/scholar/mdw/leuven/stata}}, year= 2003 } @misc{LeuSia04, author={Edwin Leuven and Barbara Sianesi}, title={PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing}, year= 2004, howpublished={EconPapers}, note={{http://econpapers.repec.org/software/bocbocode/S432001.htm}} } @article{LeuTanLue97, author={Kai-Kuen Leung, Li-Yu Tang, and Bee-Horng Lue}, title={Self-Rated Health and Mortality in Chinese Institutional Elderly Persons}, journal={Journal of Clinical Epidemiology}, volume= 50, year= 1997, pages={{1107-16}}, number= 10 } @book{Levinson01, author={Sanford Levinson}, title={What is the Constitution's Role in Wartime: Why Free Speech and Other Rights Are Not as Safe as You Might Think}, publisher={?}, year= 2001 } @article{LevKas70, author={P.S. Levy and E. H. Kass}, title={A three population model for sequential screening for Bacteriuria}, journal={American Journal of Epidemiology}, volume={91}, year={1970}, pages={148-154} } @article{Lewis01, author={Jeffrey B. Lewis}, title={Estimating Voter Preference Distributions from Individual-Level Voting Data}, journal={Political Analysis}, volume= 9, year= 2001, pages={275-297}, month={June}, number= 3 } @article{Lewis03, author={Anthony Lewis}, title={Marbury v. Madison v. Ashcroft}, journal={New York Times}, year={2003}, month={February 24, A17} } @incollection{Lewis05, author={Maureen Lewis}, title={Improving Efficiency and Impact in Health Care Services: Lessons from Central America}, booktitle={Health Systems Innovation in Central America}, publisher={The World Bank}, year= 2005, address={Washington, D.C.}, editor={Gerard M. La Forgia} } @inbook{Lewis05, author={Maureen Lewis}, title={Health Sysems Innovatin in Central American}, chapter={Improving Efficiency and Impact in Health Care Services - Lessons from Central American }, year={2005}, publisher={The World Bank}, address={Washington DC}, editor={Gerard M. La Forgia} } @book{lewis73, author={David K. Lewis}, title={Counterfactuals}, publisher={Cambridge: Harvard University Press}, year= 1973 } @article{Lewis99b, author={John A. Lewis}, title={Statistical Principles for Clinical Trials (ICH E9) An Introductory Note on an International Guideline}, journal={Statistics in Medicine}, volume={18}, year={1999}, pages={1903-1904} } @article{LewLev89, author={R.A. Lew and P.S. Levy}, title={Estimation of prevalence on the basis of screening tests}, journal={Statistics in Medicine}, volume={8}, year={1989} } @article{LewLev89, author={Robert A. Lew and Paul S. Levy}, title={Estimation of Prevalence on the Basis of Screening Tests}, journal={Statistics in Medicine}, volume={8}, year={1989}, pages={1225-1230} } @book{Li95, author={S.Z. Li}, title={Markov Random Field Modeling in Computer Vision}, publisher={Springer-Verlag}, year= 1995 } @article{LicLipDan92, author={Allen S. Lichter and Marc E. Lippman and David N. Danforth Jr and Teresa d'Angelo and Seth M. Steinberg and Ernest deMoss and Harold D. MacDonald and Cheryl M. Reichert and Maria Merino and Sandra M. Swain and Kenneth Cowan and Lynn H. Gerber and Judith L. Bader and Peggie A. Findlay and Wendy Schain and Catherine R. Gorrell and Karen Straus and Steven A. Rosenberg and Eli Glatstein}, title={Mastectomy Versus Breast-Conserving Therapy in the Treatment of Stage I and II Carcinoma of the Breast: A Randomized Trial at the National Cancer Institute}, journal={Journal of Clinical Oncology}, volume= 10, year= 1992, pages={976-983}, month= June , number= 6 } @article{LilPer94, author={David E. Lilienfeld and Daniel P. Perl}, title={Projected Neurodegenerative Disease Mortality among Minorities in the United States}, journal={Neuroepidemiology}, volume= 13, year= 1994, pages={179--186} } @article{LinCutZwi02, author={Shin Lin and David L. Cutler and Michael E. Zwick and Aravinda Chakravarti}, title={Haplotype Inference in Random Population Samples}, journal={American Journal of Human Genetics}, volume= 71, year= 2002, pages={1129--1137} } @book{LinHam97, title={Handbook of Modern Item Response Theory}, publisher={Springer}, year= 1997, editor={Wim Van Der Linden and Ronald K. Hambleton}, address={New York} } @article{LinLin80, author={Bernard S. Linn and Margaret W. Linn}, title={Objective and self-assessed health in the old and very old}, journal={Social Science and Medicine }, volume={{14A}}, year= 1980, pages={{311-15}} } @article{LinPekWan05, author={Peter K. Lindenauer and Penelope Pekow and Kaijun Wang and Dheeresh K. Mamidi and Benjamin Guierrez and Evan M. Benjamin}, title={Perioperative beta-blocker therapy and mortality after major noncardiac surgery}, journal={New England Journal of Medicine}, volume={353}, year={2005}, pages={349-361}, month={July}, number={4} } @article{LinSmi72, author={D. V. Lindley and A. F. M. Smith}, title={{B}ayes Estimates for the Linear Model}, journal={Journal of the Royal Statistical Society B}, volume= 34, year= 1972, pages={1-41}, number= 1 } @book{Lipset63, author={Lipset, Seymour Martin}, title={Political Man: The Social Basis of Politics}, publisher={Anchor Books}, year= 1963, address={Garden City, NY} } @article{LisMilFre03, author={John A. List and Daniel L. Millimet and Per G. Fredriksson and W. Warren McHone}, title={Effects of Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator}, journal={The Review of Economics and Statistics}, volume={85}, year={2003}, pages={944-952}, month={November}, number={4} } @article{LitAn04, author={Roderick Little and Hyonggin An}, title={Robust Likelihood-Based Analysis of Multivariate Data with Missing Values}, journal={Statistica Sinica}, volume={14}, year={2004}, pages={949-968} } @article{LitAn04, author={Roderick Little and Hyonggin An}, title={Robust Likelihood-Based Analysis of Multivariate Data with Missing Values}, journal={Statistica Sinica}, volume={14}, year={2004}, pages={949-968} } @book{LitRub02, author={Roderick J.A. Little and Donald B. Rubin}, title={Statistical Analysis with Missing Data, 2nd Edition}, publisher={John Wiley and Sons}, year={2002}, address={New York, New York} } @article{LitRub89, author={Rodrick J. Little and Donald Rubin}, title={The Analysis of Social Science Data with Missing Values}, journal={Sociological Methods and Research}, volume={18}, year={1989}, pages={292-326} } @inbook{LitSch95, author={Rodrick J. Little and N. Schenker}, title={Handbook of Statistical Modeling for the Social and Behavioral Sciences}, chapter={Missing Data}, year={1995}, pages={39-75} } @unpublished{Little05, author={Roderick Little}, title={Calibrated Bayes: A Bayes/Frequentist Roadmap}, note={University of Michigan}, year={2005}, month={September} } @article{Little92, author={Roderick J. Little}, title={Regression with Missing X's: A Review}, journal={Journal of the American Statistical Association}, volume={87}, year={1992}, pages={1227-1237} } @unpublished{LiuHuChe05, author={Bing Liu and Minqing Hu and Junsheng Cheng}, title={Opinion Observer: Analyzing and Comparing Opinions on the Web}, note={Bing Liu Dept of Computer Science; Univ of Illinois at Chicago, 851 south Morgan St. Chicago, IL 60607-7053, liub@cs.uic.edu}, year={2005}, month={May} } @article{LiuWonKon94, author={J. Liu and W.H. Wong and A. Kong}, title={Covariance Structure of the Gibbs Sampler with Applications to the Comparisons of Estimators and Augmentation Schemes}, journal={Biometrika}, volume={81}, year={1994}, pages={27-40} } @article{LiWen05, author={Quan Li and Ming Wen}, title={The Immediate and Lingering Effects of Armed Conflict on Adult Mortality: A Time-Series Cross-National Analysis}, abstract={This research investigates the effect of armed conflict on adult mortality across countries and over time. Theoretical mechanisms are specified for how military violence influences adult mortality, both immediately and over time after conflict. The effects of aggregate conflict, interstate and intrastate conflicts, and conflict severity are explored. The Heckman selection model is applied to account for the conflict-induced missing data problem. A pooled analysis across 84 countries for the period from 1961 to 1998 provides broad empirical support for the proposed theoretical expectations across both genders. This study confirms the importance of both the immediate and the lingering effect of military conflict on the mortality of the working-age population. The immediate effect of civil conflict is much stronger than that of the interstate conflict, while the reverse applies to the lingering effect. Both the immediate and the lingering effects of severe conflict are much stronger than those of minor conflict. While men tend to suffer higher mortality immediately from intrastate conflict and severe conflict, women in the long run experience as much mortality owing to the lingering effects of these conflicts. The mortality data show a strong data selection bias caused by military conflict. The research findings highlight the imperative for negotiating peace. Preventing a contest from escalating into a severe conflict can produce noticeable gains in saved human lives.}, journal={Journal of Peace Research}, volume={42}, year={2005}, pages={471-492}, month={July}, number={4} } @article{LoeFulKag03, author={Susanna Loeb and Bruce Fuller and Sharon Lynn Kagan and Bidemi Carrol}, title={How Welfare Reform Affects Young Children: Experimental Findings from Connecticut - A Research Note}, journal={Journal of Policy Analysis and Management}, volume={22}, year={2003}, pages={537-550}, number={4} } @article{Londregan00, author={John Londregan}, title={Estimating Legislator's Preferred Points}, journal={Political Analysis}, volume= 8, year= 2000, pages={21--34}, month={Winter}, number= 1 } @book{Londregan00b, author={John Londregan}, title={Legislative Institutions and Ideology in Chile}, publisher={Cambridge University Press}, year= 2000, address={New York} } @book{LonFre06, author={J. Scott Long and Jeremy Freese}, title={Regression Models for Categorical Dependent Variables Using Stata}, publisher={Stata Press}, year={2006}, address={College Station, TX} } @article{LooBee46, author={Loomis, C. P. and Beegle, J. A.}, title={The Spread of German Nazism in Rural Areas}, journal= asr, volume= 11, year= 1946, pages={724-734} } @book{LopAhmGui00, author = {Alan Lopez and O. Ahmed and M. Guillot and B.D. Ferguson and J.A. Salomon and C.J.L. Murray and K.H. Hill}, title = {World Mortality in 2000: Life Tables for 191 Countries}, publisher = {World Health Organization}, year = 2000, address = {Geneva} } @article{LowAltFer98, author={Robert Lowry and James E. Alt and Karen Ferree}, title={Fiscal Policy Outcomes and Electoral Accountability in the American States}, journal={American Political Science Review}, volume= 92, year= 1998, pages={759-777}, month={December} } @article{Lowenstein2006, author={Daniel H. Lowenstein}, title={Vieth's Gap: Has the Supreme Court Gone from Bad to Worse on Partisan Gerrymandering?}, journal={Cornell Journal of Law and Public Policy}, volume={N:X}, year={2006}, pages={101-130}, month={January} } @article{LozSolGak06, author = {Rafael Lozano and Patricia Soliz and Emmanuela Gakidou and Jesse Abbott-Klafter and Dennis M. Feehan and Cecilia Vidal and Juan Pablo Ortiz and Christopher J.L. Murray}, title = {Benchmarking of performance of Mexican States with Effective Coverage}, journal = {The Lancet}, volume = {368}, year = {2006}, pages = {1729-1741}, month = {November} } @article{lubin94, author = {J.H. Lubin and M.H. Gail}, title = {Sampling Strategies in Nested Case-Control Studies}, journal = {Environmental Health Perspectives}, volume = 102, year = 1994, pages = {47-51}, number = {suppl 8} } @article{Lubkemann05, author = {Stephen C. Lubkemann}, title = {Migratory Coping in Wartime Mozambique: An Anthropology of Violence and Displacement in Fragmented Wars}, journal = {Journal of Peace Research}, volume = {42}, year = {2005}, pages = {493-508}, month = {July}, number = {4} } @article{LumHea99, author = {Thomas Lumley and Patrick Heagerty}, title = {Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression}, journal = {jrssb}, volume = {61}, year = {1999}, pages = {459--477}, number = {2} } @article{LunSmi05, author = {Jennifer Hickes Lundquist and Herbert L. Smith}, title = {Family Formation Among Women in the U.S. Military: Evidence from the NLSY}, journal = {Journal of Marriage and Family}, volume = {67}, year = {2005}, pages = {1-13}, month = {February} } @article{LuRos04, author={Bo Lu and Paul R. Rosenbaum}, title={Optimal Pair Matching With Two Control Groups}, journal={Journal of Computational and Graphical Statistics}, volume={13}, year={2004}, pages={422-434}, number={2} } @inbook{Lustig94, author={Nora Lustig}, title={Solidarity as a Strategy of Poverty Alleviation}, chapter={5}, year={1994}, publisher={Center for U.S.-Mexican Studies}, pages={79-96}, series={U.S.-Mexico Contemporary Perspectives Series, 6}, address={University of California, San Diego} } @article{LuZanHor01, author = {Bo Lu and Elaine Zanuto and Robert Hornik and Paul R. Rosenbaum}, title = {Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse}, journal = {Journal of the American Statistical Association}, volume = {96}, year = {2001}, pages = {1245-1253}, month = {December}, number = {456} } @techreport{LymVar03, author = {Peter Lyman and Hal R. Varian}, title = {{How much information 2003}}, institution = {University of California}, year = 2003, note = {{http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/}} } @article{Lynch03, author = {Lynch, C.A.}, title = {{Institutional Repositories: Essential Infrastructure For Scholarship In The Digital Age}}, journal = {portal: Libraries and the Academy}, volume = {3}, year = {2003}, pages = {327--336}, number = {2} } @article{LynMcc92, author = {Henry S. Lynn and Charles E. McCulloch}, title = {When Does it Pay to Break the Matches for Analysis of a Matched-Pairs Design?}, journal = {Biometrics}, volume = {48}, year = {1992}, pages = {397-409}, month = {June} } @article{MacKunGro99, author = {J.P. Mackenbach and A.E. Kunst and F. Groenhof and J.K. Borgan and G. Costa and F. Faggiano }, title = {Socioeconomic inequalities in mortality among women and among men: an international study}, journal = {American Journal of Public Health}, volume = 89, year = 1999, pages = {1800-1806}, number = 12 } @book{Macridis55, author={Roy C. Macridis}, title={The Study of Comparative Government}, publisher={Doubleday and Co.}, year= 1955, address={New York} } @article{MacRivJur06, author = {Ellen J. MacKenzie and Frederick P. Rivara and Gregory J. Jurkovich and Avery B. Nathens and Katherine P. Frey and Brian L. Egleston and David S. Salkever and Daniel O Scharfstein}, title = {A National Evaluation of the Effect of Trauma-Center Care on Mortality}, journal = {New England Journal of Medicine}, volume = {354}, year = {2006}, pages = {366-378}, month = {January} } @article{MadDou64, author = {George L. Maddox and Elizabeth B. Douglas}, title = {Self-Assessments of Health: A Longitudinal Study of Elderly Subjects}, journal = {Journal of Health and Social Behavior }, volume = 14, year = 1973, pages = {{87-93}} } @article{MadNel92, author = {W.R. Madych and S.A. Nelson}, title = {Bounds on Multivariate Polynomials and Exponential Error Estimates for Multiquadric Interpolation}, journal = {Journal of Approximation Theory}, volume = 70, year = 1992, pages = {94--114} } @article{Malaker86, author={CR Malaker}, title={Estimation of Adult Mortality in India: 1971--1981}, journal={Demography India}, volume= 15, year= 1986, pages={126--136} } @article{ManKarMar03, author = {Kristiina Manderbacka, et al}, title = {The Effect of Point of Reference on the Association Between Self-Rated Health and Mortality}, journal = {Social Science and Medicine}, volume = 56, year = 2003, pages = {{1447-52}} } @book{ManSch99, author={Christopher D. Manning and Hinrich Sch{\"u}tze}, title={Foundations of Statistical Natural Language Processing}, publisher={Massachusetts Institute of Technology}, year={1999}, address={Cambridge, MA} } @book{Manski05, author={Charles F. Manski}, title={Social choice with Partial Knowledge of Treatment Response}, publisher={Princeton University Press}, year={2005}, series={Econometric Institute Lectures} } @article{Manski77, author={Charles F. Manski}, title={The Estimation of Choice Probabilities from Choice Based Samples}, journal={Econometrics}, volume= 45, year= 1977, pages={1977--88}, month={November}, number= 8 } @article{Manski90, author = {Charles F. Manski}, title = {The Use of Intentions Data to Predict Behavior: A Best-Case Analysis}, journal = {Journal of the American Statistical Association}, volume = {85}, year = {1990}, pages = {934-940}, month = {December}, number = {412} } @book{Manski95, author={Charles F. Manski}, title={Identification Problems in the Social Sciences}, publisher={Harvard University Press}, year= 1995 } @inproceedings{manski99, author = {Charles F. Manski}, title = {Nonlinear Statistical Inference: Essays in Honor of Takeshi Amemiya}, booktitle = {Nonparametric Identification Under Response-Based Sampling}, year = 1999 , publisher = cup, editor = {C. Hsiao and K. Morimune and J. Powell} } @article{ManSzoKoh01, author = {Mandl, K.D. and Szolovits, P. and Kohane, I.S.}, title = {{Public standards and patients' control: how to keep electronic medical records accessible but private}}, journal = {BMJ}, volume = {322}, year = {2001}, pages = {283--7}, number = {7281} } @article{ManSzoKoh01, author={Kenneth D. Mandl and Peter Szolovits and Isaac S. Kohane}, title={Public standards and patients' control: how to keep electronic medical records accessible but private}, journal={British Medical Journal}, volume={322}, year={2001}, pages={283-287}, month={February}, publisher={British Medical Journal} } @article{mantel73, author={N. Mantel}, title={Synthetic Retrospective Studies and Related Topics}, journal={Biometrics}, volume= 29, year= 1973, number={479-486} } @article{ManTudDie06, author={Dennis T. Mangano and Julia C. Tudor and Cynthia Dietzel}, title={The Risk Associated wtih Aprotinin in Cardiac Surgery}, journal={The New England Journal of Medicine}, volume={354}, year={2006}, pages={353-365}, month={January}, number={4} } @article{ManRao04, title = {{Community-Based and-Driven Development: A Critical Review}}, author = {Mansuri, G. and Rao, V.}, journal = {The World Bank Research Observer}, volume = {19}, number = {1}, pages = {1-39}, year = {2004} } @article{MarCamFay91, author = {Elizabeth A. Martin and Pamela C. Campanelli and Robert E. Fay}, title = {An Application of Rasch Analysis to Questionnaire Design: Using Vignettes to Study the Meaning of `Work' in the Current Population Survey}, journal = {The Statistician}, volume = 40, year = 1991, pages = {265--276}, month = {September}, number = 3 } @article{March57, author={James G. March}, title={party Legislative Representation as a Function of Election Results}, journal={1957}, volume={21}, year={1957-1958}, pages={521-542}, month={Winter}, number={4} } @article{Marcus00, author={George E. Marcus}, title={{Emotions in Politics}}, journal={Annual Review of Political Science}, volume={3}, year={2006}, pages={221-50} } @article{Marcus88, author={George E. Marcus}, title={{The Structure of Emotional Response: The 1984 Presidential Candidates}}, journal={American Political Science Review}, volume={82}, year={1988}, pages={737-761}, number={3} } @article{MarDiePer93, author={Donald C. Martin and Paula Diehr and Edward B. Perrin and Thomas D. Koepsell}, title={The Effect of Matching on the Power of Randomized Community Intervention Studies}, journal={Statistics in Medicine}, volume={12}, year={1993}, pages={329-338} } @article{MarHusLob95, author={David Marsh and Khatidja Husein and Melvyn Lobo and Mehboob Ali Shah and Stephen Luby}, title={Verbal autopsy in Karachi slums: comparing single and multiple cause of child deaths}, journal={Health Policy and Planning}, volume={10}, year={1995}, pages={395-403}, number={4} } @article{MarHusLob95, author={David Marsh and Khatidja Husein and Melvyn Lobo and Mehboob Ali Shah and Stephen Luby}, title={Verbal autopsy in Karachi slums: comparing single and multiple cause of child deaths}, journal={Health Policy and Planning}, volume={10}, year={1995}, pages={395-403}, number={4} } @book{MarKenBib79, author={K. V. Mardia and J. T. Kent and J. M. Bibby}, title={Multivariate Analysis}, publisher= ap, year={1979}, address={London} } @article{MarMajRas93, author={David Marsh and Nuzhat Majit and Zeba Rasmussen and Khalid Mateen and Arif Amin Khan}, title={Cause-Specific Child Mortality In A Mountainous Community In Pakistan By Verbal Autopsy}, journal={Journal of the Pakistan Medical Association}, volume={43}, year={1993}, pages={226-229}, month={November}, number={11} } @article{MarMajRas93, author={David Marsh and Nuzhat Majit and Zeba Rasmussen and Khalid Mateen and Arif Amin Khan}, title={Cause-Specific Child Mortality In A Mountainous Community In Pakistan By Verbal Autopsy}, journal={Journal of the Pakistan Medical Association}, volume={43}, year={1993}, pages={226-229}, month={November}, number={11} } @misc{MarQui01, author={Andrew D. Martin and Kevin M. Quinn}, title={The Dimensions of {S}upreme {C}ourt Decision Making: Again Revisiting {T}he {J}udicial {M}ind}, year= 2001, howpublished={Paper presented at the Annual Meeting of the Midwest Political Science Association} } @manual{MarQui05, author={Andrew D. Martin and Kevin M. Quinn}, title={MCMCpack: Markov chain Monte Carlo (MCMC) Package}, year={2005}, url={{http://mcmcpack.wustl.edu}} } @article{MarSadFik03, author={David R. Marsh and Salim Sadruddin and Fariyal F. Fikree and Chitra Krishnan and Gary L. Darmstadt}, title={Validation of verbal autopsy to determine the cause of 137 neonatal deaths in Karachi, Pakistan}, journal={2003}, volume={17}, year={2003}, pages={132-142} } @article{MarSadFik03, author={David R. Marsh and Salim Sadruddin and Fariyal F. Fikree and Chitra Krishnan and Gary L. Darmstadt}, title={Validation of verbal autopsy to determine the cause of 137 neonatal deaths in Karachi, Pakistan}, journal={2003}, volume={17}, year={2003}, pages={132-142} } @article{Marshall91, author={R.J. Marshall}, title={Mapping Disease and Mortality Rates using Empirical Bayes Estimators}, journal={Applied Statistics}, volume= 40, year= 1991, pages={283--294}, number= 2 } @article{MarSmiSta91, author={M.G. Marmot and G.D. Smith and S. Stansfeld and C. Patel and F. North and J. Head and I. White and E. Brunner and A. Feeney}, title={Health inequalities among British civil servants: the Whitehall II study [see comments].}, journal= lan, year={1991}, optvolume={337}, optpages={1387--1393} } @book{Martin92, author={Lisa Martin}, title={Coercive Cooperation: Explaining Multilateral Economic Sanctions}, publisher={Princeton University Press}, year={1992}, note={Please inquire with Lisa Martin before publishing results from these data, as this dataset includes errors that have since been corrected.} } @article{MatFatIno05, author = {Colin D. Mathers and Doris Ma Fat and Mie Inoue and Chalapati Rao and Alan Lopez}, title = {Counting the dead and what they died from: an assessment of the global status of cause of death data}, journal = {Bulletin of the World Health Organization}, volume = 83, year = 2005, pages = {171--177}, month = {March}, number = 3 } @unpublished{MatLopSte03, author={Colin D. Mathers and Alan Lopez and Claudia Stein and Doris Ma Fat and Chalapati Rao and Mie Unoue and Kenji Shiubuya and Niels Tomijima and Christina Bernard and Hongyi Xu}, title={Deaths and Disease Burden by Cause: Global Burden of Disease Estimates for 2001 by World Bank Country Groups}, note={Evidence and Information for Policy, World Health Organization, Geneva and School of Population health, University of Queensland, Brisbane, Austrailia}, year={2003} } @unpublished{MatSteFat02, author={Colin D. Mathers and Claudia Stein and Doris Ma Fat and Chalapati Rao and Mie Unoue and Niels Tomijima and Christina Bernard and Alan D. Lopez and Christopher J.L. Murray}, title={Global Burden of Disease 2000: Version 2 Methods and Results}, note={Global Programme on Evidence for Health Policy Discussion Paper No. 50 World Health Organization}, year={2002}, month={October} } @article{MauRos97, author={Gillian H. Maude and David A. Ross}, title={The Effect of Different Sensitivity, Specificity and Cause-Specific Mortality Fractions on the Estimation of Differences in Cause-Specific Mortality Rates in Children from Studies Using Verbal Autopsies}, journal={International Journal of Epidemiology}, volume={26}, year={1997}, pages={1097-1106}, number={5} } @techreport{Mayaud01, author={Philippe Mayaud}, title={Aids-Related Research Projects}, institution={The London School of Hygiene \& Tropical Medicine}, year={2001}, address={London School of Hygiene \& Tropical Medicine www.ishtm.ac.uk} } @book{McCNel89, author={Peter McCullagh and James A. Nelder}, title={Generalized Linear Models}, publisher={Chapman \& Hall}, year={1989}, series={Monograph on Statistics and Applied Probability}, edition={2nd}, number={37} } @inbook{McConahay86, author={John B. McConahay}, title={Prejudice, Discrimination, and Racism: Theory and Research}, chapter={Modern Racism Ambivalence, and the Modern Racism Scale}, year={1986}, publisher={New York: Academic Press}, editor={J. Dovidio and S.L. Gaertner} } @article{MccRidMor04, author={Daniel F. McCaffrey and Greg Ridgeway and Andrew R. Morral}, title={Propensity Score Estimation With Boosted Regression for Evaluating Causal Effects in Observational Studies}, journal={Psychological Methods}, volume={9}, year={2004}, pages={403-425}, number={4} } @article{McCShaWan94, author={John McCallum, DPhil, MPhil, Bruce Shadbolt, PhD, and Dong Wang, BSc}, title={Self-Rated Health and Survival: A 7-year Follow-Up study of Australian Elderly.}, journal={American Journal of Public Health}, volume= 84, year= 1994, pages={{1100-05}} } @unpublished{McDonald06a, author={Michael P. McDonald}, title={Seats to Votes Ratios in the United States}, note={George Mason University, Dept of Public and International Affairs 4400 University Dr., 3-F4 Fairfax, VA 22030-4444 (703)-993-4191 mmcdon@gmu.edu}, year={2006} } @unpublished{McDonald06b, author={Michael D. McDonald}, title={A Standard for Detecting and Remedying Gerrymanders}, note={Department of Political Science, Binghamton University- SUNY, Binghamton NY 13902-6000, (617) 777-2946, mdmcd@binghamton.edu}, year={2006} } @article{McKibbin69, author={Ross McKibbin}, title={The Myth of the Unemployed: Who did Vote for the Nazis?}, journal={Australian Journal of Politics and History}, volume= 15, year= 1969, pages={25--40}, number= 2 } @book{McLThr97, author={Geoffrey J. McLachlan and Thriyambakam Krishan}, title={The EM Algorithm and Extensions}, publisher={New York: Wiley} } @article{McNown92, author={Robert McNown}, title={Comment}, journal= jasa, volume= 87, year= 1992, pages={671--672}, number= 419 } @article{McNRog89, author={Rober McNown and Andrei Rogers}, title={Forecasting Mortality: A Parameterized Time Series Approach}, journal={Demography}, volume= 26, year= 1989, pages={645--660}, number= 4 } @article{McNRog92, author={Robert McNown and Andrei Rogers}, title={Forecasting Cause-Specific Mortality Using Time Series Methods}, journal={International Journal of Forecasting}, volume= 8, year= 1992, pages={413--432} } @unpublished{McqLasLai06, author={Matthew B. McQueen and Jessica Lasky-Su and Nan M. Laird and Christoph Lange}, title={Screening and Testing using the Same Data Set: A Testing Strategy for Genome-Wide Association Studies for Case-Control and Case-Cohort Designs}, year={2006} } @article{Mead92, author={A. Mead}, title={Review of the Development of Multidimensional Scaling Methods}, journal={The Statistician}, volume= 41, year= 1992, pages={27--39}, month={April}, number= 1 } @article{MebSek04, author={Walter Mebane and Jasjeet Sekhon}, title={Robust Estimation and Outlier Detection in Overdispersed Multinomial Models of Count Data}, journal= ajps, volume= 48, year= 2004, pages={391--410}, month={April} } @article{MeiKarPar04, author={Bettina Meinow et al.}, title={The effect of the duration of follow-up in mortality analysis: The temporal pattern of different predictors}, journal={Journal of Gerontology: Social Sciences}, volume={{59B}}, year={2004}, pages={{S181-89}}, number={3} } @article{Meng94, author={Xiao-Li Meng}, title={Multiple-Imputation Inferences with Uncongenial Sources of input}, journal={Statistical Science}, volume={9}, year={1994}, pages={538-573}, number={4} } @article{Meng94b, author={X.L. Meng}, title={Posterior Predictive p-Values}, journal={Annals of Statistics}, volume={22}, year={1994}, pages={1142-1160}, number={3} } @article{MenRom03, author={Xiao-Li Meng and Marin Romero}, title={Discussion: Efficiency and Self-Efficiency with Multiple Imputation Inference}, journal={International Statistical Review}, volume={71}, year={2003}, pages={607-618}, number={3} } @article{MenRub92, author={X.L. Meng and Donald Rubin}, title={Performing Likelihood Ratio Tests with Multiply-imputed Data Sets}, journal={Biometrika}, volume={79}, year={192}, pages={103-111} } @article{Metetal53, author={N. Metropolis and A. W. Rosenbluth and M. N. Rosenbluth and A. H. Teller and E. Teller}, title={Equation of State Calculations by Fast Computing Machines}, journal={Journal of Chemical Physics}, volume={21}, year= 1953, pages={1087-1092} } @article{MicBloHil04, author={Charles Michalopoulos and Howard S. Bloom and Carolyn J. Hill}, title={Can propensity-score methods match the findings from a random assignment evaluation of mandatory welfare-to-work programs?}, journal={Review of Economics and Statistics}, volume= 56, year= 2004, pages={156-179}, number= 1 } @article{Midlarsky05, author={Halbert White}, title={A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity}, abstract={This study seeks to distinguish between instances where genocide occurred and others where it might have been expected to occur but did not. Territorial loss, a corollary refugee influx, and a resulting contraction of socio-economic space are suggested to provide that distinction. Four analytic perspectives based on emotional reactions, class envy, prospect theory, and territoriality indicate the critical importance of loss. The theory is examined in the context of the mass murder of European Jewry including, of course, Germany and Austria, and all European German allies that allowed an indigenous genocidal impulse, willingness to comply with German genocidal policies, or an ability to resist German pressures for Jewish deportation. Three instances of perpetrating states - Italy, Vichy France, and Romania - emerge from the analysis. The latter two governments willingly collaborated with the Germans in victimizing their own Jewish citizenry, while Italy was on a genocidal path just prior to the German occupation. All five states mentioned above were found to experience considerable territorial loss and a contraction of socio-economic space. Bulgaria and Finland, on the other hand, actually expanded their borders at the start of the war and saved virtually all of their Jewish citizens. The importance of loss is demonstrated not only cross-sectionally, in the comparison between the five victimizers, on the one hand, and Bulgaria and Finland, on the other, but also diachronically, in the changing behavior over time of the genocidal and perpetrating states.}, journal={Econometrica}, volume={42}, year={1980}, pages={375-391}, month={July}, number={4}, optnumber={4}, optvolume={48}, optpages={817--838}, optmonth={May} } @article{Mierendorff30, author={Carl Mierendorff}, title={Gesicht und Charakter der nationalsozialistischen Bewegung}, journal={Gesellschaft}, volume= 7, year= 1930, pages={489--540}, number= 1 } @article{MiiVuoOja97, author={Seppo Miilunpalo, Ilkka Vuori, Pekka Oja, Matti Pasanen, and Helka Urponen}, title={Self Rated Health as a Health Measure: The Predictive Value of Self-Reported Health Status on the Use of Physician Services and on Mortality in the Working Age Population}, journal={Journal of Clinical Epidemiology}, volume= 50, year= 1997, pages={{517-28}} } @misc{MikSuc05, author={Gerome Miklau and Dan Suciu}, title={Managing Integrity for Data Exchanged on the Web}, year= 2005, month={16--17 June}, howpublished={Eighth International Workshop on the Web and Databases, Baltimore}, note={{http://webdb2005.uhasselt.be/papers/1-3.pdf}} } @article{Miller01, author={Tim Miller}, title={Increasing Longevity and Medicare Expenditures}, journal={Demography}, volume= 38, year= 2001, pages={215--226}, month={May}, number= 2 } @inbook{MilReiHes98, author={Arthur H. Miller and William M. Reisinger and Vicki L. Hesli}, title={Elections and Voters in Post-Communist Russia}, chapter={Leader Popularity and Party Development in Post-Soviet Russia }, year={100-135}, publisher={London: Edward Elgar}, editor={Matthew Wyman and Stephen White and Sarah Oates} } @article{MilRob89, author={Miller, Abraham H. and Robbins, James S.}, title={Who Did Vote for Hitler? A Reanalysis of the Lipset/Bendix Controversy}, journal={Polity}, volume= 21, year= 1989, pages={655-677}, number= 4 } @article{MilSha94, author={Paul Milgrom and Chris Shannon}, title={Monotone Comparative Statics}, journal={Econometrica}, volume= 62, year= 1994, pages={157--180}, month={January}, number= 1, annote={introduction of the single crossing property as a way to ensure monotonicity} } @article{MinRos00, author={Ming, K. and Rosenbaum, Paul R.}, title={Substantial gains in bias reduction from matching with a variable number of controls}, journal={Biometrics}, volume={56}, year={2000}, pages={118-124} } @article{MoeWal01, author={Karl Ove Moene and Michael Wallerstein}, title={Inequality, Social Insurance, and Redistribution}, journal={American Political Science Review}, volume={95}, year={2001}, pages={859-874}, month={December}, number={4} } @article{MoeWal03, author={Karl Ove Moene and Michael Wallerstein}, title={Earnings Inequality and Welfare Spending: A Disaggregated Analysis}, journal={World Politics}, volume={55}, year={2003}, pages={485-516}, month={July} } @book{Mommsen89, author={Hans Mommsen}, title={Die verspielte Freiheit --- der Weg der Republik von Weimar in den Untergang, 1918 bis 1933}, publisher={Propyl{\"a}en}, year= 1989 } @techreport{MonLopGel99, author={Manuel Montes-y-Gomez and Aurelio Lopez-Loez and Alexander F. Gelbukh and Grigori Sidorov and Adolfo Guzman-Arenas}, title={Text Mining: New Techniques and Applications}, institution={Center for computing Research of the National Polytechnic Institute, Mexico City}, year={1999}, month={August}, address={CIC, IPN, Laboratorio de Lenguaje Natural, Av. Juan de Dios Batiz, Mexico DF.}, volume={34} } @techreport{MonLopGel99b, author={Manuel Montes y Gomez and Aurelio Lopez Lopez and Alexander F. Gelbukh}, title={Text Mining as a Social Thermometer}, institution={Centro de Investigaci{\'o}n en Computaci{\'o}n}, year={1999}, address={CIC, IPN Laboratorio de Lenguaje Natural. Ave. Juan de Dios Batiz, Mexico DF} } @book{MonSam04, title={Decentralization and Democracy in Latin America}, publisher={University of Notre Dame Press}, year={2004}, editor={Alfred P. Montero and David J. Samuels}, address={Notre Dame, Indiana} } @book{Montgomery2001, author={Douglas C. Montgomery}, title={Design and Analysis of Experiments}, publisher={Wiley}, year={2001}, address={New York}, edition={5th} } @article{MorBlaTom03, author={Saul S. Morris and Robert E. Black and Lana Tomaskovic}, title={Predicting the distribution of under-five deaths by cause in countries without adequate vital registration systems}, journal={International Journal of Epidemiology}, volume={32}, year={2003}, pages={1041-1051} } @book{Morozov84, author={V.A. Morozov}, title={Methods for solving incorrectly posed problems}, publisher={Springer-Verlag}, year= 1984 , address={Berlin} } @article{MorSpi99, author={Mary J. Morrissey and Donna Spiegelman}, title={Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons}, journal={Biometrics}, volume={55}, year={1999}, pages={338-344}, month={June} } @unpublished{MorYamTat02, author={Satoshi Morinaga and Kenji Yamanishi and Kenji Tateishi and Toshikazu Fukushima}, title={Mining Product Reputations on the Web}, note={Satoshi Morinaga and Kenji Yamanishi NEC Corp. 4-1-1 Miyazaki Miyamae Kawasaki Kanagawa 216-8555 Japan Tel: 81-44-856-2143; morinaga@cw.jp.nec.com}, year={2002} } @unpublished{MorYamTat02, author={Satoshi Morinaga and Kenji Yamanishi and Kenji Tateishi and Toshikazu Fukushima}, title={Mining Product Reputations on the Web}, note={Satoshi Morinaga and Kenji Yamanishi NEC Corp. 4-1-1 Miyazaki Miyamae Kawasaki Kanagawa 216-8555 Japan Tel: 81-44-856-2143; morinaga@cw.jp.nec.com}, year={2002} } @article{MosSha82, author={Jana Mossey MPH, PhD, and Evelyn SHapira, MA}, title={Self-Rated Health: A Predictor of Mortality Among the Elderly}, journal={American Journal of Public Health}, volume= 72, year= 1982, pages={{800-08}} } @article{moynihan00, author={Ray Moynihan and Lisa Bero and Dennis Ross-Degnan and David Henry and Kriby Lee and Judy Watkins and Connie Mah and Stephen B. Soumerai}, title={Coverage by the News Media of the Benefits and Risks of Medications}, journal={New England Journal of Medicine}, volume= 342, year= 2000, pages={1645-1650}, month={June 1}, number= 22 } @article{MugLac02, author={Anthony Mughan and Dean Lacy}, title={Economic Performance, Job Insecurity, and Electoral Choice}, journal={British Journal of Political Science}, volume= 32, year= 2002, pages={513--533} } @book{MurEva03, title={Health Systems Performance Assessment: Debates, Methods and Empiricism}, publisher={World Health Organization}, year= 2003, editor={Christopher J.L. Murray and David B. Evans}, address={Geneva} } @book{MurLop96, title={The Global Burden of Disease}, publisher={Harvard University Press and WHO}, year= 1996 , editor={Christopher L.J.\ Murray and Alan D.\ Lopez} } @article{MurLop97, author={Christopher J. L. Murray and Alan D. Lopez}, title={Mortality by Cause for Eight Regions of the World: Global Burden of Disease Study}, journal={The Lancet}, volume={349}, year={1997}, pages={1269-1276} } @article{murphy69, author={George G. S. Murphy}, title={On Counterfactual Propositions}, journal={History and Theory}, volume= 9, year= 1969, pages={14-38} } @book{Murphy72, author={Paul L. Murphy}, title={The Meaning of Freedom of Speech}, publisher={Greenwood}, year= 1972, address={Westport, CT} } @book{Murray98, author={Murray, David M.}, title={Design and Analysis of Group-Randomized Trials}, publisher={Oxford UP}, year={1998}, address={New York} } @book{Mutz98, author={Diana C. Mutz}, title={Impersonal Influence: How Perceptions of Mass Collectives Affect Political Attitudes}, publisher={Cambridge University Press}, year={1998}, address={New York, NY} } @article{MwaLesDec05, author={Samuel M. Mwalili and Emmanuel Lesaffre and Dominique Declerck}, title={A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study}, journal={Applied Statistics}, volume={54}, year={2005}, pages={77-93} } @article{Myerson04, author = {Roger Myerson}, year = {2004}, title = {Political Economics and the Weimar Disaster}, journal = {Journal of Institutional and Theoretical Economics}, pages = {187-209}, volume = {160} } @article{Nagler91, author={Jonathan Nagler}, title={{The Effect of Registration Laws and Education on U. S. Voter Turnout}}, journal= apsr, volume={85}, year={1991}, pages={1393--1405}, number={4} } @unpublished{NasYi03, author={Tetsuya Nasukawa and Jeonghee Yi}, title={Sentiment Analysis: Capturing Favoriability Using Natural Language Processing}, note={Tetsuya Nasukawa IBM Research, Tokyo Research Laboratory 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken, 242-8502, Japan; nasukawa@jp.ibm.cop}, year={2003}, month={October} } @unpublished{NasYi03, author={Tetsuya Nasukawa and Jeonghee Yi}, title={Sentiment Analysis: Capturing Favoriability Using Natural Language Processing}, note={Tetsuya Nasukawa IBM Research, Tokyo Research Laboratory 1623-14 Shimotsuruma, Yamato-shi, Kanagawa-ken, 242-8502, Japan; nasukawa@jp.ibm.cop}, year={2003}, month={October} } @book{National02, author={NIPSSR}, title={Population Projections for Japan (January, 2002)}, publisher={National Institute of Population and Social Security Research}, year= 2002, annote={Life tables for Japan are constructed using the Lee-Carter method.} } @book{Neuendorf02, author={Neuendorf, K.A.}, title={{The Content Analysis Guidebook}}, publisher={Thousand Oaks, CA: Sage Publications}, year={2002} } @article{Neuhaus02, author={John M. Neuhaus}, title={Analysis of Clustered and Longitudinal Binary Data Subject to Response Misclassification}, journal={Biometrics}, volume={58}, year={2002}, pages={675-683}, month={September} } @article{Neuhaus99, author={John M. Neuhaus}, title={Bias and efficiency loss due to misclassified responses in binary regression}, journal={Biomtrika}, volume={86}, year={1999}, pages={843-855}, number={4} } @article{neutra78, author={Raymond R. Neutra and Margaret E. Drolette}, title={Estimating Exposure-Specific Disease Rates from Case-Control Studies Using Bayes Theorem}, journal={American Journal of Epidemiology}, volume= 108, year= 1978, pages={214-222}, number= 3 } @article{Neyman23, author={J. Neyman}, title={On the application of probability theory to agricultural experiments. Essay on Principles. Section 9}, journal={Statistical Science}, volume={5}, year={1923}, pages={465-480}, note={Translated in 1990, with discussion} } @article{Neyman23b, author={J. Neyman}, title={Statistical Problems in Agricultural Experiments}, journal={Journal of the Royal Statistical Association}, volume= 2, year= 1923, pages={107--180}, number= 2 } @article{Nickerson05, author={David W. Nickerson}, title={Scalable Protocols Offer Efficient Design for Field Experiements}, journal={Political Analysis}, volume={13}, year={2005}, pages={233-252} } @article{NieFet86, author={Richard G. Niemi and Patrick Fett}, title={The Swing Ratio: An Explanation and an Assessment}, journal={Legislative Studies Quarterly}, volume={11}, year={1986}, pages={75-90}, month={February}, number={1} } @misc{NISO01, author={NISO}, title={The Dublin Core Metadata Element Set}, year={2001}, note={{http://www.niso.org/standards/resources/Z39-85.pdf}} } @article{NiyGirPog98, author = {P. Niyogi and F. Girosi and T. Poggio}, title = {Incorporating Prior Information in Machine Learning by Creating Virtual061 Examples}, journal = {Proceedings of the IEEE.}, volume = {86}, year = {1998}, pages = {2196--2209}, number = {11} } @book{Noakes71, author={Noakes, J.}, title={The Nazi Party in Lower Saxony}, publisher={Oxford University Press}, year= 1971, address={New York} } @article{NovReaRau06, author={Scott P. Novak and Sean F. Reardon and Stephen W. Raudenbush and Stephen L. Buka}, title={Retail tobacco outlet density and youth cigarette smoking: A propensity-modeling approach}, journal={American Journal of Public Health}, volume={96}, year={2006}, pages={670-676}, month={April}, number={4} } @article{nurminen95, author={Markku Nurminen}, title={To Use or Not to Use the Odds Ratio in Epidemiologic Analysis}, journal={European Journal of Epidemiology}, volume= 11, year= 1995, number={365-371} } @article{NybPetGai03, author={Hanne Nybo et al}, title={Predictors of Mortality in 2249 Nonagenarians-The Danish 1905 Cohort Study}, journal={Journal of the American Geriatrics Society}, volume= 51, year= 2003, pages={{1365-73}} } @booklet{OetParHym03, title={The Not So Short Introduction to \LaTeXe\}, author = {Tobias Oetiker, Hubert Partl, Irene Hyma and Elisabeth Schlegl}, year = 2003, note = {{Available at http://www.ctan.org/tex-archive/info/lshort/english/lshort.pdf.}} } @article{OhaWooMoo90, author={A. O'Hagan and E.G. Woodward and L.C. Moodaley}, title={Practical Bayesian Analysis of a Simple Logistic Regression: Predicting Corneal Transplants}, journal={Statistics in Medicine}, volume={9}, year={1990}, pages={1091-1101} } @book{Ohr97a, author = {Dieter Ohr}, title = {Nationalsozialistische Propaganda und Weimarer Wahlen: empirische Analysen zur Wirkung von NSDAP-Versammlungen}, publisher = {Westdeutscher Verlag}, year = 1997, address = {Opladen} } @article{Ohr97b, author={Dieter Ohr}, title={Nationalsozialistische Versammlungspropaganda und Wahlerfolg der NSDAP: eine kausale Beziehung?}, journal={Historical Social Research}, volume= 22, year= 1997, pages={106--127}, number={3/4} } @article{OkoDev01, author={Ike S. Okosun and G.E. Alan Dever}, title={Verbal Autopsy: A Necessary Solution for the Paucity of Mortality Data in the Less-Developed Countries}, journal={Ethnicity and Disease}, volume={11}, year={2001}, pages={575-577} } @article{OkoDev01, author={Ike S. Okosun and G.E. Alan Dever}, title={Verbal Autopsy: A Necessary Solution for the Paucity of Mortality Data in the Less-Developed Countries}, journal={Ethnicity and Disease}, volume={11}, year={2001}, pages={575-577} } @article{OloFliAns94, author={John O'Loughlin and Colin Flint and Luc Anselin}, title={The Geography of the Nazi Vote: Context, Confession, and Class in the Reichstag Election of 1930}, journal={Annals of the Association of American Geographers}, volume= 84, year= 1994, pages={351-380} } @article{O'Loughlin02, author={John O'Loughlin}, title={The Electoral Geography of Weimar Germany}, journal={Political Analysis}, volume= 10, year= 2002, pages={217--243}, number= 3 } @article{Oman85, author={Samuel D. Oman}, title={Specifying a Prior Distribution in Structured Regression Problems}, journal= jasa, volume= 80, year= 1985, pages={190--195}, month={March}, number= 389 } @article{OneRus97, author={John R. Oneal and Bruce Russett}, title={The Classical Liberals Were Right: Democracy, Interdependence, and Conflict, 1950-1985}, journal= isq, volume= 41, year= 1997, pages={267--293}, month={June}, number= 2 } @inproceedings{OrcWoo72, author={T. Orchard and M.A. Woodbury}, title={A Missing Information Principle: Theory and Applications}, booktitle={Proceedings of the 6th Berkeley Symposium on Mathematical Statistics and Probability}, year={1972}, publisher={Berkeley: University of California Press}, pages={697-715} } @book{ORourke98, author={Joseph O'Rourke}, title={Computational Geometry in C}, publisher={Cambridge University Press}, year= 1998, address={New York} } @article{OSS44, author={OSS}, title={Greater Germany --- Kreis Boundaries}, journal={OSS Map 6289}, year= 1944, month={July 1} } @article{OveMag92, author={John E. Overall and Kevin N. Magee}, title={Directional Baseline Differences and Type I Error Probabilities in Randomized Clinical Trials}, journal={Journal of Biopharmaceutical Statistics}, volume={2}, year={1992}, pages={189-203}, number={2} } @article{PacPacDuk90, author={Sara Pacque-Margolis and Michel Pacque and Zwannah Dukuly and John Boateng and Hugh R. Taylor}, title={Application of the Verbal Autopsy During A Clinical Trial}, journal={Social Science Medicine}, volume={31}, year={1990}, pages={585-591}, number={5} } @article{PalPet03, author={Ted Palmer and Anthony Petrosino}, title={The "Experimenting Agency". The California Youth Authority Research Division}, journal={Evaluation Review}, volume={22}, year={2003}, pages={228-266}, month={June}, number={3} } @article{PalPoo87, author={Thomas R. Palfrey and Keith T. 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Skoufias} } @unpublished{Small05, author={Dylan S. Small}, title={Sensitivity Analysis for Limited Information Linear Simultaneous Equations Models With Overidentifying Restrictions}, note={Dept. of Statistics, The Wharton School of the University of Pennsylvania, Philadelphia, PA 19104-6340, dmall@wharton.upenn.edu}, year={2005}, month={September} } @book{SmiMor91, author={P.J. Smith and R.H. Morrow}, title={Methods for field trials of interventions against tropical diseases: a 'toolbox'}, publisher={Oxford University Press}, year={1991}, address={Oxford} } @article{Smith03, author={Tom W. Smith}, title={Developing Comparable Questions in Cross-National Surveys, in Cross-Cultural Survey Methods}, journal={John Wiley and Sons}, volume={2003}, pages={Janet A. Harkness and Fons J. R. van de Vijver and Peter Ph. 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Semantic Orientation Applied to}, publisher = {40th Annual Meeting of the Associatin for Computational Linguistics}, year = {2002}, month = {July}, organization = {Institute for Information Technology}, address = {National Research Council of Canada, Ottawa, Ontario, Canada K1A0R6} } @article{Urdal05, author = {Henrik Urdal}, title = {People vs. Malthus: Population Pressure, Environmental Degradation, and Armed Conflict Revisited}, journal = {Journal of Peace Research}, volume = {42}, year = {2005}, pages = {417-434}, month = {July}, number = {4}, publisher = {Journal for Peace Research} } @book{UttLock02, title = {American Political Scientists: a Dictionary}, publisher = {Greenwood Press}, year = {2002}, editor = {Glenn H. 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Verrall}, title={A state space formulation of Whittaker graduation, with extensions}, journal={Insurance: Mathematics and Economics}, volume= 13, year= 1993, pages={7--14} } @article{VerSch77, author={Sidney Verba and Kay Lehman Schlozman}, title={Unemployment, Class Consciousness, and Radical Politics: What Didn't Happen in the Thirties}, journal={Journal of Politics}, volume= 39, year= 1977, pages={291--323}, number= 2 } @book{VerSchBra95, author={Sidney Verba and Kay Lehman Schlozman and Henry E. Brady}, title={Voice and Equality: Civic Volunteerism in American Politics}, publisher={Harvard University Press}, year= 1995, address={Cambridge, MA} } @article{Villalonga04, author={Belen Villalonga}, title={Does Diversification Cause the "Diversification Discount"?}, journal={Financial Management}, volume={33}, year={2004}, pages={5-27}, number={2} } @article{Voth03, author = {Voth, Hans-Joachim}, title = {With a Bang, not a Whimper: Pricking Germany's Stock Market Bubble in 1927 and the Slide into Depression}, journal = {Journal of Economic History}, volume = 63, year = 2003, pages = {65--99}, number = 1 } @article{Voth95, author = {Voth, Hans-Joachim}, title = {Did High Wages or High Interest Rates Bring Down the Weimar Republic? A Cointegration Model of Investment in Germany, 1925-1930}, journal = {Journal of Economic History}, volume = 55, year = 1995, pages = {801--821}, month = {December}, number = 4 } @article{WacWei82, author={Wacholder, S. and Weinberg, C.R.}, title={{Paired versus Two-Sample Design for a Clinical Trial of Treatments with Dichotomous Outcome: Power Considerations}}, journal={Biometrics}, volume={38}, year={1982}, pages={801--812}, number={3} } @article{Wagstaff00, author={Adam Wagstaff}, title={Socioeconomic inequalities in Child Mortality: Comparisons Across Nine Developing Countries}, journal= bull, year={2000}, optnumber={1}, optvolume={78}, optpages={19--29} } @article{Wahba75, author={G. Wahba}, title={Smoothing noisy data by spline functions}, journal={Numer. Math}, volume= 24, year= 1975, pages={383--393} } @article{Wahba77, author={G. Wahba}, title={Practical approximate solutions to linear operator equations when the data are noisy}, journal={SIAM J. Numer. 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Wahba}, title={Spline bases, regularization, and generalized cross-validation for solving approximation problems with large quantities of noisy data}, booktitle={Approximation theory III}, publisher={Academic Press}, year= 1980, address={New York}, editor={W. Cheney}, pages={905--912} } @article{Wahba85, author={G. Wahba}, title={A comparison of {GCV} and {GML} for choosing the smoothing parameter in the generalized splines smoothing problem}, journal={The Annals of Statistics}, volume= 13, year= 1985, pages={1378--1402} } @book{Wahba90, author={G. Wahba}, title={Splines Models for Observational Data}, publisher={{Series in Applied Mathematics, Vol. 59, SIAM}}, year= 1990 , address={Philadelphia} } @techreport{WahLinZha99, author={G. Wahba and Y. Lin and H. Zhang}, title={Generalized Approximate Cross Validation for SVM, or, anather way to look at margin-like quantities}, institution={Department of Statistics, University of Wisconsin}, year={1999}, type={Tech. Report}, number={1006} } @misc{Wakefield01, author={Jon Wakefield}, title={Ecological Inference for $2 \times 2$ Tables}, year= 2001, howpublished={Working Paper \# 12, Center for Statistics and the Social Sciences, University of Washington} } @article{WalCarXiaGel97, author={Lance A. Waller and Bradley P. Carlin and Hong Xia and Alan E. Gelfand}, title={Hierarchical Spatio-Temporal Mapping of Disease Rates}, journal= jasa, volume= 92, year= 1996, pages={607-617} } @article{WalDon94, author={G.E.L. Walraven and P.W.J. van Dongen}, title={Assessment of maternal mortality in Tanzania}, journal={British Journal of Obstetrics and Gynaecology}, volume= 101, year= 1994, pages={414--417} } @book{Waldron99, author={Jeremy Waldron}, title={Law and disagreement}, publisher={Oxford University Press}, year={1999}, address={New York} } @article{Waletal97, author={L.A. Waller and B.P. Carlin and H. Xia and A.E. Gelfand}, title={{Hierarchical Spatio-Temporal Mapping of Disease Rates}}, journal= jasa, volume= 92, year= 1997, pages={607--617}, number= 438 } @techreport{WalHogHam06, author={Robert Walker and Lesley Hoggart and Gayle Hamilton with Susan Blank}, title={Making random assignment happen: Evidence from the UK Employment Retention and Advancement (ERA) demonstration}, institution={Department for Work and Pensions, Corporate Document Services}, year={2006}, month={March}, type={research report}, note={ISBN 1 84 123981 X, Research Report 330} } @article{WanRob98, author={Naisyin Wang and James Robins}, title={Large-sample theory for parametric multiple imputation procedures}, journal={Biometrika}, volume={85}, year={1998}, pages={935-948} } @article{WanSchAvo05, author={Philip S. Wang and Sebastian Schneeweiss and Jerry Avorn and Michael A. Fischer and Helen Mogun and Daniel H. Solomon and M. 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The use of the MIRAB model of island economies based on migration, remittances, aid and bureaucracy is extended to examine its impact on social cohesion and the avoidance of internal conflict. For Polynesia, MIRAB is found to be a sustainable development strategy. Continuous emigration from Polynesia serves to reduce population pressure and communal tensions. Further, remittance income supports the Polynesian economies, and this also reduces the potential for conflict. For Micronesia, except Kiribati and Nauru, migration access to the USA is assured. In contrast, for the Melanesian countries, there is minimal emigration, rapid population growth and considerable intercommunal tension, which has resulted in several coups and one 'failed state'. Demographic pressure created by rapid population growth results in a lack of employment opportunities for youths (who provide the majority of participators in civil unrest and conflicts) rather than in direct pressure on land and other natural resources.}, journal={Journal for Peace Research}, volume={42}, year={2005}, pages={435-454}, month={July}, number={4} } @unpublished{WarSivCao05, author={Michael D. Ward and Randolph M. Siverson and Xun Cao}, title={Everybody Out of the Pool!}, note={Michael Ward, Dept of Politcal Science, Univ of WA, Seattle mdw@u.washington.edu}, year={2005}, month={August} } @techreport{WasRoe06, author={Larry Wasserman and Kathryn Roeder}, title={Weighted Hypothesis Testing}, institution={Carnegie Mellon University}, year={2006}, month={April} } @article{Weibe04, author={Janyce M. 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Weiss}, title={Clinical Epidemiology: The Study of the Outcome of Illness}, publisher={Oxford University Press}, year={1986}, volume={Volume 11}, address={New York}, series={Monographs in Epidemiology and Biostatistics } } @article{WeiTan90, author={Greg C. Wei and Martin A. Tanner}, title={A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms}, journal={Journal of the American Statistical Association}, volume={85}, year={1990}, pages={699-704}, month={September} } @article{WeiWanIbr97, author={Robert E. Weiss and Yan Wang and Joseph G. Ibrahim}, title={Predictive Model Selection for Repeated Measures Random Effects Models Using Bayes Factors}, journal={Biometrics}, volume= 53, year= 1997, pages={592--602}, month={June} } @article{Wellhofer03, title = {{Democracy and Fascism: Class, Civil Society, and Rational Choice in Italy}}, author = {E. Spencer Wellhofer}, journal = {American Political Science Review}, volume = {97}, number = {01}, pages = {91--106}, year = {2003} } @article{Werner00, author={Suzanne Werner}, title={The Effects of Political Similarity on the Onset of Militarized Disputes, 1816-1985}, journal= prq, volume= 53, year= 2000, pages={343--374}, month={June} } @article{Wernette1977, author={Wernette, Dee Richard}, title={Quantitative Methods in Studying Political Mobilization in Late Weimar Germany}, journal={Historical Methods Newsletter}, volume= 10, year= 1977, pages={97-101} } @book{WesHar97, author={Mike West and Jeff Harrison}, title={Bayesian Forecasting and Dynamic Linear Models}, publisher={Springer}, year= 1997, address={New York} } @article{WesHarMig85, author={Mike West and P. Jeff Harrison and Helio S. Migon}, title={Dynamic Generalized Linear Models and Bayesian Forecasting}, journal={Journal of the American Statistical Association}, volume={80}, year={1985}, pages={73-83}, month={March}, number={389} } @article{Western95, author={Bruce Western}, title={{Concepts and Suggestions for Robust Regression Analysis}}, journal={American Journal of Political Science}, volume={39}, year={1995}, pages={786--817}, number={3} } @article{Western98, author={Bruce Western}, title={{Causal Heterogeneity in Comparative Research: a Bayesian Hierarchical Modelling Approach}}, journal={American Journal of Political Science}, volume= 42, year= 1998, pages={1233--1259}, month={October}, number= 4 } @article{WhiEva96, author={Stephen Whitefield and Geoffrey Evans}, title={Support for Democracy and Poltical Opposition in Russia 1993-95}, journal={Post Soviet Affairs}, volume={12}, year={1996}, pages={218-52}, number={3} } @book{WhiRosMcA97, author={Stephen White and Richard Rose and Ian McAllister}, title={How Russia Votes}, publisher={Chatham House Publishers, Inc.}, year={1997}, address={Chatham, NJ} } @unpublished{WhiSetCha06, author={David R. Whiting and Philip W. Setel and Daniel Chandramohan and Lara J. Wolfson and Yusuf Hemed and Alan D. Lopez}, title={Estimating Cause-Specific Mortality from Community- and Facility-Based Data Sources in Tanzania: Options and Implications for Mortality Burden Estimates}, note={Whiting, MEASURE Evaluation, Carolina Population Center, Univ. of NC at Chapel Hill, Dept of Medicine, School of Clinical Medical Sciences, Univ of Newcastle upon Tyne England; david.whiting@ncl.ac.uk}, year={2006} } @article{Whitbeck05, author={Caroline Whitbeck}, title={The Responsible Collection, Retention, Sharing, and Interpretation of Data}, journal={Online Ethics Center for Engineering and Science}, year= 2005, note={{http://onlineethics.org/reseth/mod/data.html}} } @article{White02, author={Kevin M. White}, title={Longevity Advances in High-IncomeCountries, 1955-96}, journal={Population and Development Review}, volume= 28, year= 2002, pages={59--76}, month={March}, number= 1 } @article{White80, author={Halbert White}, title={A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity}, journal={Econometrica}, volume={48}, year={1980}, pages={817--838}, month={May}, number={4} } @book{White82, author={Halbert L. White}, title={Asymptotic Theory For Econometricians}, publisher={Academic Press}, year= 1984, address={New York} } @book{White92, author={Halbert H. White}, title={Artificial Neural Networks, Approximation and Learning Theory}, publisher={Blackwell}, year= 1992, address={Cambridge, MA} } @article{Whittaker23, author={{Whittaker E.T.}}, title={On a New Method of Graduation}, journal={Proceedings of the Edinburgh Mathematical Society}, volume= 41, year= 1923, pages={63--75} } @article{WidKub96, author={Widmer, G. and Kubat, M.}, title={{Learning in the presence of concept drift and hidden contexts}}, journal={Machine Learning}, volume={23}, year={1996}, pages={69--101}, number={1}, publisher={Springer} } @unpublished{WieWilBel01, author={Janyce Wiebe and Theresa Wilson and Matthew Bell}, title={Identifying Collocations for Recognizing Opinions}, note={University of Pittsburgh wiebe, twilson, mbell@cs.pitt.edu}, year={2001}, month={April} } @article{WilBerNob01, author={B.P. Will and J.M. Berthelot and K.M. Nobrega and W. Flanagan and W.K. Evans}, title={Canada's Population Health Model (POHEM): A Tool for Performing Economic Evaluations of Cancer Control Interventions}, journal={European Journal of Cancer}, volume= 37, year= 2001, pages={1797--1804} } @article{WilGouBos02, author={Brian G. Williams and Eleanor Gouws and Cynthia Boschi-Pinto and Jennifer Bryce and Christopher Dye}, title={Estimates of world-wide distribution of child deaths from acute respiratory infections}, journal={The Lancet Infectious Diseases}, volume={2}, year={2002}, pages={25-32}, month={January} } @article{WilGouBos02, author={Brian G. Williams and Eleanor Gouws and Cynthia Boschi-Pinto and Jennifer Bryce and Christopher Dye}, title={Estimates of world-wide distribution of child deaths from acute respiratory infections}, journal={The Lancet Infectious Diseases}, volume={2}, year={2002}, pages={25-32}, month={January} } @article{WilHol07, author={Elizabeth Ty Wilde and Robinson Hollister}, title={How Close is Close Enough? Evaluating Propensity Score Matching Using Data from a Class-Size Reduction Experiment}, journal={Journal of Policy Analysis and Management}, volume={26}, year={2007}, number={3} } @techreport{Wilmoth93, author={John Wilmoth}, title={{Computational Methods for Fitting and Extrapolating the Lee-Carter Model of Mortality Change}}, institution={Department of Demography, University of California, Berkeley}, year= 1993 } @incollection{Wilmoth96, author={John R. Wilmoth}, title={Mortality Projections for Japan: A Comparison of Four Methods}, booktitle={Health and Mortality Among Elderly Populations}, publisher={Oxford University Press}, year= 1996, address={Oxford}, editor={G. Caselli and Alan Lopez}, pages={266-287} } @article{Wilmoth98, author={John Wilmoth}, title={The Future of Human Longevity: A Demographer's Perspective}, journal={Science}, volume= 280, year= 1998, pages={395--397}, month={April 17}, number= 5362 } @article{Wilmoth98b, author={John Wilmoth}, title={Is the Pace of Japanese Mortality Decline Converging Towards International Trends?}, journal={Population and Development Review}, volume= 24, year= 1998, pages={593--600}, number= 3 } @article{WinMar92, author={Christopher Winship and Robert D. Mare}, title={Models for Sample Selection Bias}, journal={Annual Review of Sociology}, volume={18}, year={1992}, pages={327-50} } @article{WinMor99, author={Christopher Winship and Stephen L. Morgan}, title={The Estimation of causal Effects from Observational Data}, journal={American Review of Sociology}, volume= 25, year= 1999, pages={659--707} } @article{WinRad94, author={Christopher Winship and Larry Radbill}, title={Sampling Weights and Regression Analysis}, journal={Sociological Methods and Research}, volume={23}, year={1994}, pages={230-257}, month={November}, number={2} } @unpublished{WinSob00, author={Christopher Winship and Michael Sobel}, title={Causal Inference in Sociological Studies}, note={Harvard University}, year= 2000 } @article{WirLin94, author={D.N. Wirawan and M. Linnan}, title={The Bali indirect maternal mortality study}, journal={Studies in Family Planning}, volume= 5, year= 1994, pages={304--309} } @inbook{WolCalJoh94, author = {F.D. Wolinsky, C.M. Callahan, and R.J. Johnson}, title = {Subjective Health Status and Mortality in the Elderly}, year = 1994, publisher = {{New York: Springer Publishing Company}}, pages = {{13-28}}, journal = {Facts and Research in Gerontology} } @article{WolFir02, author = {Rory Wolfe and David Firth}, title = {Modelling Subjective Use of an Ordinal Reponse Scale in a Many Period Crossover Experiment}, journal = {Applied Statistics}, volume = 51, year = 2002, pages = {245--255}, month = {April}, number = 2 } @article{WolJoh92, author = {Fredric Wolinsky and Robert Johnson}, title = {Perceived Health Status and Mortality Among Older Men and Women}, journal = {Journal of Gerontology: Social Sciences}, volume = 47, year = 1992, pages = {{S304-12}} } @article{WolJohStu95, author = {Fredric D. Wolinsky, Robert L. Johnson, and Timothy E. Stump}, title = {The Riske of Mortality among Older Adults over an Eight-Year Period}, journal = {The Gerontologist}, volume = 35, year = 1995, pages = {{150-61}} } @article{WonBenKof98, author = {John B. Wong and William G. Bennett and Raymond S. Koff and Stephen G. Pauker}, title = {Pretreatment Evaluation of Chronic Hepatitis C: Risks Benefits, and Costs}, journal = {Journal of the American Medical Association}, volume = 280, year = 1998, pages = {2088--2093} } @article{WonMcqMch00, author = {John B. Wong and Gerladine M. McQuillan and John G. McHutchison and Thierry Poynard}, title = {Estimating Future Hepatitis C Morbidity, Mortality, and Costs in the United States}, journal = {American Journal of Public Health}, volume = 90, year = 2000, pages = {1562--1569}, number = 10 } @book{WraPet96, author = {Richard Wrangham and Dale Peterson}, title = {Demonic Males}, publisher = {Houghton Mifflin}, year = 1996 } @book{WuHam00, author = {Chien-Fu Wu and Michael Hamada}, title = {Experiments: Planning, analyzing and Parameter Design Optimization}, publisher = {Wiley-Interscience}, year = {2000}, address = {New York} } @book{WuHam00, author = {Chien-Fu Wu and Michael Hamada}, title = {Experiments: Planning, Analyzing, and Parameter Design Optimization}, publisher = {Wiley-Interscience}, year = {2000}, address = {New York} } @article{WuSch93, author = {Z. Wu and R. Schaback}, title = {Local Error Estimates for Radial Basis Function Interpolation of Scattered Data}, journal = {Journal of Numerical Analysis}, volume = 13, year = 1993, pages = {13--27} } @article{YanRaoMa05, author = {Gonghuan Yang and Chalapati Rao and Jiemin Ma and Lijun Wang and Xia Wan and Guillermo Dubrovsy and Alan D. Lopez}, title = {Validation of verbal autopsy procedures for adult deaths in China}, journal = {International Journal of Epidemiology}, year = {2005}, month = {September}, note = {Advance Access published 9/6/05 doi:10.1093/ije/dyi181} } @article{YeeHas03, author = {Yee, T. W. and Hastie, T. J.}, title = {Reduced-rank vector generalized linear models}, journal = {Statistical Modelling}, volume = 3, year = {2003}, pages = {15--41}, issue = 1 } @article{YeeWil96, author = {T.W. Yee and C.J. Wild}, title = {Vector Generalized Additive Models}, journal = {Journal of the Royal Statistical Society. Series B (Methodological)}, volume = {58}, year = {1996}, pages = {481--493}, number = {3} } @article{Yoo02, author = {Thomas W. Yoo}, title = {Presumed Disloyal: Executive Power Judicial Deference, and the Construction of Race Before and After September 11}, journal = {Columbia Human Rights Law Review}, volume = 34, year = {2002}, pages = {1--??} } @Article{YacYac06, author = {Jason Webb Yackee and Susan Webb Yackee}, title = {A Bias Towards Business? Assessing Interest Group Influence on the U.S. Bureaucracy}, journal = {Journal of Politics}, year = {2006}, OPTkey = {}, volume = {68}, number = {1}, pages = {128-169}, OPTmonth = {}, OPTnote = {}, OPTannote = {} } @article{YosMagBos03, author = {Hirokazu Yoshikawa and Katherine A. Magnuson and Johannes M. Bos and Jo Ann Hsueh}, title = {Effects of Earnings-Supplement Policies on Adult Economic and Middle-Childhood Outcomes Differ for the `Hardest to Employ'}, journal = {Child Development}, volume = {74}, year = {2003}, pages = {1500-1521}, month = {September/October}, number = {5} } @proceedings{YuHat03, editor={Hong Yu and Vasileios Hatzivassiloglou}, title={Towards Answering Opinion Questions: Separating Facts from Opinions and Identifying the Polarity of Opinion Sentences}, year={03}, organization={2003 Conference on Empirical Methods in Natural Language Processing}, address={Hong Yu Dept. of Computer Science columbia Univ. New York, NY 10027 hongyu@cs.columbia.edu} } @article{YuKeaSly98, author={Elena S. H. Yu, Yin M. Kean, Donal J. Slyman, et al}, title={Self Perceived Health and 5-Year Mortality Risks among the Elderly in Shanghai, China}, journal={American Journal of Epidemiology}, volume= 147, year= 1998, pages={{880-90}} } @article{yule12, author={G.U. Yule}, title={On the Methods of Measuring the Association Between Two Attributes}, journal={Journal of the Royal Statistical Society}, volume= 75, year= 1912, pages={579--642} } @book{Zaba86, author={Basia Zaba}, title={Measurement of Emigration Using Indirect Techniques. Manual for the collection and analysis of Data on Residence of Relatives}, publisher={Ordina Editions, 10, place St. Jacques 4000 Liege Belgium}, year={1986}, note={Working Group on the Methodology for the Study of International Migration} } @article{ZabDav96, author={Basia Zaba and Patricia H. David}, title={Fertility and the Distribution of Child Mortality Risk Among Women: An Illustrative Analysis}, journal={Population Studies}, volume= 50, year= 1996, pages={263--278} } @book{Zaller92, author={John R. Zaller}, title={The Nature and Origins of Mass Opinion}, publisher={Cambridge University Press}, year= 1992, address={New York, NY} } @article{ZamRouOrh01, author={Asad Zaman and Peter J. Rousseeuw and Mehmet Orhan}, title={{Econometric applications of high-breakdown robust regression techniques}}, journal={Economics Letters}, volume={71}, year={2001}, pages={1--8} } @article{Zeileis04, author={Achim Zeileis}, title={Econometric Computing with HC and HAC Covariance Matrix Estimators}, journal={Journal of Statistical Software}, volume={11}, year={2004}, pages={1--17}, number={10}, publisher={Wiley} } @article{Zellner62, author={A. 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Software}, \url{http://gking.harvard.edu/matchit/}. \end{verse} In addition, the {\tt convex.hull} discard option is implemented via the {\tt WhatIf} package \citep{KinZen06,KinZen07,StoKinZen05}. Generalized linear distance measures are implemented via the {\tt stats} package \citep{VenRip02}. Generalized additive distance measures are implemented via the {\tt mcgv} package \citep{HasTib90}. The neural network distance measure is implemented via the {\tt nnet} package \citep{Ripley96}. The classification trees distance measure is implemented via the {\tt rpart} package \citep{BreFriOls84}. Full and optimal matching are implemented via the {\tt optmatch} package \citep{Hansen04}. Genetic matching is implemented via the {\tt Matching} package \citep{DiaSek05}. Coarsened exact matching is implemented via the \texttt{cem} package \citep{IacKinPor08,IacKinPor08b}. \section{What if My datasets Are Big and Are Taking Up Too Much Memory?} {\tt matchit()} does not save the data set in its output object, but it does save a matrix of the covariates. {\tt match.data()} will create a matched data set. One can eliminate the original data set to save memory in R by {\tt rm(name)}, where {\tt name} is the name of the data set, after calling {\tt match.data()}. %\section{Can I use a Difference-in-Difference Estimator for Matched % Data?} % %A difference-in-differences (DID) analysis can be easily conducted %with \MatchIt. If we were interested in the DID matching estimate in %the Lalonde data, we could simply include {\texttt re75} as a %covariate in the preprocessing step. Then the analysis can be %performed on the change in income from 1975 to 1978: {\tt re78}-{\tt % re75}. Time-varying covariates (of which none exist in the Lalonde %data) should of course also be differenced for the DID estimator. %** we should show how to do this with zelig \section{How Exactly are the Weights Created?} \label{subsec:weights} Each type of matching method can be thought of as creating groups of units with at least one treated unit and at least one control unit in each. In exact matching, subclassification, or full matching, these groups are the subclasses formed, and the number of treated and control units will vary quite a bit across subclasses. In nearest neighbor or optimal matching, the groups are the pairs (or sets) of treated and control units matched. In 1:1 nearest neighbor matching there will be one treated unit and one control unit in each group. In 2:1 nearest neighbor matching there will be one treated unit and two control units in each group. Unmatched units receive a weight of 0. All matched treated units receive a weight of 1. These weights are constructed to estimate the average treatment effect on the treated, with the control group essentially weighted to look like the treated group. The weights for matched control units are formed as follows: \begin{enumerate} \item Within each group, each control unit is given a preliminary weight of $n_{ti}/n_{ci}$, where $n_{ti}$ and $n_{ci}$ are the number of treated and control units in group $i$, respectively. \item If matching is done with replacement, each control unit's weight is added up across the groups in which it was matched. \item The control group weights are scaled to sum to the number of uniquely matched control units. \end{enumerate} With subclassification, when the analysis is done separately within each subclass and then aggregated up across the subclasses, these weights will generally not be used, but they may be used for full matching or nearest neighbor matching if the number of control units matched to each treated unit varies. \section{How Do I Create Observation Names?} \label{rnames} Since the diagnostics often make use of the observation names of the data frame, you may find it helpful to specify observation names for the data input. Use the \texttt{row.names} command to achieve this. For example, to assign the names ``Dan'', ``Kosuke'', ``Liz'' and ``Gary'' to a data frame with the first four observations in the Lalonde data, type: \begin{verbatim} > test <- lalonde[1:4, ] > row.names(test) <- c("Dan", "Kosuke", "Liz", "Gary") > print(test) age educ black hisp married nodegr re74 re75 re78 u74 u75 treat Dan 37 11 1 0 1 1 0 0 9930 1 1 1 Kosuke 22 9 0 1 0 1 0 0 3596 1 1 1 Liz 30 12 1 0 0 0 0 0 24910 1 1 1 Gary 27 11 1 0 0 1 0 0 7506 1 1 1 \end{verbatim} \section{How Can I See Outcomes of Matched Pairs?} To obtain outcomes of matched pairs, recall that the original dataset has unique row names corresponding to each of the observations. The row names of \texttt{match.matrix} correspond to the names of the treated, and each of the cells corresponds to a name of matched controls. So to obtain matched outcomes, you can use: \begin{verbatim} cbind(lalonde[row.names(foo$match.matrix),"re78"], lalonde[foo$match.matrix,"re78"]) \end{verbatim} \section{How Do I Ensure Replicability As \MatchIt\ Versions Develop?} \label{subsec:vercontrol} As the literature on matching techniques is rapidly evolving, \MatchIt\ will strive to incorporate new developments. \MatchIt\ is thereby an evolving program. Users may be concerned that analysis written in a particular version may not be compatible with newer versions of the program. The primary way to ensure that replication archives remain valid is to record the version of \MatchIt\ that was used in the analysis. Our website maintains binaries of all public release versions, so that researchers can replicate results exactly with the appropriate version (for Unix-based platforms, see \hlink{http://gking.harvard.edu/src/contrib/}{http://gking.harvard.edu/src/contrib/}; for windows, see \hlink{http://gking.harvard.edu/bin/windows/contrib/}{http://gking.harvard.edu/bin/windows/contrib/}). In addition, users may find it helpful to install packages with version control, using the {\tt installWithVers} command with {\tt install.packages}. So for example, in the windows R console, users may download the appropriate version from our website and install the package with version control by: \begin{verbatim} install.packages(choose.files('',filters=Filters[c('zip','All'),]), .libPaths()[1],installWithVers=T,CRAN=NULL) \end{verbatim} {\tt R CMD INSTALL} similarly permits users to specify this version using the \\ {\tt --with-package-versions} option. After having specified version control, different versions of the program may be called as necessary. Similar advice may also be appropriate for version control for R more generally. \section{How Do I Use My Own Distance Measure with \MatchIt\,?} A vector of your own distance measure can be used by specifying it as the input for {\tt distance} option in {\tt matchit()}. \section{What Do I Do about Missing Data?} \MatchIt\ requires complete data sets, with no missing values (other than potential outcomes of course). If there are missing values in the data set, imputation techniques should be used first to fill in (``impute'') the missing values (both covariates and outcomes), or the analysis should be done using only complete cases (which we do not in general recommend). For imputation software, see Amelia at (\hlink{http://gking.harvard.edu/stats.shtml}{http://gking.harvard.edu/stats.shtml}) or other programs at \hlink{http://www.multiple-imputation.com}{http://www.multiple-imputation.com}. For more information on missing data and imputation methods, see \cite{KinHonJos01}. \section{Why Preprocessing?} The purpose of matching is to approximate an experimental template, where the matching procedure approximates blocking prior to random treatment assignment in order to balance covariates between treatment and control groups. Separation of the estimation procedure into two steps simulates the research design of an experiment, where no information on outcomes is known at the point of experimental design and randomization. The separation of the balancing process in \MatchIt\ from the analysis process afterward helps keep clear the goal of balancing control and treatment groups and makes it less likely that the user will inadvertently cook the books in his or her favor. %%% Local Variables: %%% mode: latex %%% TeX-master: t %%% End: MatchIt/vignettes/face_off.jpg0000644000176200001440000002137612162551623016061 0ustar liggesusersÿØÿàJFIFddÿìDucky2ÿîAdobedÀÿÛ„         #"""#''''''''''     !! !!''''''''''ÿÀ^&"ÿÄ…!1AQaq‘"2¡±ÁBRb‚’#3ðÑrCs4¢²Sc“$&£³D5ÿÚ ?áȈ€ˆˆˆ€½ZÝ#^#Ö¼–Õ¨d…°¸Ò¿ ä‚K Ú =!NùBF€ypPø˜åŽY¢B‚&Ô€=h)96ïeháZ­E-ÜQ6,“èup#¢‰@DD3ãn´ö«rý·V.Ž¤ï˜\0žu¬Nÿt*å¬+÷wbñÖ¯Œ»´lÏØÉOàdúÊ %9¢" """ "" ×Ë8‹²¼ è¹÷=Á‹8gÄê€8Uùa-%¹m”;½*Ëåå2MÇ·áiý÷´AFƳºñ‚®v؉qp-¥Xçi×ÒºCܼ>1¶•ô._ŠsÏvÂ$1ÒFÚë£ðF•çò¶»¸˜š+êATî·‘ ž5 ÒŠ‘r ¡r¼÷ /ØçP¹¤…J¾¥tA ˆˆˆ€ˆˆˆ€ˆˆˆ€½iR>×ÐWš÷‡Ÿˆ¢ ÜO3˜@’€·]\:)Ëf¸ÊíÚSîªå€ü³Ù#ªèˆ«MhZããÑYì¦Ne7ïÓŸŠ wpö“z¾¥¥û•¡™YZ<B" iE`ËwÆK aŠ|MlVÀ;ÌIhØ>…_[Ö")Pö ×®”EôÒº/ˆˆ€ˆˆˆ€ˆˆ:Ë!G^H~€®XH#–òâýÑ€íXÓéTÞÄŠh±—†‘æº]Æ"FE wZžh9Dw&n÷Ѭd’µhíèR7Ì‚Ü·PÓ_ .wo°w³C˜Z_,¥ƒMEºd£Ë¶Œ¼m!¯±w(ÆÈÇ4ŠÑs¼˜¤„x®“‘¡Îhä¹ÎWø®Aˆx¢" """ """ /hx¯ë¡,¶15ñ48T¥%cg,Mt ^<Ö0+ÒŠÃq<Ð@÷v%ðÞ6åÒ%`4Ô=º‚Ú®w–?¼#WA™¡¶´p ÕEÏrÀ‰z Š9÷LkDB¯{¨]UæÆÇ"~qmvÕà|s ¸f·¹–†åk¨ö;B ñ]Ÿ+Úp÷ˆd±¶‹ÿmvÝ5§Ã'VŸ¡qûË;œ}Ô¶W‘˜® qd±žD ðDD¹jK<™+ÎAô-5µ‰äé> ƒÁ£Ü+±nZâO. 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MatchIt/vignettes/balance.tex0000644000176200001440000001572112162551623015733 0ustar liggesusers\section{Checking Balance} \label{sec:balance} \subsection{Quick Overview} To check balance, use \texttt{summary(m.out)} for numerical summaries and \texttt{plot(m.out)} for graphical summaries. \subsection{Details} \subsubsection{The {\tt summary()} Command} The \texttt{summary()} command gives measures of the balance between the treated and control groups in the full (original) data set, and then in the matched data set. If the matching worked well, the measures of balance should be smaller in the matched data set (smaller values of the measures indicate better balance). The \texttt{summary()} output for subclassification is the same as that for other types of matching, except that the balance statistics are shown separately for each subclass, and the overall balance in the matched samples is calculated by aggregating across the subclasses, where each subclass is weighted by the number of units in the subclass. For exact matching, the covariate values within each subclass are guaranteed to be the same, and so the measures of balance are not output for exact matching; only the sample sizes in each subclass are shown. \begin{itemize} \item {\bf Balance statistics:} The statistics the \texttt{summary()} command provides include means, the original control group standard deviation (where applicable), mean differences, standardized mean differences, and (median, mean and maximum) Quantile-Quantile (Q-Q) plot differences. In addition, the \texttt{summary()} command will report (a) the matched call, (b) how many units were matched, unmatched, or discarded due to the \texttt{discard} option (described below), and (c) the percent improvement in balance for each of the balance measures, defined as $100((|a|-|b|)/|a|)$, where $a$ is the balance before and $b$ is the balance after matching. For each set of units (original and matched data sets, with weights used as appropriate in the matched data sets), the following statistics are provided: \begin{enumerate} \item ``Means Treated'' and ``Means Control'' show the weighted means in the treated and control groups \item ``SD Control" is the standard deviation calculated in the control group (where applicable) \item ``Mean Diff'' is the difference in means between the groups \item The final three columns of the summary output give summary statistics of a Q-Q plot (see below for more information on these plots). Those columns give the median, mean, and maximum distance between the two empirical quantile functions (treated and control groups). Values greater than 0 indicate deviations between the groups in some part of the empirical distributions. The plots of the two empirical quantile functions themselves, described below, can provide further insight into which part of the covariate distribution has differences between the two groups. \end{enumerate} \item {\bf Additional options:} Three options to the \texttt{summary()} command can also help with assessing balance and respecifying the propensity score model, as necessary. First, the {\tt interactions = TRUE} option with {\tt summary()} shows the balance of all squares and interactions of the covariates used in the matching procedure. Large differences in higher order interactions usually are a good indication that the propensity score model (the distance measure) needs to be respecified. Similarly, the {\tt addlvariables} option with {\tt summary()} will provide balance measures on additional variables not included in the original matching procedure. If a variable (or interaction of variables) not included in the original propensity score model has large imbalances in the matched groups, including that variable in the next model specification may improve the resulting balance on that variable. Because the outcome variable is not used in the matching procedure, a variety of matching methods can be tried, and the one that leads to the best resulting balance chosen. Finally, the {\tt standardize = TRUE} option will print out standardized versions of the balance measures, where the mean difference is standardized (divided) by the standard deviation in the original treated group. \end{itemize} \subsubsection{The \texttt{plot()} Command} We can also examine the balance graphically using the \texttt{plot()} command, which provides three types of plots: jitter plots of the distance measure, Q-Q plots of each covariate, and histograms of the distance measure. For subclassification, separate Q-Q plots can be printed for each subclass. The jitter plot for subclassification is the same as that for other types of matching, with the addition of vertical lines indicating the subclass cut-points. With the histogram option, 4 histograms are provided: the original treated and control groups and the matched treated and control groups. For the Q-Q plots and the histograms, the weights that result after matching are used to create the plots. Three examples of the output from the {\tt plot()} command are shown in Figure~\ref{fig:plotcommandoutput}. If the empirical distributions are the same in the treated and control groups, the points in the Q-Q plots would all lie on the 45 degree line (lower left panel of Figure~\ref{fig:plotcommandoutput}). Deviations from the 45 degree line indicate differences in the empirical distribution. The jitter plot (top panel) shows the overall distribution of propensity scores in the treated and control groups. In the jitter plot, which can be created by setting \texttt{type = "jitter"}, the size of each point is proportional to the weight given to that unit. Observation names can be interactively identified by clicking the first mouse button near the units. The histograms (lower right panel) can be plotted by setting \texttt{type = "hist"}. \begin{figure} \begin{center} \includegraphics[height=2.7in, keepaspectratio=true]{figs/jitterplotnn.pdf} \includegraphics[height=3in,keepaspectratio=true]{figs/qqplotnn1.pdf} \includegraphics[height=3in,keepaspectratio=true]{figs/hist.pdf} \caption{Examples of the three types of output from the \texttt{plot} command resulting from matching on the /texttt{lalonde} data set based on real earnings in 1974 (\texttt{re74}) divided by 1000, real earnings in 1975 (\texttt{re75}) divided by 1000, years of education (\texttt{educ}), Hispanic (\texttt{hispan}) and marital status (\texttt{married}). Observations in both the treated and the control groups outside the support of the distance measure were discarded. The upper plot shows the jitter plot of the distance measure. The lower left plot shows the QQ plots for the first three covariates (\texttt{I(re74/1000)}, \texttt{I(re75/1000)}, \texttt{educ}). The lower right plot shows the histograms of the density of propensity scores for observations before and after matching.} \label{fig:plotcommandoutput} \end{center} \end{figure} %%% Local Variables: %%% mode: latex %%% TeX-master: "matchit" %%% End: MatchIt/vignettes/asa.bst0000644000176200001440000006663212162551623015111 0ustar liggesusers%% %% This is file `asa.bst', %% generated with the docstrip utility. %% %% The original source files were: %% %% merlin.mbs (with options: `,ay,nat,nm-rev,ed-rev,nmdash,dt-beg,yr-par,note-yr,tit-qq,atit-u,thtit-a,vnum-x,volp-com,pp-last,add-pub,pre-pub,edpar,edby,edbyx,blk-com,pp,ed,abr,ednx,ord,em-it,nfss') %% ---------------------------------------- %% *** BibTeX Style for ASA Journals *** %% (Brett Presnell, 24 August 1998) %% %------------------------------------------------------------------- % The original source file contains the following version information: % \ProvidesFile{merlin.mbs}[1998/02/25 3.85a (PWD)] % % NOTICE: % This file may be used for non-profit purposes. % It may not be distributed in exchange for money, % other than distribution costs. % % The author provides it `as is' and does not guarantee it in any way. % % Copyright (C) 1994-98 Patrick W. Daly %------------------------------------------------------------------- % For use with BibTeX version 0.99a or later %------------------------------------------------------------------- % This bibliography style file is intended for texts in ENGLISH % This is an author-year citation style bibliography. As such, it is % non-standard LaTeX, and requires a special package file to function properly. % Such a package is natbib.sty by Patrick W. Daly % The form of the \bibitem entries is % \bibitem[Jones et al.(1990)]{key}... % \bibitem[Jones et al.(1990)Jones, Baker, and Smith]{key}... % The essential feature is that the label (the part in brackets) consists % of the author names, as they should appear in the citation, with the year % in parentheses following. There must be no space before the opening % parenthesis! % With natbib v5.3, a full list of authors may also follow the year. % In natbib.sty, it is possible to define the type of enclosures that is % really wanted (brackets or parentheses), but in either case, there must % be parentheses in the label. % The \cite command functions as follows: % \citet{key} ==>> Jones et al. (1990) % \citet*{key} ==>> Jones, Baker, and Smith (1990) % \citep{key} ==>> (Jones et al., 1990) % \citep*{key} ==>> (Jones, Baker, and Smith, 1990) % \citep[chap. 2]{key} ==>> (Jones et al., 1990, chap. 2) % \citep[e.g.][]{key} ==>> (e.g. Jones et al., 1990) % \citep[e.g.][p. 32]{key} ==>> (e.g. Jones et al., p. 32) % \citeauthor{key} ==>> Jones et al. % \citeauthor*{key} ==>> Jones, Baker, and Smith % \citeyear{key} ==>> 1990 %--------------------------------------------------------------------- ENTRY { address author booktitle chapter edition editor howpublished institution journal key month note number organization pages publisher school series title type volume year } {} { label extra.label sort.label short.list } INTEGERS { output.state before.all mid.sentence after.sentence after.block } FUNCTION {init.state.consts} { #0 'before.all := #1 'mid.sentence := #2 'after.sentence := #3 'after.block := } STRINGS { s t } FUNCTION {output.nonnull} { 's := output.state mid.sentence = { ", " * write$ } { output.state after.block = { add.period$ write$ newline$ "\newblock " write$ } { output.state before.all = 'write$ { add.period$ " " * write$ } if$ } if$ mid.sentence 'output.state := } if$ s } FUNCTION {output} { duplicate$ empty$ 'pop$ 'output.nonnull if$ } FUNCTION {output.check} { 't := duplicate$ empty$ { pop$ "empty " t * " in " * cite$ * warning$ } 'output.nonnull if$ } FUNCTION {fin.entry} { add.period$ write$ newline$ } FUNCTION {new.block} { output.state before.all = 'skip$ { after.block 'output.state := } if$ } FUNCTION {new.sentence} { output.state after.block = 'skip$ { output.state before.all = 'skip$ { after.sentence 'output.state := } if$ } if$ } FUNCTION {add.blank} { " " * before.all 'output.state := } FUNCTION {date.block} { skip$ } FUNCTION {not} { { #0 } { #1 } if$ } FUNCTION {and} { 'skip$ { pop$ #0 } if$ } FUNCTION {or} { { pop$ #1 } 'skip$ if$ } FUNCTION {non.stop} { duplicate$ "}" * add.period$ #-1 #1 substring$ "." = } FUNCTION {new.block.checkb} { empty$ swap$ empty$ and 'skip$ 'new.block if$ } FUNCTION {field.or.null} { duplicate$ empty$ { pop$ "" } 'skip$ if$ } FUNCTION {emphasize} { duplicate$ empty$ { pop$ "" } { "\textit{" swap$ * "}" * } if$ } FUNCTION {capitalize} { "u" change.case$ "t" change.case$ } FUNCTION {space.word} { " " swap$ * " " * } % Here are the language-specific definitions for explicit words. % Each function has a name bbl.xxx where xxx is the English word. % The language selected here is ENGLISH FUNCTION {bbl.and} { "and"} FUNCTION {bbl.editors} { "eds." } FUNCTION {bbl.editor} { "ed." } FUNCTION {bbl.edby} { "edited by" } FUNCTION {bbl.edition} { "ed." } FUNCTION {bbl.volume} { "vol." } FUNCTION {bbl.of} { "of" } FUNCTION {bbl.number} { "no." } FUNCTION {bbl.nr} { "no." } FUNCTION {bbl.in} { "in" } FUNCTION {bbl.pages} { "pp." } FUNCTION {bbl.page} { "p." } FUNCTION {bbl.chapter} { "chap." } FUNCTION {bbl.techrep} { "Tech. Rep." } FUNCTION {bbl.mthesis} { "Master's thesis" } FUNCTION {bbl.phdthesis} { "Ph.D. thesis" } FUNCTION {bbl.first} { "1st" } FUNCTION {bbl.second} { "2nd" } FUNCTION {bbl.third} { "3rd" } FUNCTION {bbl.fourth} { "4th" } FUNCTION {bbl.fifth} { "5th" } FUNCTION {bbl.st} { "st" } FUNCTION {bbl.nd} { "nd" } FUNCTION {bbl.rd} { "rd" } FUNCTION {bbl.th} { "th" } MACRO {jan} {"Jan."} MACRO {feb} {"Feb."} MACRO {mar} {"Mar."} MACRO {apr} {"Apr."} MACRO {may} {"May"} MACRO {jun} {"Jun."} MACRO {jul} {"Jul."} MACRO {aug} {"Aug."} MACRO {sep} {"Sep."} MACRO {oct} {"Oct."} MACRO {nov} {"Nov."} MACRO {dec} {"Dec."} FUNCTION {eng.ord} { duplicate$ "1" swap$ * #-2 #1 substring$ "1" = { bbl.th * } { duplicate$ #-1 #1 substring$ duplicate$ "1" = { pop$ bbl.st * } { duplicate$ "2" = { pop$ bbl.nd * } { "3" = { bbl.rd * } { bbl.th * } if$ } if$ } if$ } if$ } MACRO {acmcs} {"ACM Computing Surveys"} MACRO {acta} {"Acta Informatica"} MACRO {cacm} {"Communications of the ACM"} MACRO {ibmjrd} {"IBM Journal of Research and Development"} MACRO {ibmsj} {"IBM Systems Journal"} MACRO {ieeese} {"IEEE Transactions on Software Engineering"} MACRO {ieeetc} {"IEEE Transactions on Computers"} MACRO {ieeetcad} {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"} MACRO {ipl} {"Information Processing Letters"} MACRO {jacm} {"Journal of the ACM"} MACRO {jcss} {"Journal of Computer and System Sciences"} MACRO {scp} {"Science of Computer Programming"} MACRO {sicomp} {"SIAM Journal on Computing"} MACRO {tocs} {"ACM Transactions on Computer Systems"} MACRO {tods} {"ACM Transactions on Database Systems"} MACRO {tog} {"ACM Transactions on Graphics"} MACRO {toms} {"ACM Transactions on Mathematical Software"} MACRO {toois} {"ACM Transactions on Office Information Systems"} MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"} MACRO {tcs} {"Theoretical Computer Science"} INTEGERS { nameptr namesleft numnames } FUNCTION {format.names} { 's := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{vv~}{ll}{, jj}{, f.}" format.name$ 't := nameptr #1 > { namesleft #1 > { ", " * t * } { numnames #2 > { "," * } 'skip$ if$ s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " et~al." * } { bbl.and space.word * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {format.names.ed} { format.names } FUNCTION {format.key} { empty$ { key field.or.null } { "" } if$ } FUNCTION {format.authors} { author empty$ { "" } { author format.names } if$ } FUNCTION {format.editors} { editor empty$ { "" } { editor format.names editor num.names$ #1 > { " (" * bbl.editors * ")" * } { " (" * bbl.editor * ")" * } if$ } if$ } FUNCTION {format.in.editors} { editor empty$ { "" } { editor format.names.ed } if$ } FUNCTION {format.note} { note empty$ { "" } { note #1 #1 substring$ duplicate$ "{" = 'skip$ { output.state mid.sentence = { "l" } { "u" } if$ change.case$ } if$ note #2 global.max$ substring$ * } if$ } FUNCTION {format.title} { title empty$ { "" } { title "\enquote{" swap$ * non.stop { ",} " * } { "} " * } if$ } if$ } FUNCTION {end.quote.title} { title empty$ 'skip$ { before.all 'output.state := } if$ } FUNCTION {format.full.names} {'s := #1 'nameptr := s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{vv~}{ll}" format.name$ 't := nameptr #1 > { namesleft #1 > { ", " * t * } { numnames #2 > { "," * } 'skip$ if$ s nameptr "{ll}" format.name$ duplicate$ "others" = { 't := } { pop$ } if$ t "others" = { " et~al." * } { bbl.and space.word * t * } if$ } if$ } 't if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {author.editor.key.full} { author empty$ { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.full.names } if$ } { author format.full.names } if$ } FUNCTION {author.key.full} { author empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { author format.full.names } if$ } FUNCTION {editor.key.full} { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.full.names } if$ } FUNCTION {make.full.names} { type$ "book" = type$ "inbook" = or 'author.editor.key.full { type$ "proceedings" = 'editor.key.full 'author.key.full if$ } if$ } FUNCTION {output.bibitem} { newline$ "\bibitem[{" write$ label write$ ")" make.full.names duplicate$ short.list = { pop$ } { * } if$ "}]{" * write$ cite$ write$ "}" write$ newline$ "" before.all 'output.state := } FUNCTION {n.dashify} { 't := "" { t empty$ not } { t #1 #1 substring$ "-" = { t #1 #2 substring$ "--" = not { "--" * t #2 global.max$ substring$ 't := } { { t #1 #1 substring$ "-" = } { "-" * t #2 global.max$ substring$ 't := } while$ } if$ } { t #1 #1 substring$ * t #2 global.max$ substring$ 't := } if$ } while$ } FUNCTION {word.in} { bbl.in " " * } FUNCTION {format.date} { year duplicate$ empty$ { "empty year in " cite$ * "; set to ????" * warning$ pop$ "????" } 'skip$ if$ extra.label * before.all 'output.state := " (" swap$ * ")" * } FUNCTION {format.btitle} { title emphasize } FUNCTION {tie.or.space.connect} { duplicate$ text.length$ #3 < { "~" } { " " } if$ swap$ * * } FUNCTION {either.or.check} { empty$ 'pop$ { "can't use both " swap$ * " fields in " * cite$ * warning$ } if$ } FUNCTION {format.bvolume} { volume empty$ { "" } { bbl.volume volume tie.or.space.connect series empty$ 'skip$ { bbl.of space.word * series emphasize * } if$ "volume and number" number either.or.check } if$ } FUNCTION {format.number.series} { volume empty$ { number empty$ { series field.or.null } { output.state mid.sentence = { bbl.number } { bbl.number capitalize } if$ number tie.or.space.connect series empty$ { "there's a number but no series in " cite$ * warning$ } { bbl.in space.word * series * } if$ } if$ } { "" } if$ } FUNCTION {is.num} { chr.to.int$ duplicate$ "0" chr.to.int$ < not swap$ "9" chr.to.int$ > not and } FUNCTION {extract.num} { duplicate$ 't := "" 's := { t empty$ not } { t #1 #1 substring$ t #2 global.max$ substring$ 't := duplicate$ is.num { s swap$ * 's := } { pop$ "" 't := } if$ } while$ s empty$ 'skip$ { pop$ s } if$ } FUNCTION {convert.edition} { edition extract.num "l" change.case$ 's := s "first" = s "1" = or { bbl.first 't := } { s "second" = s "2" = or { bbl.second 't := } { s "third" = s "3" = or { bbl.third 't := } { s "fourth" = s "4" = or { bbl.fourth 't := } { s "fifth" = s "5" = or { bbl.fifth 't := } { s #1 #1 substring$ is.num { s eng.ord 't := } { edition 't := } if$ } if$ } if$ } if$ } if$ } if$ t } FUNCTION {format.edition} { edition empty$ { "" } { output.state mid.sentence = { convert.edition "l" change.case$ " " * bbl.edition * } { convert.edition "t" change.case$ " " * bbl.edition * } if$ } if$ } INTEGERS { multiresult } FUNCTION {multi.page.check} { 't := #0 'multiresult := { multiresult not t empty$ not and } { t #1 #1 substring$ duplicate$ "-" = swap$ duplicate$ "," = swap$ "+" = or or { #1 'multiresult := } { t #2 global.max$ substring$ 't := } if$ } while$ multiresult } FUNCTION {format.pages} { pages empty$ { "" } { pages multi.page.check { bbl.pages pages n.dashify tie.or.space.connect } { bbl.page pages tie.or.space.connect } if$ } if$ } FUNCTION {format.journal.pages} { pages empty$ 'skip$ { duplicate$ empty$ { pop$ format.pages } { ", " * pages n.dashify * } if$ } if$ } FUNCTION {format.vol.num.pages} { volume field.or.null } FUNCTION {format.chapter.pages} { chapter empty$ { "" } { type empty$ { bbl.chapter } { type "l" change.case$ } if$ chapter tie.or.space.connect } if$ } FUNCTION {format.in.ed.booktitle} { booktitle empty$ { "" } { editor empty$ { word.in booktitle emphasize * } { word.in booktitle emphasize * ", " * editor num.names$ #1 > { bbl.editors } { bbl.editor } if$ * " " * format.in.editors * } if$ } if$ } FUNCTION {format.thesis.type} { type empty$ 'skip$ { pop$ type "t" change.case$ } if$ } FUNCTION {format.tr.number} { type empty$ { bbl.techrep } 'type if$ number empty$ { "t" change.case$ } { number tie.or.space.connect } if$ } FUNCTION {format.article.crossref} { word.in " \cite{" * crossref * "}" * } FUNCTION {format.book.crossref} { volume empty$ { "empty volume in " cite$ * "'s crossref of " * crossref * warning$ word.in } { bbl.volume volume tie.or.space.connect bbl.of space.word * } if$ " \cite{" * crossref * "}" * } FUNCTION {format.incoll.inproc.crossref} { word.in " \cite{" * crossref * "}" * } FUNCTION {format.publisher} { publisher empty$ { "empty publisher in " cite$ * warning$ } 'skip$ if$ "" address empty$ publisher empty$ and 'skip$ { address empty$ 'skip$ { address * } if$ publisher empty$ 'skip$ { address empty$ 'skip$ { ": " * } if$ publisher * } if$ } if$ output } STRINGS {oldname} FUNCTION {name.or.dash} { 's := oldname empty$ { s 'oldname := s } { s oldname = { "---" } { s 'oldname := s } if$ } if$ } FUNCTION {article} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title crossref missing$ { journal emphasize "journal" output.check format.vol.num.pages output } { format.article.crossref output.nonnull format.pages output } if$ format.journal.pages format.note output fin.entry } FUNCTION {book} { output.bibitem author empty$ { format.editors "author and editor" output.check editor format.key output name.or.dash } { format.authors output.nonnull name.or.dash crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ format.date "year" output.check date.block format.btitle "title" output.check crossref missing$ { format.bvolume output format.number.series output format.publisher } { format.book.crossref output.nonnull } if$ format.edition output format.note output fin.entry } FUNCTION {booklet} { output.bibitem format.authors output author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title howpublished output address output format.note output fin.entry } FUNCTION {inbook} { output.bibitem author empty$ { format.editors "author and editor" output.check editor format.key output name.or.dash } { format.authors output.nonnull name.or.dash crossref missing$ { "author and editor" editor either.or.check } 'skip$ if$ } if$ format.date "year" output.check date.block format.btitle "title" output.check crossref missing$ { format.publisher format.bvolume output format.chapter.pages "chapter and pages" output.check format.number.series output } { format.chapter.pages "chapter and pages" output.check format.book.crossref output.nonnull } if$ format.edition output format.pages "pages" output.check format.note output fin.entry } FUNCTION {incollection} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title crossref missing$ { format.in.ed.booktitle "booktitle" output.check format.publisher format.bvolume output format.number.series output format.chapter.pages output format.edition output } { format.incoll.inproc.crossref output.nonnull format.chapter.pages output } if$ format.pages "pages" output.check format.note output fin.entry } FUNCTION {inproceedings} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title crossref missing$ { format.in.ed.booktitle "booktitle" output.check publisher empty$ { organization output address output } { organization output format.publisher } if$ format.bvolume output format.number.series output format.pages output } { format.incoll.inproc.crossref output.nonnull format.pages output } if$ format.note output fin.entry } FUNCTION {conference} { inproceedings } FUNCTION {manual} { output.bibitem format.authors output author format.key output name.or.dash format.date "year" output.check date.block format.btitle "title" output.check organization output address output format.edition output format.note output fin.entry } FUNCTION {mastersthesis} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title bbl.mthesis format.thesis.type output.nonnull school "school" output.check address output format.note output fin.entry } FUNCTION {misc} { output.bibitem format.authors output author format.key output name.or.dash format.date "year" output.check date.block format.title output end.quote.title howpublished output format.note output fin.entry } FUNCTION {phdthesis} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title bbl.phdthesis format.thesis.type output.nonnull school "school" output.check address output format.note output fin.entry } FUNCTION {proceedings} { output.bibitem format.editors output editor format.key output name.or.dash format.date "year" output.check date.block format.btitle "title" output.check format.bvolume output format.number.series output address output organization output publisher output format.note output fin.entry } FUNCTION {techreport} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title format.tr.number output.nonnull institution "institution" output.check address output format.note output fin.entry } FUNCTION {unpublished} { output.bibitem format.authors "author" output.check author format.key output name.or.dash format.date "year" output.check date.block format.title "title" output.check end.quote.title format.note "note" output.check fin.entry } FUNCTION {default.type} { misc } READ FUNCTION {sortify} { purify$ "l" change.case$ } INTEGERS { len } FUNCTION {chop.word} { 's := 'len := s #1 len substring$ = { s len #1 + global.max$ substring$ } 's if$ } FUNCTION {format.lab.names} { 's := s #1 "{vv~}{ll}" format.name$ s num.names$ duplicate$ #2 > { pop$ " et~al." * } { #2 < 'skip$ { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" = { " et~al." * } { bbl.and space.word * s #2 "{vv~}{ll}" format.name$ * } if$ } if$ } if$ } FUNCTION {author.key.label} { author empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { author format.lab.names } if$ } FUNCTION {author.editor.key.label} { author empty$ { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.lab.names } if$ } { author format.lab.names } if$ } FUNCTION {editor.key.label} { editor empty$ { key empty$ { cite$ #1 #3 substring$ } 'key if$ } { editor format.lab.names } if$ } FUNCTION {calc.short.authors} { type$ "book" = type$ "inbook" = or 'author.editor.key.label { type$ "proceedings" = 'editor.key.label 'author.key.label if$ } if$ 'short.list := } FUNCTION {calc.label} { calc.short.authors short.list "(" * year duplicate$ empty$ { pop$ "????" } 'skip$ if$ * 'label := } FUNCTION {sort.format.names} { 's := #1 'nameptr := "" s num.names$ 'numnames := numnames 'namesleft := { namesleft #0 > } { s nameptr "{vv{ } }{ll{ }}{ f{ }}{ jj{ }}" format.name$ 't := nameptr #1 > { " " * namesleft #1 = t "others" = and { "zzzzz" * } { t sortify * } if$ } { t sortify * } if$ nameptr #1 + 'nameptr := namesleft #1 - 'namesleft := } while$ } FUNCTION {sort.format.title} { 't := "A " #2 "An " #3 "The " #4 t chop.word chop.word chop.word sortify #1 global.max$ substring$ } FUNCTION {author.sort} { author empty$ { key empty$ { "to sort, need author or key in " cite$ * warning$ "" } { key sortify } if$ } { author sort.format.names } if$ } FUNCTION {author.editor.sort} { author empty$ { editor empty$ { key empty$ { "to sort, need author, editor, or key in " cite$ * warning$ "" } { key sortify } if$ } { editor sort.format.names } if$ } { author sort.format.names } if$ } FUNCTION {editor.sort} { editor empty$ { key empty$ { "to sort, need editor or key in " cite$ * warning$ "" } { key sortify } if$ } { editor sort.format.names } if$ } FUNCTION {presort} { calc.label label sortify " " * type$ "book" = type$ "inbook" = or 'author.editor.sort { type$ "proceedings" = 'editor.sort 'author.sort if$ } if$ #1 entry.max$ substring$ 'sort.label := sort.label * " " * title field.or.null sort.format.title * #1 entry.max$ substring$ 'sort.key$ := } ITERATE {presort} SORT STRINGS { last.label next.extra } INTEGERS { last.extra.num number.label } FUNCTION {initialize.extra.label.stuff} { #0 int.to.chr$ 'last.label := "" 'next.extra := #0 'last.extra.num := #0 'number.label := } FUNCTION {forward.pass} { last.label label = { last.extra.num #1 + 'last.extra.num := last.extra.num int.to.chr$ 'extra.label := } { "a" chr.to.int$ 'last.extra.num := "" 'extra.label := label 'last.label := } if$ number.label #1 + 'number.label := } FUNCTION {reverse.pass} { next.extra "b" = { "a" 'extra.label := } 'skip$ if$ extra.label 'next.extra := extra.label duplicate$ empty$ 'skip$ { "{\natexlab{" swap$ * "}}" * } if$ 'extra.label := label extra.label * 'label := } EXECUTE {initialize.extra.label.stuff} ITERATE {forward.pass} REVERSE {reverse.pass} FUNCTION {bib.sort.order} { sort.label " " * year field.or.null sortify * " " * title field.or.null sort.format.title * #1 entry.max$ substring$ 'sort.key$ := } ITERATE {bib.sort.order} SORT FUNCTION {begin.bib} { preamble$ empty$ 'skip$ { preamble$ write$ newline$ } if$ "\begin{thebibliography}{" number.label int.to.str$ * "}" * write$ newline$ "\newcommand{\enquote}[1]{``#1''}" write$ newline$ "\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi" write$ newline$ } EXECUTE {begin.bib} EXECUTE {init.state.consts} ITERATE {call.type$} FUNCTION {end.bib} { newline$ "\end{thebibliography}" write$ newline$ } EXECUTE {end.bib} %% End of customized bst file %% %% End of file `asa.bst'. MatchIt/vignettes/Makefile0000755000176200001440000000023612162551623015262 0ustar liggesusersall: pdflatex matchit bibtex matchit pdflatex matchit pdflatex matchit matchit.pdf: pdflatex matchit bibtex matchit pdflatex matchit pdflatex matchit MatchIt/man/0000755000176200001440000000000012163124614012356 5ustar liggesusersMatchIt/man/user.prompt.Rd0000644000176200001440000000053311141576547015157 0ustar liggesusers\name{user.prompt} \alias{user.prompt} \title{Pause in demo files} \description{ Use \code{user.prompt} while writing demo files to force users to hit return before continuing. } \usage{ user.prompt() } \seealso{\code{readline}} \author{Olivia Lau \email{olau@fas.harvard.edu} } \examples{ \dontrun{ user.prompt() } } \keyword{file} MatchIt/man/matchit.Rd0000644000176200001440000001754111600517515014307 0ustar liggesusers\name{matchit} \alias{matchit} \alias{MatchIt} \alias{Matchit} \title{MatchIt: Matching Software for Causal Inference} \description{ \code{matchit} is the main command of the package \emph{MatchIt}, which enables parametric models for causal inference to work better by selecting well-matched subsets of the original treated and control groups. MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2004) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. Matched data sets created by MatchIt can be entered easily in Zelig (\url{http://gking.harvard.edu/zelig}) for subsequent parametric analyses. Full documentation is available online at \url{http://gking.harvard.edu/matchit}, and help for specific commands is available through \code{help.matchit}.} \details{The matching is done using the \code{matchit(treat ~ X, ...)} command, where \code{treat} is the vector of treatment assignments and \code{X} are the covariates to be used in the matching. There are a number of matching options, detailed below. The full syntax is \code{matchit(formula, data=NULL, discard=0, exact=FALSE, replace=FALSE, ratio=1, model="logit", reestimate=FALSE, nearest=TRUE, m.order=2, caliper=0, calclosest=FALSE, mahvars=NULL, subclass=0, sub.by="treat", counter=TRUE, full=FALSE, full.options=list(), \dots)} A summary of the results can be seen graphically using \code{plot(matchitobject)}, or numerically using \code{summary(matchitobject)}. \code{print(matchitobject)} also prints out the output. } \usage{matchit(formula, data, method = "nearest", distance = "logit", distance.options = list(), discard = "none", reestimate = FALSE, ...) } \arguments{ \item{formula}{This argument takes the usual syntax of R formula, \code{treat ~ x1 + x2}, where \code{treat} is a binary treatment indicator and \code{x1} and \code{x2} are the pre-treatment covariates. Both the treatment indicator and pre-treatment covariates must be contained in the same data frame, which is specified as \code{data} (see below). All of the usual R syntax for formula works. For example, \code{x1:x2} represents the first order interaction term between \code{x1} and \code{x2}, and \code{I(x1^2)} represents the square term of \code{x1}. See \code{help(formula)} for details.} \item{data}{This argument specifies the data frame containing the variables called in \code{formula}.} \item{method}{This argument specifies a matching method. Currently, \code{"exact"} (exact matching), \code{"full"} (full matching), \code{"genetic"} (genetic matching), \code{"nearest"} (nearest neighbor matching), \code{"optimal"} (optimal matching), and \code{"subclass"} (subclassification) are available. The default is \code{"nearest"}. Note that within each of these matching methods, \emph{MatchIt} offers a variety of options.} \item{distance}{This argument specifies the method used to estimate the distance measure. The default is logistic regression, \code{"logit"}. A variety of other methods are available.} \item{distance.options}{ This optional argument specifies the optional arguments that are passed to the model for estimating the distance measure. The input to this argument should be a list.} \item{discard}{This argument specifies whether to discard units that fall outside some measure of support of the distance score before matching, and not allow them to be used at all in the matching procedure. Note that discarding units may change the quantity of interest being estimated. The options are: \code{"none"} (default), which discards no units before matching, \code{"both"}, which discards all units (treated and control) that are outside the support of the distance measure, \code{"control"}, which discards only control units outside the support of the distance measure of the treated units, and \code{"treat"}, which discards only treated units outside the support of the distance measure of the control units.} \item{reestimate}{This argument specifies whether the model for distance measure should be re-estimated after units are discarded. The input must be a logical value. The default is \code{FALSE}.} \item{...}{Additional arguments to be passed to a variety of matching methods.} } \value{ \item{call}{The original \code{matchit} call.} \item{formula}{The formula used to specify the model for estimating the distance measure.} \item{model}{The output of the model used to estimate the distance measure. \code{summary(m.out$model)} will give the summary of the model where \code{m.out} is the output object from \code{matchit}.} \item{match.matrix}{An \eqn{n_1} by \code{ratio} matrix where the row names, which can be obtained through \code{row.names(match.matrix)}, represent the names of the treatment units, which come from the data frame specified in \code{data}. Each column stores the name(s) of the control unit(s) matched to the treatment unit of that row. For example, when the \code{ratio} input for nearest neighbor or optimal matching is specified as 3, the three columns of \code{match.matrix} represent the three control units matched to one treatment unit). \code{NA} indicates that the treatment unit was not matched.} \item{discarded}{A vector of length $n$ that displays whether the units were ineligible for matching due to common support restrictions. It equals \code{TRUE} if unit \eqn{i} was discarded, and it is set to \code{FALSE} otherwise.} \item{distance}{A vector of length \eqn{n} with the estimated distance measure for each unit.} \item{weights}{A vector of length \eqn{n} that provides the weights assigned to each unit in the matching process. Unmatched units have weights equal to \code{0}. Matched treated units have weight \code{1}. Each matched control unit has weight proportional to the number of treatment units to which it was matched, and the sum of the control weights is equal to the number of uniquely matched control units.} \item{subclass}{The subclass index in an ordinal scale from 1 to the total number of subclasses as specified in \code{subclass} (or the total number of subclasses from full or exact matching). Unmatched units have \code{NA}.} \item{q.cut}{The subclass cut-points that classify the distance measure.} \item{treat}{The treatment indicator from \code{data} (the left-hand side of \code{formula}).} \item{X}{The covariates used for estimating the distance measure (the right-hand side of \code{formula}).} \item{nn}{A basic summary table of matched data (e.g., the number of matched units)} } \seealso{Please use \code{help.matchit} to access the matchit reference manual. The complete document is available online at \url{http://gking.harvard.edu/matchit}. } \references{Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15(3): 199-236. \url{http://gking.harvard.edu/files/abs/matchp-abs.shtml} } \author{ Daniel Ho \email{daniel.ho@yale.edu}; Kosuke Imai \email{kimai@princeton.edu}; Gary King \email{king@harvard.edu}; Elizabeth Stuart\email{estuart@jhsph.edu} } \keyword{environment} MatchIt/man/match.data.Rd0000644000176200001440000000412511546107572014663 0ustar liggesusers\name{match.data} \alias{match.data} \title{Output Matched Data Sets} \description{\code{match.data} outputs matched data sets from \code{matchit()}. } \usage{ match.data(object, group="all", distance = "distance", weights = "weights", subclass = "subclass") } \arguments{ \item{object}{The output object from \code{matchit}. This is a required input.} \item{group}{This argument specifies for which matched group the user wants to extract the data. Available options are \code{"all"} (all matched units), \code{"treat"} (matched units in the treatment group), and \code{"control"} (matched units in the control group). The default is \code{"all"}.} \item{distance}{This argument specifies the variable name used to store the distance measure. The default is \code{"distance"}.} \item{weights}{This argument specifies the variable name used to store the resulting weights from matching. The default is \code{"weights"}.} \item{subclass}{This argument specifies the variable name used to store the subclass indicator. The default is \code{"subclass"}.} } \value{ Returns a subset of the original data set sent to \code{matchit()}, with just the matched units. The data set also contains the additional variables \code{distance}, \code{weights}, and \code{subclass}. The variable \code{distance} gives the estimated distance measure, and \code{weights} gives the weights for each unit, generated in the matching procedure. The variable \code{subclass} gives the subclass index for each unit (if applicable). See the \url{http://gking.harvard.edu/matchit/} for the complete documentation and type \code{demo(match.data)} at the R prompt to see a demonstration of the code. } \seealso{Please use \code{help.matchit} to access the matchit reference manual. The complete document is available online at \url{http://gking.harvard.edu/matchit}.} \author{ Daniel Ho \email{daniel.ho@yale.edu}; Kosuke Imai \email{kimai@princeton.edu}; Gary King \email{king@harvard.edu}; Elizabeth Stuart \email{estuart@jhsph.edu} } \keyword{methods} MatchIt/man/lalonde.Rd0000644000176200001440000000326311141576547014302 0ustar liggesusers\name{lalonde} \docType{data} \alias{lalonde} \title{Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).} \description{ This is a subsample of the data from the treated group in the National Supported Work Demonstration (NSW) and the comparison sample from the Current Population Survey (CPS). This data was previously analyzed extensively by Lalonde (1986) and Dehejia and Wahba (1999). The full dataset is available at \url{http://www.columbia.edu/~rd247/nswdata.html}. } \usage{data(lalonde)} \format{ A data frame with 313 observations (185 treated, 429 control). There are 10 variables measured for each individual. "treat" is the treatment assignment (1=treated, 0=control). "age" is age in years. "educ" is education in number of years of schooling. "black" is an indicator for African-American (1=African-American, 0=not). "hispan" is an indicator for being of Hispanic origin (1=Hispanic, 0=not). "married" is an indicator for married (1=married, 0=not married). "nodegree" is an indicator for whether the individual has a high school degree (1=no degree, 0=degree). "re74" is income in 1974, in U.S. dollars. "re75" is income in 1975, in U.S. dollars. "re78" is income in 1978, in U.S. dollars. } \references{ Lalonde, R. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review 76: 604-620. Dehejia, R.H. and Wahba, S. (1999). Causal Effects in Nonexperimental Studies: Re-Evaluating the Evaluation of Training Programs. Journal of the American Statistical Association 94: 1053-1062. } \source{\url{http://www.columbia.edu/~rd247/nswdata.html}} \keyword{datasets} MatchIt/man/help.matchit.Rd0000644000176200001440000000225111141576547015240 0ustar liggesusers\name{help.matchit} \alias{help.matchit} \title{HTML Help for Matchit Commands and Models} \description{ The \code{help.matchit} command launches html help for Matchit commands and supported methods. The full manual is available online at \url{http://gking.harvard.edu/matchit}. } \usage{ help.matchit(object) } \arguments{ \item{object}{a character string representing a Matchit command or model. \code{help.matchit("command")} will take you to an index of Matchit commands and \code{help.matchit("method")} will take you to a list of matching methods. The following inputs are currently available: \code{exact}, \code{nearest}, \code{subclass}, \code{full}, \code{optimal}. } } \seealso{The complete document is available online at \url{http://gking.harvard.edu/matchit}. } \author{ Daniel Ho <\email{daniel.ho@yale.edu}>; Kosuke Imai <\email{kimai@princeton.edu}>; Gary King <\email{king@harvard.edu}>; Elizabeth Stuart<\email{estuart@jhsph.edu}> } \keyword{documentation} MatchIt/inst/0000755000176200001440000000000012163124614012560 5ustar liggesusersMatchIt/inst/CITATION0000644000176200001440000000154311555344365013733 0ustar liggesuserscitHeader("To cite MatchIt in publications use:") citEntry(entry = "Article", title = "{MatchIt}: Nonparametric Preprocessing for Parametric Causal Inference", author = personList(as.person("Daniel E. Ho"), as.person("Kosuke Imai"), as.person("Gary King"), as.person("Elizabeth A. Stuart")), journal = "Journal of Statistical Software", year = "2011", volume = "42", number = "8", pages = "1--28", url = "http://www.jstatsoft.org/v42/i08/", textVersion = paste("Daniel E. Ho, Kosuke Imai, Gary King, Elizabeth A. Stuart (2011).", "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.", "Journal of Statistical Software, Vol. 42, No. 8, pp. 1-28.", "URL http://www.jstatsoft.org/v42/i08/") ) MatchIt/demo/0000755000176200001440000000000012163124614012527 5ustar liggesusersMatchIt/demo/subclass.R0000644000176200001440000000077210701247016014475 0ustar liggesusers### ### An Example Script for Subclassification ### ## load the Lalonde data data(lalonde) ## sublclassification m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "subclass") user.prompt() ## a short summary print(m.out) user.prompt() ## balance diagnostics print(summary(m.out)) user.prompt() ## balance diagnostics through plots plot(m.out) user.prompt() plot(m.out, type="jitter") s.out <- summary(m.out, standardize=TRUE) plot(s.out) MatchIt/demo/optimal.R0000644000176200001440000000112710701247016014316 0ustar liggesusers### ### An Example Script for Optimal Matching ### ## load the Lalonde data data(lalonde) ## optimal ratio matching using the propensity score based on logistic regression m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "optimal", distance = "logit", ratio = 2) user.prompt() ## a short summary print(m.out) user.prompt() ## balance diagnostics through statistics print(summary(m.out)) user.prompt() ## balance diagnostics through graphics plot(m.out) user.prompt() plot(m.out, type="jitter") s.out <- summary(m.out, standardize=TRUE) plot(s.out) MatchIt/demo/nearest.R0000644000176200001440000000555010701247016014316 0ustar liggesusers### ### An Example Script for Nearest Neighbor Matching ### data(lalonde) user.prompt() ## 1:1 Nearest neighbor matching m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "nearest") ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics s.out <- summary(m.out, standardize=TRUE) print(s.out) user.prompt() ## balance diagnostics through graphics plot(m.out) user.prompt() plot(m.out, type="jitter") user.prompt() plot(m.out, type="hist") user.prompt() plot(s.out) ## 2:1 Nearest neighbor matching m.out1 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", ratio=2) user.prompt() ## print a short summary print(m.out1) user.prompt() ## balance diagnostics through statistics print(summary(m.out1)) user.prompt() ## balance diagnostics through graphics plot(m.out) user.prompt() plot(m.out, type="jitter") ## 1:1 Nearest neighbor matching with Mahalanobis matching on re74 and re75 and exact matching on married m.out2 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", mahvars=c("re74", "re75"), exact=c("married"), caliper=.25) user.prompt() ## print a short summary print(m.out2) user.prompt() ## balance diagnostics through statistics s.out2 <- summary(m.out2, standardize=TRUE) print(s.out2) user.prompt() ## balance diagnostics through graphics plot(m.out2) user.prompt() plot(m.out2, type="jitter") user.prompt() plot(s.out2) ## 1:1 Nearest neighbor matching with units outside the common support discarded m.out3 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", discard= "both") user.prompt() ## print a short summary print(m.out3) user.prompt() ## balance diagnostics through statistics print(summary(m.out3)) user.prompt() ## balance diagnostics through graphics plot(m.out3) plot(m.out3, type="jitter") ## 2:1 Nearest neighbor matching with replacement m.out4 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", replace=TRUE, ratio=2) user.prompt() ## print a short summary print(m.out4) user.prompt() ## balance diagnostics through statistics print(summary(m.out4)) user.prompt() ## balance diagnostics through graphics plot(m.out4) plot(m.out4, type="jitter") plot(m.out4, type="hist") ## 1:1 Nearest neighbor matching followed by subclassification m.out5 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", subclass=5) user.prompt() ## print a short summary print(m.out5) user.prompt() ## balance diagnostics through statistics print(summary(m.out5)) user.prompt() ## balance diagnostics through graphics plot(m.out5) user.prompt() s.out5 <- summary(m.out5, standardize=TRUE) plot(s.out5) MatchIt/demo/match.data.R0000644000176200001440000000204410274456423014665 0ustar liggesusers### ### An Example Script for Obtaining Mathced Data ### ## load the Lalonde data data(lalonde) user.prompt() ## perform nearest neighbor matching m.out1 <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "nearest", distance = "logit") user.prompt() ## obtain matched data m.data1 <- match.data(m.out1) user.prompt() ## summarize the resulting matched data summary(m.data1) user.prompt() ## obtain matched data for the treatment group m.data2 <- match.data(m.out1, group = "treat") user.prompt() summary(m.data2) user.prompt() ## obtain matched data for the control group m.data3 <- match.data(m.out1, group = "control") user.prompt() summary(m.data3) user.prompt() ## run a subclassification method m.out2 <- matchit(treat ~ re74 + re75 + age + educ, data=lalonde, method = "subclass") user.prompt() ## specify different names m.data4 <- match.data(m.out2, subclass = "block", weights = "w", distance = "pscore") user.prompt() ## print the variable names of the matched data names(m.data4) MatchIt/demo/genetic.R0000644000176200001440000000101710701247016014265 0ustar liggesusers#### #### demo file for Genetic Matching #### ## loading the lalonde data data(lalonde) ## using logistic propensity score as one of the covariates m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "genetic", distance = "logit") user.prompt() ## printing a short summary print(m.out) user.prompt() ## numerical balance diagonstics s.out <- summary(m.out, standardize=TRUE) print(s.out) user.prompt() ## graphical balance diagnostics plot(m.out) plot(s.out) MatchIt/demo/full.R0000644000176200001440000000114510701142754013616 0ustar liggesusers### ### An Example Script for Full Matching ### ## load the Lalonde data data(lalonde) ## conduct full matching using the propensity score based on logistic regression m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "full", distance = "logit") ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics s.out <- summary(m.out) print(s.out) s.out <- summary(m.out, standardize=TRUE) print(s.out) user.prompt() ## balance diagnostics through graphics plot(m.out) plot(s.out) MatchIt/demo/exact.R0000644000176200001440000000054410701247016013757 0ustar liggesusers### ### An Example Script for Exact Matching ### ## laod the Lalonde data data(lalonde) ## exact matching m.out <- matchit(treat ~ educ + black + hispan, data = lalonde, method = "exact") user.prompt() ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics print(summary(m.out, covariates = T)) MatchIt/demo/cem.R0000644000176200001440000000247711023755604013433 0ustar liggesusers### ### An Example Script for Coarsened Exact Matching ### ## load the Lalonde data data(lalonde) ## coarsened exact matching with automatic coarsening m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "cem") user.prompt() ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics; standardize = T for plotting s.out <- summary(m.out, covariates = T, standardize = T) print(s.out) user.prompt() ## graphical balance checks plot(m.out) plot(s.out) ## create some cutpoints for continuous variables re74cut <- hist(lalonde$re74, br=seq(0,max(lalonde$re74)+1000, by=1000),plot=FALSE)$breaks re75cut <- hist(lalonde$re75, br=seq(0,max(lalonde$re75)+1000, by=1000),plot=FALSE)$breaks agecut <- hist(lalonde$age, br=seq(15,55, length=14),plot=FALSE)$breaks mycp <- list(re75=re75cut, re74=re74cut, age=agecut) ## coarsened exact matching with user-given cutpoints m.out2 <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "cem", cutpoints = mycp) user.prompt() ## print a short summary print(m.out2) user.prompt() ## balance diagnostics through statistics s.out2 <- summary(m.out2, covariates = T) print(s.out2) user.prompt() MatchIt/demo/analysis.R0000644000176200001440000000641710701247016014503 0ustar liggesusers### ### Example 1: calculating the average treatment effect for the treated ### ## load the Lalonde data data(lalonde) ## load Zelig package: if not already installed, try install.package("Zelig") library(Zelig) ## propensity score matching m.out1 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, method = "nearest", data = lalonde) user.prompt() ## fit the linear model to the control group controlling for propensity score and ## other covariates z.out1 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75 + distance, data = match.data(m.out1, "control"), model = "ls") user.prompt() ## set the covariates to the covariates of matched treated units ## use conditional prediction by setting cond = TRUE. x.out1 <- setx(z.out1, data = match.data(m.out1, "treat"), fn = NULL, cond = TRUE) user.prompt() ## simulate quantities of interest s.out1 <- sim(z.out1, x = x.out1) user.prompt() ## obtain a summary print(summary(s.out1)) user.prompt() ### ### Example 2: calculating the average treatment effect for the entire sample ### ## fit the linear model to the treatment group controlling for propensity score and ## other covariates z.out2 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75 + distance, data = match.data(m.out1, "treat"), model = "ls") user.prompt() ## conducting the simulation procedure for the control group x.out2 <- setx(z.out2, data = match.data(m.out1, "control"), fn = NULL, cond = TRUE) user.prompt() s.out2 <- sim(z.out2, x = x.out2) user.prompt() ## Note that Zelig calculates the difference between observed and ## either predicted or expected values. This means that the treatment ## effect for the control units is actually the effect of control ## (observed control outcome minus the imputed outcome under treatment ## from the model). Hence, to combine treatment effects just reverse ## the signs of the estimated treatment effect of controls. ate.all <- c(s.out1$qi$att.ev, -s.out2$qi$att.ev) user.prompt() ## some summaries ## point estimate print(mean(ate.all)) user.prompt() ## standard error print(sd(ate.all)) user.prompt() ## 95% confidence interval print(quantile(ate.all, c(0.025, 0.975))) user.prompt() ### ### Example 3: subclassification ### ## subclassification with 4 subclasses m.out2 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = lalonde, method = "subclass", subclass = 4) user.prompt() ## controlling only for the estimated prpensity score and lagged Y within each subclass ## one can potentially control for more z.out3 <- zelig(re78 ~ re74 + re75 + distance, data = match.data(m.out2, "control"), model = "ls", by = "subclass") user.prompt() ## conducting simulations x.out3 <- setx(z.out3, data = match.data(m.out2, "treat"), fn = NULL, cond = TRUE) user.prompt() ## for the demonstration purpose, we set the number of simulations to be 100 s.out3 <- sim(z.out3, x = x.out3, num = 100) user.prompt() ## overall results print(summary(s.out3)) user.prompt() ## summary for each subclass print(summary(s.out3, subset = 1)) user.prompt() print(summary(s.out3, subset = 2)) user.prompt() print(summary(s.out3, subset = 3)) MatchIt/demo/00Index0000644000176200001440000000103611024257145013662 0ustar liggesusersexact Demo of exact matching, using Lalonde dataset full Demo of full matching, using Lalonde dataset genetic Demo of genetic matching, using Lalonde dataset match.data Demo of obtaining matched data nearest Demos of nearest neighbor matching, using Lalonde dataset subclass Demo of subclassification, using Lalonde dataset optimal Demo of optimal matching, using Lalonde dataset analysis Demo of using Zelig with MatchIt for parametric causal inference after matching cem Demo of coarsened exact matching 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ÆèœíKq}æKâ[Œ$µ60kB>®?Íúé~Å– =8º>J¶&µ«}»ð˜Þ¯mRkÞYߌ^Âíw­á¯*t¹1RVÞÚÚŸùá0M)Ž>Æ5&¢J“ç…‰Í/š¢Í䲓ÊOYUËö4útXúP`²¾ú²o­ ®^ºˆ¬úJÝG}ßnëÙ$£äÛ’±3mžÏén#ë÷eáeO÷Q4§;¶ž„¦}¡±içvUÿäÐ&£e¶‡òr͈æVMQkÈqÎ3•i_N!RôJîØuwŠÉL!cçë¼…îÑGgï¾7§)(Ï¢°ŠSŠ¯{·ˆÝþƒÍü¤Â9‹÷voþ4³Ç\îa~{óÙAQíë1$¤ æbåœkÄHY­Ü›È¿±IÃe–£è<ºÙ7sm…—H{<:ÊDgpåM—Â]²ý4XiŽ&Ï£—µŠuÔší&0®ó‹tz^O„Rî¾íhÑM›­ÝÏÓ0»‚U­”ëϔϾíÒ2Ë©· ÄïdÜÕK2Xj|Ø2ÃE<3 çúªõ1Ò"ØwvF«G¼Ô’ûÙÌÚøqÑ^Õ?›S¶ÍV³ò¹­¥ü¥Ü£,ðå¢÷îÚ¶Ÿë2¼fPÌ€•ŸéGË*gÏõD»X¤ Nõ† ÕawiÍ;y:Zî><ƒ¼õd("C0F.‡¥itjºRÌ/™É3'(N̪^¼¨ˆ#—·›üê^é;dfú™ÌA{7ëtg¹ø£<¯°ŠÄ¾Ú—-øˆI`w.7º¾Á‚n¶’òj_î1¸K 3mäXk¾¤§_º02?¹ÃJüÐÇKhöoäçnï»oF¶»rFª‚«ì‰ÍeîþÕqéµ%F“¯˜°ïK.ORÒ§eäÊÖÜuLJåË‚ý™ÌÄæòçã.€h¤ºTÉb=³l™üèj‰"ͱd»ÛÞ-Æ ‚º½¹ç¼—Àû4-®Å:<¹Ø²tÈ»~Œ°cfáš»˜ ¸=Ëeõe´[‹’Gç_j̤ƒYÔò¸î°WÀþ´Á)ØlCaí£÷…jz/à‹Û'ÔgŽh£xo¢šVå:yw1îø~ÉšÝýçê­­ß»4tßU||<©òkO°+34v(yÖ£Õ­ë T•iít ʾZ't<|烶ž×ÒxÍÜ¥Ö–>ŸZËíq ͵åŒ*lp›wt¨_:W‰t)Kçå‚êô·Ö“ü¹rS±f7¿ÇÏ7N6œâU%bËA³ý72šÑÍ¢‹Åa“ÀQÞ±ëãF û‚a¿0:ÓY`yÒ³emφ¬s`•J䶗ÅÕÔç”îß]4ô»W4ïÒ™AÍÉÕ.¥<™F<$äV°ž"VEôQ‘—ü÷xÊüºÿù"Oï®  ù¨ü Ú 61ŠE¨öSX¾fŸ÷tMíW"ßóß/$[ðº ÕÞ÷ Ô}ú6´£,ùN–&B7Íñäü˜5±`+ñ¬ñ™mLȆj\‹ÑÐ7†W>¬°×ôÓ¶—ÌÙê 'è<›î&üù£àÇ€ùî;tËâ÷ip8r=äú½l,\*ÀÁl4Ö\隆åd»Áä4ŸŽö]´ÈÇø…ow™‹~àE¯>·Ýž ÷êÆ3¾´Kñï}}û&¾p°î<Œ²Ýo›ùaƒþ,DX~Z$žr±CßÖ¼)ƒñî Ôz¾®¢‹+x|’ÆT·5~ËÖi±Ï»¼—5ßRy¼¬ ¿Hf~WîVØÁ 4ÇWC)•­Ñ-ö?==þwrâeogbËnYyo®Å÷rJÊßÏ2“lžã—äe”¯~«$o%}?qû“Ãû²²ùÈI?žE¯»v{ýîú}­]Óéß(éí¾ÿ¥,{·‹ß]W¼m~⎽'6!ù‡VVnMÆ·‰ò’­¯)G5ò¹ôÊúDuQV:^¿ý7zÖïo]|áüBÙ¡f/T´ÇÊã)ËiëÉ?ǽº?XD¿táòR"ÆÊù¨/]¶öϪŽµdo¤ÿê}Šx›co¼,²âüxl³¦W¬¸9Ný¹ý\ž…I¦GädÃãû»V(”·ÿƂ݈ïÝù¼‚¼Õ¸"οúq¦Â@kMatchIt/R/0000755000176200001440000000000012163124614012004 5ustar liggesusersMatchIt/R/weights.subclass.R0000644000176200001440000000213410262644716015427 0ustar liggesusersweights.subclass <- function(psclass, treat) { ttt <- treat[!is.na(psclass)] classes <- na.omit(psclass) n <- length(ttt) labels <- names(ttt) tlabels <- labels[ttt==1] clabels <- labels[ttt==0] weights <- rep(0, n) names(weights) <- labels weights[tlabels] <- 1 for(j in unique(classes)){ qn0 <- sum(ttt==0 & classes==j) qn1 <- sum(ttt==1 & classes==j) weights[ttt==0 & classes==j] <- qn1/qn0 } if (sum(weights[ttt==0])==0) weights[ttt==0] <- rep(0, length(weights[clabels])) else { ## Number of C units that were matched to at least 1 T num.cs <- sum(weights[clabels] > 0) weights[clabels] <- weights[clabels]*num.cs/sum(weights[clabels]) } if (any(is.na(psclass))) { tmp <- rep(0, sum(is.na(psclass))) names(tmp) <- names(treat[is.na(psclass)]) weights <- c(weights, tmp)[names(treat)] } if (sum(weights)==0) stop("No units were matched") else if (sum(weights[tlabels])==0) stop("No treated units were matched") else if (sum(weights[clabels])==0) stop("No control units were matched") return(weights) } MatchIt/R/weights.matrix.R0000644000176200001440000000273710270312711015107 0ustar liggesusersweights.matrix <- function(match.matrix, treat, discarded){ n <- length(treat) labels <- names(treat) tlabels <- labels[treat==1] clabels <- labels[treat==0] in.sample <- !discarded names(in.sample) <- labels match.matrix <- match.matrix[tlabels,,drop=F][in.sample[tlabels],,drop=F] num.matches <- dim(match.matrix)[2]-apply(as.matrix(match.matrix), 1, function(x){sum(is.na(x))}) names(num.matches) <- tlabels[in.sample[tlabels]] t.units <- row.names(match.matrix)[num.matches>0] c.units <- na.omit(as.vector(as.matrix(match.matrix))) weights <- rep(0,length(treat)) names(weights) <- labels weights[t.units] <- 1 for (cont in clabels) { treats <- na.omit(row.names(match.matrix)[cont==match.matrix[,1]]) if (dim(match.matrix)[2]>1) for (j in 2:dim(match.matrix)[2]) treats <- c(na.omit(row.names(match.matrix)[cont==match.matrix[,j]]),treats) for (k in unique(treats)) weights[cont] <- weights[cont] + 1/num.matches[k] } if (sum(weights[clabels])==0) weights[clabels] <- rep(0, length(weights[clabels])) else weights[clabels] <- weights[clabels]*length(unique(c.units))/sum(weights[clabels]) weights[!in.sample] <- 0 if (sum(weights)==0) stop("No units were matched") else if (sum(weights[tlabels])==0) stop("No treated units were matched") else if (sum(weights[clabels])==0) stop("No control units were matched") return(weights) } MatchIt/R/user.prompt.R0000644000176200001440000000012210425406160014416 0ustar liggesusersuser.prompt <- function() silent <- readline("\nPress to continue: ") MatchIt/R/summary.matchit.subclass.R0000644000176200001440000001016611024275003017070 0ustar liggesuserssummary.matchit.subclass <- function(object, interactions = FALSE, addlvariables=NULL, standardize = FALSE, ...) { X <- object$X ## Fix X matrix so that it doesn't have any factors varnames <- colnames(X) for(var in varnames) { if(is.factor(X[,var])) { tempX <- X[,!colnames(X)%in%c(var)] form<-formula(substitute(~dummy-1,list(dummy=as.name(var)))) X <- cbind(tempX, model.matrix(form, X)) } } XX <- cbind(distance=object$distance,X) if (!is.null(addlvariables)) XX <- cbind(XX, addlvariables) treat <- object$treat weights <- object$weights nam <- dimnames(XX)[[2]] kk <- ncol(XX) ## Summary Stats aa <- apply(XX,2,qoi,tt=treat,ww=as.numeric(weights!=0),standardize=standardize) sum.all <- as.data.frame(matrix(0,kk,6)) sum.matched <- as.data.frame(matrix(0,kk,6)) row.names(sum.all) <- row.names(sum.matched) <- nam names(sum.all) <- names(sum.matched) <- names(aa[[1]]) sum.all.int <- sum.matched.int <- NULL for(i in 1:kk){ sum.all[i,] <- aa[[i]][1,] sum.matched[i,] <- aa[[i]][2,] if(interactions){ for(j in i:kk){ x2 <- XX[,i]*as.matrix(XX[,j]) jqoi <- qoi(x2,tt=treat,ww=as.numeric(weights!=0),standardize=standardize) sum.all.int <- rbind(sum.all.int,jqoi[1,]) sum.matched.int <- rbind(sum.matched.int,jqoi[2,]) row.names(sum.all.int)[nrow(sum.all.int)] <- row.names(sum.matched.int)[nrow(sum.matched.int)] <- paste(nam[i],nam[j],sep="x") } } } xn <- aa[[1]]$xn sum.all <- rbind(sum.all,sum.all.int) sum.matched <- rbind(sum.matched,sum.matched.int) ## By Subclass qbins <- max(object$subclass,na.rm=TRUE) if(interactions){ q.table <- array(0,dim=c(kk+sum(1:kk),6,qbins)) ii <- 0 nn <- NULL } else { q.table <- array(0,dim=c(kk,6,qbins)) } aa <- apply(XX,2,qoi.by.sub,tt=treat,ww=weights, qq=object$subclass,standardize=standardize) for(i in 1:kk){ if(!interactions){ q.table[i,,] <- as.matrix(aa[[i]]$q.table) nn <- names(aa) } else { ii <- ii + 1 q.table[ii,,] <- as.matrix(aa[[i]]$q.table) nn <- c(nn,names(aa)[i]) for(j in i:kk){ ii <- ii + 1 x2 <- XX[,i]*as.matrix(XX[,j]) q.table[ii,,] <- as.matrix(qoi.by.sub(x2,tt=treat,ww=weights,qq=object$subclass,standardize=standardize)$q.table) nn <- c(nn,paste(nam[i],nam[j],sep="x")) } } } qn <- aa[[1]]$qn dimnames(q.table) <- list(nn,row.names(aa[[i]]$q.table),paste("Subclass",1:qbins)) ## Aggregate Subclass if(is.null(object$call$sub.by)){ object$call$sub.by <- "treat" } if(object$call$sub.by=="treat") { wsub <- qn[1,]/sum(qn[1,]) } else if(object$call$sub.by=="control") { wsub <- qn[2,]/sum(qn[2,]) } else if(object$call$sub.by=="all") { wsub <- qn[3,]/sum(qn[3,]) } sum.subclass <- sum.all for(i in 1:kk){ for(j in 1:6){ if(j==3) { sum.subclass[i,j] <- sqrt(sum((wsub^2)*(q.table[i,j,]^2))) } else { sum.subclass[i,j] <- sum(wsub*q.table[i,j,]) } } } ## Imbalance Reduction stat0 <- abs(cbind(sum.all[,2]-sum.all[,1], sum.all[,4:6])) stat1 <- abs(cbind(sum.subclass[,2]-sum.subclass[,1], sum.subclass[,4:6])) reduction <- as.data.frame(100*(stat0-stat1)/stat0) if(sum(stat0==0 & stat1==0, na.rm=T)>0){ reduction[stat0==0 & stat1==0] <- 0 } if(sum(stat0==0 & stat1>0,na.rm=T)>0){ reduction[stat0==0 & stat1>0] <- -Inf } if (standardize) names(reduction) <- c("Std. Mean Diff.", "eCDF Med","eCDF Mean", "eCDF Max") else names(reduction) <- c("Mean Diff.", "eQQ Med","eQQ Mean", "eQQ Max") ## output res <- list(call=object$call, sum.all = sum.all, sum.matched = sum.matched, sum.subclass = sum.subclass, reduction = reduction, qn = qn, q.table = q.table) class(res) <- c("summary.matchit.subclass", "summary.matchit") return(res) } MatchIt/R/summary.matchit.full.R0000644000176200001440000000617710703212305016221 0ustar liggesuserssummary.matchit.full <- function(object, interactions = FALSE, addlvariables = NULL, numdraws = 5000, standardize = FALSE, ...) { XX <- cbind(distance=object$distance,object$X) if (!is.null(addlvariables)) XX <- cbind(XX, addlvariables) treat <- object$treat weights <- object$weights nam <- dimnames(XX)[[2]] kk <- ncol(XX) ## Get samples of T and C units to send to qqplot t.plot <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=weights[treat==1]) c.plot <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=weights[treat==0]) ## Summary Stats aa <- apply(XX,2,qoi,tt=treat,ww=weights, t.plot=t.plot, c.plot=c.plot, standardize=standardize) sum.all <- as.data.frame(matrix(0,kk,6)) sum.matched <- as.data.frame(matrix(0,kk,6)) row.names(sum.all) <- row.names(sum.matched) <- nam names(sum.all) <- names(sum.matched) <- names(aa[[1]]) sum.all.int <- sum.matched.int <- NULL for(i in 1:kk){ sum.all[i,] <- aa[[i]][1,] sum.matched[i,] <- aa[[i]][2,] if(interactions){ for(j in i:kk){ x2 <- XX[,i]*as.matrix(XX[,j]) names(x2) <- names(XX[,1]) jqoi <- qoi(x2,tt=treat,ww=weights, t.plot=t.plot, c.plot=c.plot, standardize=standardize) sum.all.int <- rbind(sum.all.int,jqoi[1,]) sum.matched.int <- rbind(sum.matched.int,jqoi[2,]) row.names(sum.all.int)[nrow(sum.all.int)] <- row.names(sum.matched.int)[nrow(sum.matched.int)] <- paste(nam[i],nam[j],sep="x") } } } xn <- aa[[1]]$xn sum.all <- rbind(sum.all,sum.all.int) sum.matched <- rbind(sum.matched,sum.matched.int) ## Imbalance Reduction stat0 <- abs(cbind(sum.all[,2]-sum.all[,1], sum.all[,4:6])) stat1 <- abs(cbind(sum.matched[,2]-sum.matched[,1], sum.matched[,4:6])) reduction <- as.data.frame(100*(stat0-stat1)/stat0) if(sum(stat0==0 & stat1==0, na.rm=T)>0){ reduction[stat0==0 & stat1==0] <- 0 } if(sum(stat0==0 & stat1>0,na.rm=T)>0){ reduction[stat0==0 & stat1>0] <- -Inf } if (standardize) names(reduction) <- c("Std. Mean Diff.", "eCDF Med","eCDF Mean", "eCDF Max") else names(reduction) <- c("Mean Diff.", "eQQ Med","eQQ Mean", "eQQ Max") ## Sample sizes nn <- matrix(0, ncol=2, nrow=4) nn[1,] <- c(sum(object$treat==0), sum(object$treat==1)) nn[2,] <- c(sum(object$treat==0 & object$weights>0), sum(object$treat==1 & object$weights>0)) nn[3,] <- c(sum(object$treat==0 & object$weights==0 & object$discarded==0), sum(object$treat==1 & object$weights==0 & object$discarded==0)) nn[4,] <- c(sum(object$treat==0 & object$weights==0 & object$discarded==1), sum(object$treat==1 & object$weights==0 & object$discarded==1)) dimnames(nn) <- list(c("All","Matched","Unmatched","Discarded"), c("Control","Treated")) ## output res <- list(call=object$call, nn = nn, sum.all = sum.all, sum.matched = sum.matched, reduction = reduction) class(res) <- c("summary.matchit.full", "summary.matchit") return(res) } MatchIt/R/summary.matchit.exact.R0000644000176200001440000000253011041165132016351 0ustar liggesuserssummary.matchit.exact <- function(object, covariates = FALSE, ...) { XX <- object$X treat <- object$treat qbins <- max(object$subclass,na.rm=TRUE) if(!covariates){ q.table <- as.data.frame(matrix(0,qbins,3)) names(q.table) <- c("Treated","Control","Total") for(i in 1:qbins){ qi <- object$subclass==i q.table[i,] <- c(sum(treat[qi]==1, na.rm=T), sum(treat[qi]==0, na.rm=T), length(treat[qi & !is.na(qi)])) } } else { kk <- ncol(XX) q.table <- as.data.frame(matrix(0,qbins,kk+3)) names(q.table) <- c("Treated","Control","Total",dimnames(XX)[[2]]) for(i in 1:qbins){ qi <- object$subclass==i & !is.na(object$subclass==i) q.table[i,] <- c(sum(treat[qi]==1, na.rm=T), sum(treat[qi]==0, na.rm=T), length(treat[qi & !is.na(qi)]),as.numeric(XX[qi,,drop=F][1,])) } } ntab <- table(factor(!is.na(object$subclass), levels=c("TRUE","FALSE")), treat) nn <- rbind(table(treat), ntab[c("TRUE","FALSE"),]) dimnames(nn) <- list(c("All","Matched","Discarded"), c("Control","Treated")) ## output res <- list(q.table = q.table, nn = nn, subclass = object$subclass, treat = object$treat, call = object$call) class(res) <- c("summary.matchit.exact", "summary.matchit") return(res) } MatchIt/R/summary.matchit.R0000644000176200001440000000530611434402563015262 0ustar liggesuserssummary.matchit <- function(object, interactions = FALSE, addlvariables = NULL, standardize = FALSE, ...) { X <- object$X ## Fix X matrix so that it doesn't have any factors varnames <- colnames(X) for(var in varnames) { if(is.factor(X[,var])) { tempX <- X[,!colnames(X)%in%c(var)] form<-formula(substitute(~dummy-1,list(dummy=as.name(var)))) X <- cbind(tempX, model.matrix(form, X)) } } ## No distance output for pure Mahalanobis if("matchit.mahalanobis"%in%class(object)){ XX <- X } else{ XX <- cbind(distance=object$distance,X) } if (!is.null(addlvariables)) XX <- cbind(XX, addlvariables) treat <- object$treat weights <- object$weights nam <- dimnames(XX)[[2]] dupnam <- duplicated(nam) if(sum(dupnam)>0){ nam[dupnam] <- paste(nam[dupnam],".1",sep="") } kk <- ncol(XX) ## Summary Stats aa <- apply(XX,2,qoi,tt=treat,ww=weights,standardize=standardize,std=T) sum.all <- as.data.frame(matrix(0,kk,7)) sum.matched <- as.data.frame(matrix(0,kk,7)) row.names(sum.all) <- row.names(sum.matched) <- nam names(sum.all) <- names(sum.matched) <- names(aa[[1]]) sum.all.int <- sum.matched.int <- NULL for(i in 1:kk){ sum.all[i,] <- aa[[i]][1,] sum.matched[i,] <- aa[[i]][2,] if(interactions){ for(j in i:kk){ x2 <- XX[,i]*as.matrix(XX[,j]) jqoi <- qoi(x2,tt=treat,ww=weights,standardize=standardize,std=T) sum.all.int <- rbind(sum.all.int,jqoi[1,]) sum.matched.int <- rbind(sum.matched.int,jqoi[2,]) row.names(sum.all.int)[nrow(sum.all.int)] <- row.names(sum.matched.int)[nrow(sum.matched.int)] <- paste(nam[i],nam[j],sep="x") } } } xn <- aa[[1]]$xn sum.all <- rbind(sum.all,sum.all.int) sum.matched <- rbind(sum.matched,sum.matched.int) ## Imbalance Reduction stat0 <- abs(cbind(sum.all[,2]-sum.all[,1], sum.all[,5:7])) stat1 <- abs(cbind(sum.matched[,2]-sum.matched[,1], sum.matched[,5:7])) reduction <- as.data.frame(100*(stat0-stat1)/stat0) if(sum(stat0==0 & stat1==0, na.rm=T)>0){ reduction[stat0==0 & stat1==0] <- 0 } if(sum(stat0==0 & stat1>0,na.rm=T)>0){ reduction[stat0==0 & stat1>0] <- -Inf } if (standardize) names(reduction) <- c("Std. Mean Diff.", "eCDF Med","eCDF Mean", "eCDF Max") else names(reduction) <- c("Mean Diff.", "eQQ Med","eQQ Mean", "eQQ Max") ## output res <- list(call=object$call, nn = object$nn, sum.all = sum.all, sum.matched = sum.matched, reduction = reduction) class(res) <- "summary.matchit" return(res) } MatchIt/R/qqsum.R0000644000176200001440000000120010331713325013264 0ustar liggesusers## Function for QQ summary stats qqsum <- function (x, y, standardize = FALSE){ sx <- sort(x) sy <- sort(y) lenx <- length(sx) leny <- length(sy) if (standardize) { vals <- sort(unique(c(sx, sy))) sx <- ecdf(sx) sx <- sx(vals) sy <- ecdf(sy) sy <- sy(vals) } else { if (leny < lenx) sx <- approx(1:lenx, sx, n = leny, method = "constant")$y if (leny > lenx) sy <- approx(1:leny, sy, n = lenx, method = "constant")$y } dxy <- abs(sx-sy) meandiff <- mean(dxy) meddiff <- median(dxy) maxdiff <- max(dxy) invisible(list(meandiff=meandiff, meddiff=meddiff, maxdiff=maxdiff)) } MatchIt/R/print.summary.matchit.subclass.R0000644000176200001440000000142010304323376020223 0ustar liggesusersprint.summary.matchit.subclass <- function(x, digits = max(3, getOption("digits") - 3), ...){ sum.all <- x$sum.all sum.matched <- x$sum.matched q.table <- x$q.table cat("\nCall:", deparse(x$call), sep = "\n") cat("Summary of balance for all data:\n") print.data.frame(round(sum.all,digits)) cat("\n") cat("\nSummary of balance by subclasses:\n") print.table(round(q.table, digits)) cat("\nSample sizes by subclasses:\n") print.data.frame(x$qn, digits = digits) cat("\nSummary of balance across subclasses\n") print.data.frame(round(x$sum.subclass, digits)) cat("\nPercent Balance Improvement:\n") print.data.frame(round(x$reduction,digits)) cat("\n") } MatchIt/R/print.summary.matchit.exact.R0000644000176200001440000000063011041165132017503 0ustar liggesusersprint.summary.matchit.exact <- function(x, digits = max(3, getOption("digits") - 3), ...){ cat("\nCall:", deparse(x$call), sep = "\n") cat("\nSample sizes:\n") print.table(x$nn,digits=digits) cat("\nMatched sample sizes by subclass:\n") print.data.frame(x$q.table, digits = digits) cat("\n") invisible(x) } MatchIt/R/print.summary.matchit.R0000644000176200001440000000142210304323376016407 0ustar liggesusersprint.summary.matchit <- function(x, digits = max(3, getOption("digits") - 3), ...){ sum.all <- x$sum.all sum.matched <- x$sum.matched q.table <- x$q.table xn <- x$nn cat("\nCall:", deparse(x$call), sep = "\n") cat("\nSummary of balance for all data:\n") print.data.frame(round(sum.all,digits)) cat("\n") xs1 <- sum.matched cc <- row.names(sum.all) if(!is.null(x$sum.matched) | identical(eval(x$call$method),"All")) { cat("\nSummary of balance for matched data:\n") print.data.frame(round(xs1,digits)) cat("\nPercent Balance Improvement:\n") print.data.frame(round(x$reduction,digits)) cat("\nSample sizes:\n") print.table(xn, digits=digits) cat("\n") } invisible(x) } MatchIt/R/print.matchit.subclass.R0000644000176200001440000000061310304316120016517 0ustar liggesusersprint.matchit.subclass <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep = "\n") cat("\nSample sizes by subclasses:\n\n") nsub <- table(x$subclass,x$treat) nn <- rbind(table(x$treat),nsub) dimnames(nn) <- list(c("All",paste("Subclass",dimnames(nsub)[[1]])), c("Control","Treated")) print.table(nn, ...) invisible(x) cat("\n") } MatchIt/R/print.matchit.full.R0000644000176200001440000000100510304316120015636 0ustar liggesusersprint.matchit.full <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep = "\n") cat("\nSample sizes:\n") if (any(x$weights>0)) nn <- rbind(table(x$treat), table(x$weights>0, x$treat), c(0,0)) else nn <- rbind(table(x$treat), table(x$weights>0,x$treat)[2:1,]) dimnames(nn) <- list(c("All","Matched","Discarded"), c("Control","Treated")) print.table(nn, ...) invisible(x) cat("\n") } MatchIt/R/print.matchit.exact.R0000644000176200001440000000105410304316120016004 0ustar liggesusersprint.matchit.exact <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep = "\n") cat("\nExact Subclasses: ", max(x$subclass, na.rm=T),"\n",sep="") cat("\nSample sizes:\n") ntab <- table(factor(!is.na(x$subclass), levels=c("TRUE","FALSE")), x$treat) nn <- rbind(table(x$treat), ntab[c("TRUE","FALSE"),]) dimnames(nn) <- list(c("All","Matched","Unmatched"), c("Control","Treated")) print.table(nn, ...) invisible(x) cat("\n") } MatchIt/R/print.matchit.R0000644000176200001440000000064111430774543014724 0ustar liggesusersprint.matchit <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep="\n") cat("\nSample sizes:\n") #if(any(x$weights>0)) # nn <- rbind(table(x$treat), # table(x$weights>0, x$treat), # c(0,0)) #else # nn <- rbind(table(x$treat), # table(x$weights>0,x$treat)[2:1,]) print.table(x$nn, ...) invisible(x) cat("\n") } MatchIt/R/plot.summary.matchit.R0000644000176200001440000000333410703021044016223 0ustar liggesusersplot.summary.matchit <- function(x, interactive = TRUE, ...) { if ("matchit.exact" %in% class(x)){ stop("Not appropriate for exact matching. No plots generated.") } if (!"Std. Mean Diff."%in%names(x$sum.all)){ stop("Not appropriate for unstandardized summary. Run summary() with the standardize=TRUE option, and then plot.") } sd.pre <- abs(x$sum.all$"Std. Mean Diff.") sd.post <- abs(x$sum.matched$"Std. Mean Diff.") if (!is.null(x$q.table)) sd.post <- abs(x$sum.subclass$"Std. Mean Diff") ases.dat <- data.frame(es.unw = sd.pre, es.w = sd.post) par(mfrow=c(1,1)) plot(c(0.85, 2.15), c(0, min(3, max(unlist(ases.dat[, 1:2]), na.rm = TRUE))), type = "n", xaxt = "n", ylab = "Absolute Standardized Diff in Means", xlab = "", main = "") abline(h = c(0.2, 0.4, 0.6, 0.8, 1.0)) axis(side = 1, at = 1:2, labels = c("All Data", "Matched Data")) for (i in 1:nrow(ases.dat)) { points(1:2, abs(ases.dat[i, c("es.unw", "es.w")]), type = "b", col = "grey", pch=19) } temp1 <- ases.dat[abs(ases.dat$es.unw) < abs(ases.dat$es.w),] for (i in 1:nrow(temp1)) { points(1:2, abs(temp1[i, c("es.unw", "es.w")]), type = "b", col = "black", lwd = 2, pch=19) } if (max(ases.dat$es.w, na.rm = TRUE) > 3) mtext(text = "Some standardized diffs in means > 3 after matching!", side = 3, col = "red") if(interactive==TRUE) { print("To identify the variables, use first mouse button; to stop, use second.") identify(rep(1, length(sd.pre)),sd.pre,rownames(x$sum.all),atpen=T) identify(rep(2, length(sd.post)),sd.post,rownames(x$sum.all),atpen=T) } } MatchIt/R/plot.matchit.subclass.R0000644000176200001440000000277111221036205016352 0ustar liggesusersplot.matchit.subclass <- function(x, discrete.cutoff=5, type="QQ", interactive = T, subclass = NULL, which.xs=NULL,...){ choice.menu <- function(choices,question) { k <- length(choices)-1 Choices <- data.frame(choices) row.names(Choices) <- 0:k names(Choices) <- "Choices" print.data.frame(Choices,right=FALSE) ans <- readline(question) while(!ans%in%c(0:k)) { print("Not valid -- please pick one of the choices") print.data.frame(Choices,right=FALSE) ans <- readline(question) } return(ans) } if(type=="QQ"){ if(interactive){ choices <- c("No",paste("Yes : Subclass ", 1:max(x$subclass,na.rm=T))) question <- "Would you like to see quantile-quantile plots of any subclasses?" ans <- -1 while(ans!=0) { ans <- as.numeric(choice.menu(choices,question)) if(ans!=0) { matchit.qqplot(x,discrete.cutoff,which.subclass=ans, interactive = interactive, which.xs=which.xs,...) } } } else { matchit.qqplot(x,discrete.cutoff,which.subclass=subclass, interactive=interactive, which.xs=which.xs,...) } } else if(type=="jitter"){ jitter.pscore(x, interactive=interactive,...) } else if(type=="hist"){ hist.pscore(x,...) } else { stop("Invalid type") } } MatchIt/R/plot.matchit.R0000644000176200001440000000170111434402563014536 0ustar liggesusers# Need to account for weights -- how do we do qq plots with weights plot.matchit <- function(x, discrete.cutoff=5, type="QQ", numdraws=5000, interactive = T, which.xs = NULL, ...){ if ("matchit.exact" %in% class(x)){ stop("Not appropriate for exact matching. No plots generated.") } if(type=="QQ"){ matchit.qqplot(x=x,discrete.cutoff=discrete.cutoff, numdraws=numdraws, interactive=interactive, which.xs = which.xs, ...) } else if(type=="jitter"){ if("matchit.mahalanobis" %in% class(x)){ stop("Not appropriate for pure Mahalanobis matching. No plots generated.") } jitter.pscore(x, interactive=interactive,...) } else if(type=="hist"){ if("matchit.mahalanobis" %in% class(x)){ stop("Not appropriate for pure Mahalanobis matching. No plots generated.") } hist.pscore(x,...) } else { stop("Invalid type") } } MatchIt/R/matchit2subclass.R0000644000176200001440000000716211500472177015414 0ustar liggesusersmatchit2subclass <- function(treat, X, data, distance, discarded, is.full.mahalanobis, match.matrix=NULL, subclass=6, sub.by="treat", verbose = FALSE){ if(verbose) cat("Subclassifying... \n") # sub.by if(!is.vector(sub.by)|length(sub.by)!=1){ warning(sub.by," is not a valid sub.by option; sub.by=\"treat\" used instead",call.=FALSE); sub.by <- "treat"} if(!sub.by%in%c("treat","control","all")){ warning(sub.by, " is not a valid sub.by option; sub.by=\"treat\" used instead",call.=FALSE); sub.by <- "treat"} #subclass if(length(subclass)==1){ if(subclass<0 | subclass==1){ warning(subclass, " is not a valid subclass; subclass=6 used instead",call.=FALSE); subclass <- 6} } else { if(!is.vector(subclass)){ warning(subclass, " is not a valid subclass; subclass=6 used instead",call.=FALSE); subclass <- 6} if(sum(subclass<=1 & subclass>=0)!=length(subclass)){ warning("Subclass ", subclass, " is not bounded by 0 and 1; subclass=6 used instead", call.=FALSE); subclass <- 6} } in.sample <- !discarded n <- length(treat) ## Matching & Subclassification if(!is.null(match.matrix)){ #match.matrix <- match.matrix[in.sample[treat==1],,drop=F] t.units <- row.names(match.matrix)[in.sample[treat==1]==1] c.units <- na.omit(as.vector(as.matrix(match.matrix))) matched <-c(t.units,c.units) matched <- names(treat)%in%matched } else matched <- rep(TRUE,n) names(matched) <- names(treat) m1 <- matched[treat==1] m0 <- matched[treat==0] p1 <- distance[treat==1][m1] p0 <- distance[treat==0][m0] ## Settting Cut Points if(length(subclass)!=1 | (length(subclass)==1 & all(subclass<1))) { subclass <- sort(subclass) if (subclass[1]==0) subclass <- subclass[-1] if (subclass[length(subclass)]==1) subclass <- subclass[-length(subclass)] if(sub.by=="treat") q <- c(0,quantile(p1,probs=c(subclass)),1) else if(sub.by=="control") q <- c(0,quantile(p0,probs=c(subclass)),1) else if(sub.by=="all") q <- c(0,quantile(distance,probs=c(subclass)),1) else stop("Invalid input for sub.by") } else { if(subclass<=0){stop("Subclass must be a positive vector",call.=FALSE)} sprobs <- seq(0,1,length=(round(subclass)+1)) sprobs <- sprobs[2:(length(sprobs)-1)] min.dist <- min(distance,na.rm=TRUE)-0.01 max.dist <- max(distance,na.rm=TRUE)+0.01 if(sub.by=="treat") q <- c(min.dist,quantile(p1,probs=sprobs,na.rm=TRUE), max.dist) else if(sub.by=="control") q <- c(min.dist,quantile(p0,probs=sprobs,na.rm=TRUE), max.dist) else if(sub.by=="all") q <- c(min.dist, quantile(distance,probs=sprobs,na.rm=TRUE), max.dist) else stop("Must specify a valid sub.by",call.=FALSE) } ## Calculating Subclasses qbins <- length(q)-1 psclass <- rep(0,n) names(psclass) <- names(treat) for (i in 1:qbins){ q1 <- q[i] q2 <- q[i+1] psclass <- psclass+i*as.numeric(distance=q1) } ## No subclass for discarded or unmatched units psclass[in.sample==0] <- NA psclass[!matched] <- NA if(verbose){cat("Done\n")} res <- list(subclass = psclass, q.cut = q, weights = weights.subclass(psclass, treat)) #warning for discrete data unique.classes <- unique(psclass) unique.classes <- unique.classes[!is.na (unique.classes)] if(length(unique.classes)!=subclass){ warning("Due to discreteness in data, fewer subclasses generated",call.=F) } class(res) <- c("matchit.subclass", "matchit") return(res) } MatchIt/R/matchit2optimal.R0000644000176200001440000000355012162545334015240 0ustar liggesusersmatchit2optimal <- function(treat, X, data, distance, discarded, is.full.mahalanobis, ratio = 1, verbose=FALSE, ...) { #if (!("optmatch" %in% .packages(all = TRUE))) # install.packages("optmatch") require(optmatch) if(verbose) cat("Optimal matching... \n") ## optimal matching for undiscarded units ttt <- treat[!discarded] n0 <- length(ttt[ttt==0]) n1 <- length(ttt[ttt==1]) d1 <- distance[ttt==1] d0 <- distance[ttt==0] d <- matrix(0, ncol=n0, nrow=n1) tlabels <- rownames(d) <- names(ttt[ttt==1]) clabels <- colnames(d) <- names(ttt[ttt==0]) for (i in 1:n1) d[i,] <- abs(d1[i]-d0) full <- fullmatch(d, min.controls = ratio, max.controls = ratio, omit.fraction = (n0-ratio*n1)/n0, ...) psclass <- full[pmatch(names(ttt), names(full))] psclass <- as.numeric(as.factor(psclass)) names(psclass) <- names(ttt) mm <- matrix(0, nrow = n1, ncol = ratio, dimnames = list(tlabels, 1:ratio)) for (i in 1:n1) mm[i,] <- names(which(psclass[tlabels[i]] == psclass[-pmatch(tlabels[i], names(psclass))])) if (any(discarded)) { ## add psclass = NA for discarded units tmp <- rep(NA, sum(discarded)) names(tmp) <- names(treat[discarded]) psclass <- c(psclass, tmp)[names(treat)] ## add match.matrix = NA for discarded units tdisc <- discarded[treat==1] if (any(tdisc)) { tmp <- matrix(NA, nrow = sum(tdisc), ncol= ratio, dimnames = list(names(treat[treat==1 & discarded]), 1:ratio)) mm <- as.matrix(rbind(mm, tmp)[names(treat[treat==1]),]) } } ## calculate weights and return the results res <- list(match.matrix = mm, subclass = psclass, weights = weights.matrix(mm, treat, discarded)) class(res) <- "matchit" return(res) } MatchIt/R/matchit2nearest.R0000644000176200001440000002371211500472177015235 0ustar liggesusersmatchit2nearest <- function(treat, X, data, distance, discarded, ratio=1, replace = FALSE, m.order = "largest", caliper = 0, calclosest = FALSE, mahvars = NULL, exact = NULL, subclass=NULL, verbose=FALSE, sub.by=NULL, is.full.mahalanobis,...){ if(verbose) cat("Nearest neighbor matching... \n") #replace if(!(identical(replace,TRUE) | identical(replace,FALSE))){ warning("replace=",replace," is invalid; used replace=FALSE instead",call.=FALSE);replace=FALSE} #m.order if(!(identical(m.order,"largest") | identical(m.order,"smallest") | identical(m.order,"random"))){ warning("m.order=",m.order," is invalid; used m.order='largest' instead",call.=FALSE);m.order="largest"} #ratio ratio <- round(ratio) if(!is.numeric(ratio) | ratio[1]<1 | !identical(round(length(ratio)),1)){ warning("ratio=",ratio," is invalid; used ratio=1 instead",call.=FALSE);ratio=1} #caliper if(!is.vector(caliper) | !identical(round(length(caliper)),1)){ warning("caliper=",caliper," is invalid; Caliper matching not done",call.=FALSE);caliper=0} if(caliper<0){ warning("caliper=",caliper," is less than 0; Caliper matching not done",call.=FALSE);caliper=0} #calclosest if(!(identical(calclosest,TRUE)| identical(calclosest,FALSE))){ warning("calclosest=",calclosest," is invalid; used calclosest=FALSE instead",call.=FALSE) calclosest=FALSE} #mahvars & caliper if (!is.null(mahvars) & caliper[1]==0){ warning("No caliper size specified for Mahalanobis matching. Caliper=.25 used.",call. = FALSE);caliper=.25} #when mahalanobis distance is used for all covars if(is.full.mahalanobis){ mahvars <- X Sigma <- var(X) ## Note: caliper irrelevant, but triggers mahalanobis matching caliper <- .25 ## no subclass with full mahalanobis if(!is.null(subclass)){ warning("No subclassification with pure Mahalanobis distance.",call. = FALSE) subclass <- NULL } } # Sample sizes, labels n <- length(treat) n0 <- length(treat[treat==0]) n1 <- length(treat[treat==1]) d1 <- distance[treat==1] d0 <- distance[treat==0] if(is.null(names(treat))) names(treat) <- 1:n labels <- names(treat) tlabels <- names(treat[treat==1]) clabels <- names(treat[treat==0]) in.sample <- !discarded names(in.sample) <- labels ## 10/1/07: Warning for if fewer control than ratio*treated and matching without replacement if (n0 < ratio*n1 & replace==FALSE) { if (ratio > 1) warning(paste("Not enough control units for ", ratio, " matches for each treated unit when matching without replacement. Not all treated units will receive", ratio, "matches")) else warning(paste("Fewer control than treated units and matching without replacement. Not all treated units will receive a match. Treated units will be matched in the order specified by m.order:", m.order)) } ## Generating match matrix match.matrix <- matrix(0, nrow=n1, ncol=ratio, dimnames=list(tlabels, 1:ratio)) ## Vectors of whether unit has been matched: ## = 0 if not matched (unit # of match if matched) ## = -1 if can't be matched (if in.sample=0) matchedc <- rep(0,length(d0)) names(matchedc) <- clabels ## These are the units that are ineligible because of discard ## (in.sample==0) matchedc[in.sample[clabels]==0] <- -1 match.matrix[in.sample[tlabels]==0,] <- -1 matchedt <- match.matrix[,1] names(matchedt) <- tlabels ## total number of matches (including ratios) = ratio * n1 tr <- length(match.matrix[match.matrix!=-1]) r <- 1 ## Caliper for matching (=0 if caliper matching not done) sd.cal <- caliper*sqrt(var(distance[in.sample==1])) ## Var-covar matrix for Mahalanobis (currently set for full sample) if (!is.null(mahvars) & !is.full.mahalanobis) { if(!sum(mahvars%in%names(data))==length(mahvars)) { warning("Mahvars not contained in data. Mahalanobis matching not done.",call.=FALSE) mahvars=NULL } else { ww <- mahvars%in%dimnames(X)[[2]] nw <- length(mahvars) mahvars <- data[,mahvars,drop=F] Sigma <- var(mahvars) if(sum(ww)!=nw){ X <- cbind(X,mahvars[!ww]) } mahvars <- as.matrix(mahvars) } } ## Now for exact matching within nearest neighbor ## exact should not equal T for this type of matching--that would get sent to matchit2exact if (!is.null(exact)){ if(!sum(exact%in%names(data))==length(exact)) { warning("Exact variables not contained in data. Exact matching not done.",call.=FALSE) exact=NULL } else { ww <- exact%in%dimnames(X)[[2]] nw <- length(exact) exact <- data[,exact,drop=F] if(sum(ww)!=nw){ X <- cbind(X,exact[!ww]) } } } ## Looping through nearest neighbour matching for all treatment units ## Only do matching for units with in.sample==1 (matched!=-1) if(verbose){ trseq <- floor(seq(tr/10,tr,tr/10)) cat("Matching Treated: ") } for(i in 1:tr){ ## Make new matchedc column to be used for exact matching ## Will only be 0 (eligible for matching) if it's an exact match if(verbose) {if(i%in%trseq){cat(10*which(trseq==i),"%...",sep="")}} # a counter matchedc2 <- matchedc ##in cases there's no replacement and all controls have been used up if(!0%in%matchedc2){ match.matrix[match.matrix[,r]==0 & !is.na(match.matrix[,r]),r] <- NA if(r1){itert <- sample(itert,1)} ## Calculating all the absolute deviations in propensity scores ## Calculate only for those eligible to be matched (matchedc==0) ## this first if statement only applies to replacement ratio ## matching, so that each treatment unit is matched to a different ## control unit than from the previous round ## match number = NA if no units within caliper ## Set things up for exact matching ## Make matchedc2==-2 if it isn't an exact match ## There might be a more efficient way to do this, but I couldn't figure ## out another way to compare a vector with the matrix if (!is.null(exact)) { for (k in 1:dim(exact)[2]) matchedc2[exact[itert,k]!=exact[clabels,k]] <- -2 } ## Need to add a check in case there aren't any eligible matches left... if(replace & r!=1) { if (sum(!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0)==0) { deviation <- NULL mindev <- NA } else deviation <- abs(d0[!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0]-iterd1) } else { if (sum(matchedc2==0)==0) { deviation <- NULL mindev <- NA } else deviation <- abs(d0[matchedc2==0]-iterd1) } if (caliper!=0 & (!is.null(deviation))) { if(replace & r!=1) pool <- clabels[!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0][deviation <= sd.cal] else pool <- clabels[matchedc2==0][deviation <= sd.cal] if(length(pool)==0) { if (calclosest==FALSE) mindev <- NA else { if (replace & r!= 1){ mindev <- clabels[!clabels%in%match.matrix[itert,(1:r-1)]][min(deviation)==deviation] } else{mindev <- clabels[matchedc2==0][min(deviation)==deviation]} } } else if (length(pool)==1) mindev <- pool[1] else if (is.null(mahvars)) mindev <- sample(pool, 1) else { ## This has the important vars for the C's within the caliper poolvarsC <- mahvars[pool,,drop=F] ## Sigma is the full group var/covar matrix of Mahalvars mahal <- mahalanobis(poolvarsC, mahvars[itert,],Sigma) mindev <- pool[mahal==min(mahal)] } } else if(!is.null(deviation)) { if (replace & r!=1){ mindev <- clabels[!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0][min(deviation)==deviation] } else {mindev <- clabels[matchedc2==0][min(deviation)==deviation]} } ## Resolving ties in minimum deviation by random draw if(length(mindev)>1){goodmatch <- sample(mindev,1)} else goodmatch <- mindev ## Storing which treatment unit has been matched to control, and ## vice versa matchedt[itert==tlabels] <- goodmatch matchedc[goodmatch==clabels] <- itert ## instead of the in.sample, we now have an index with dimensions n1 by # of ## matches (ratio) match.matrix[which(itert==tlabels),r] <- goodmatch ## If matching with replacement, set matchedc back to 0 so it can be reused if (replace) matchedc[goodmatch==clabels] <- 0 } if(verbose){cat("Done\n")} x <- as.matrix(match.matrix) x[x==-1] <- NA ## Calculate weights and return the results res <- list(match.matrix = match.matrix, weights = weights.matrix(match.matrix, treat, discarded), X=X) ## Subclassifying if(!is.null(subclass)){ if(is.null(sub.by)) sub.by="treat" psres <- matchit2subclass(treat,X,data,distance,discarded, match.matrix=match.matrix, subclass=subclass, verbose=verbose, sub.by=sub.by, ...) res$subclass <- psres$subclass res$q.cut <- psres$q.cut class(res) <- c("matchit.subclass", "matchit") } else{ class(res) <- "matchit" } return(res) } MatchIt/R/matchit2genetic.R0000644000176200001440000000272712162545334015216 0ustar liggesusersmatchit2genetic <- function(treat, X, data, distance, discarded, is.full.mahalanobis, ratio = 1, verbose = FALSE, ...) { #if (!("rgenoud" %in% .packages(all = TRUE))) # install.packages("rgenoud") #require(rgenoud) #if (!("Matching" %in% .packages(all = TRUE))) # install.packages("Matching") require(Matching) if (verbose) cat("Genetic matching... \n") tt <- treat[!discarded] n <- length(tt) n1 <- length(tt[tt==1]) xx <- X[!discarded,] dd <- distance[!discarded] tind <- (1:n)[tt==1] cind <- (1:n)[tt==0] labels <- names(tt) tlabels <- names(tt[tt==1]) clabels <- names(tt[tt==0]) out <- GenMatch(tt, cbind(dd, xx), M = ratio, ...)$matches ## ratio matching does not seem to work with GenMatch mm <- matrix(0, nrow = n1, ncol = max(table(out[,1])), dimnames = list(tlabels, 1:max(table(out[,1])))) for (i in 1:n1) { tmp <- labels[c(out[out[,1]==tind[i],2:(ratio+1)])] if (length(tmp) < ncol(mm)) tmp <- c(tmp, rep(NA, ncol(mm)-length(tmp))) mm[i,] <- tmp } if (any(discarded)) { tdisc <- discarded[treat==1] tmp <- matrix(NA, nrow = sum(tdisc), ncol = ncol(mm), dimnames = list(names(treat[treat == 1 & discarded]), 1:ncol(mm))) mm <- as.matrix(rbind(mm, tmp)[names(treat[treat==1]),]) } res <- list(match.matrix = mm, weights = weights.matrix(mm, treat, discarded)) class(res) <- "matchit" return(res) } MatchIt/R/matchit2full.R0000644000176200001440000000221712162545334014534 0ustar liggesusersmatchit2full <- function(treat, X, data, distance, discarded, is.full.mahalanobis, verbose=FALSE, ...) { #if (!("optmatch" %in% .packages(all = TRUE))) # install.packages("optmatch") require(optmatch) if(verbose) cat("Full matching... \n") ## full matching for undiscarded units ttt <- treat[!discarded] ddd <- distance[!discarded] n0 <- length(ttt[ttt==0]) n1 <- length(ttt[ttt==1]) d1 <- ddd[ttt==1] d0 <- ddd[ttt==0] d <- matrix(0, ncol=n0, nrow=n1) rownames(d) <- names(ttt[ttt==1]) colnames(d) <- names(ttt[ttt==0]) for (i in 1:n1) d[i,] <- abs(d1[i]-d0) full <- fullmatch(d, ...) psclass <- full[pmatch(names(ttt), names(full))] psclass <- as.numeric(as.factor(psclass)) names(psclass) <- names(ttt) ## add psclass = NA for discarded units if (any(discarded)) { tmp <- rep(NA, sum(discarded)) names(tmp) <- names(treat[discarded]) psclass <- c(psclass, tmp)[names(treat)] } ## calculate weights and return the results res <- list(subclass = psclass, weights = weights.subclass(psclass, treat)) class(res) <- c("matchit.full", "matchit") return(res) } MatchIt/R/matchit2exact.R0000644000176200001440000000111211500472177014666 0ustar liggesusersmatchit2exact <- function(treat, X, data, distance, discarded, is.full.mahalanobis, verbose=FALSE, ...){ if(verbose) cat("Exact matching... \n") n <- length(treat) xx <- apply(X, 1, function(x) paste(x, collapse = "\r")) xx1 <- xx[treat==1] xx0 <- xx[treat==0] cc <- unique(xx1) cc <- cc[cc%in%xx0] ncc <- length(cc) psclass <- rep(NA,n) names(psclass) <- names(treat) for(i in 1:ncc) psclass[xx==cc[i]] <- i res <- list(subclass = psclass, weights = weights.subclass(psclass, treat)) class(res) <- c("matchit.exact", "matchit") return(res) } MatchIt/R/matchit2cem.R0000644000176200001440000000346712162545334014346 0ustar liggesusers# matchit2cem - matchit wrapper for cem matching algorithm # # 06/10/2008 - m.blackwell # # this function takes inputs from matchit() and returns the # strata for each observation in the subclass entry and the # weight for each observation in the weight entry. No match # matrix is returned since matches are not unique within # strata. # matchit2cem <- function(treat, X, data, distance, discarded, is.full.mahalanobis, ratio = 1, verbose = FALSE, k2k.method=NULL, ...) { #if (!("cem" %in% .packages(all = TRUE))) # install.packages("cem",repos="http://gking.harvard.edu/") require(cem) if (verbose) cat("Coarsened exact matching...\n") n <- length(treat) # cem takes the data all together and wants the treatment specified # with the column name of the data frame. Here we massage the matchit # inputs to this format. Note that X has its proper columnames, but # treat does not have the original column name. cem.data <- as.data.frame(cbind(treat,X)) mat <- cem(treatment="treat",data=cem.data,verbose=as.integer(verbose)+1, method=k2k.method,...) # here we create a column vector where the matched entry get its stratum # and the unmatched entry gets an NA. strat <- rep(NA,n) names(strat) <- names(treat) strat[mat$matched] <- mat$strata[mat$matched] # here we just add the names onto the wieght from the cem output wh <- mat$w names(wh) <- names(treat) # weighting functions in matchit error-out on these conditions, # so we should too. if (sum(wh)==0) stop("No units were matched") else if (sum(wh[treat==1])==0) stop("No treated units were matched") else if (sum(wh[treat==0])==0) stop("No control units were matched") res <- list(subclass = strat, weights = mat$w) class(res) <- "matchit" return(res) } MatchIt/R/matchit.qqplot.R0000644000176200001440000000666411221036205015103 0ustar liggesusersmatchit.qqplot <- function(x,discrete.cutoff, which.subclass=NULL, numdraws=5000, interactive = T, which.xs = NULL,...){ X <- x$X ## Fix X matrix so that it doesn't have any factors varnames <- colnames(X) for(var in varnames) { if(is.factor(X[,var])) { tempX <- X[,!colnames(X)%in%c(var)] form<-formula(substitute(~dummy-1,list(dummy=as.name(var)))) X <- cbind(tempX, model.matrix(form, X)) } } covariates <- X if(!is.null(which.xs)){ if(sum(which.xs%in%dimnames(covariates)[[2]])!=length(which.xs)){ stop("which.xs is incorrectly specified") } covariates <- covariates[,which.xs,drop=F] } treat <- x$treat matched <- x$weights!=0 ratio <- x$call$ratio if(is.null(ratio)){ratio <- 1} ## For full or ratio matching, sample numdraws observations using the weights if(identical(x$call$method,"full") | (ratio!=1)) { t.plot <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=x$weights[treat==1]) c.plot <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=x$weights[treat==0]) m.covariates <- x$X[c(t.plot, c.plot),] m.treat <- x$treat[c(t.plot, c.plot)] } else { m.covariates <- covariates[matched,,drop=F] m.treat <- treat[matched] } if(!is.null(which.subclass)){ subclass <- x$subclass sub.index <- subclass==which.subclass & !is.na(subclass) sub.covariates <- covariates[sub.index,,drop=F] sub.treat <- treat[sub.index] sub.matched <- matched[sub.index] ## Matched units in each subclass m.covariates <- sub.covariates[sub.matched,,drop=F] m.treat <- sub.treat[sub.matched] ## Compare to full sample--reset covariates and treat to full data set # covariates <- x$X # treat <- x$treat } nn <- dimnames(covariates)[[2]] nc <- length(nn) covariates <- data.matrix(covariates) # oma <- c(4, 4, 6, 4) oma <- c(2.25,0,3.75,1.5) opar <- par(mfrow = c(3, 3), mar = rep.int(1/2, 4), oma = oma) on.exit(par(opar)) # par(oma=c(2.25,0,3.75,1.5)) for (i in 1:nc){ xi <- covariates[,i] m.xi <- m.covariates[,i] ni <- nn[i] plot(xi,type="n",axes=F) if(((i-1)%%3)==0){ htext <- "QQ Plots" if(!is.null(which.subclass)){ htext <- paste(htext,paste(" (Subclass ",which.subclass,")",sep=""),sep="") } mtext(htext, 3, 2, TRUE, 0.5, cex=1.1,font=2) mtext("All", 3, .25, TRUE, 0.5, cex=1,font = 1) mtext("Matched", 3, .25, TRUE, 0.83, cex=1,font = 1) mtext("Control Units", 1, 0, TRUE, 2/3, cex=1,font = 1) mtext("Treated Units", 4, 0, TRUE, 0.5, cex=1,font = 1) } par(usr = c(0, 1, 0, 1)) l.wid <- strwidth(nn, "user") cex.labels <- max(0.75, min(1.45, 0.85/max(l.wid))) text(0.5,0.5,ni,cex=cex.labels) if(length(table(xi))<=discrete.cutoff){ xi <- jitter(xi) m.xi <- jitter(m.xi) } rr <- range(xi) eqqplot(xi[treat==0],xi[treat==1], xlim=rr,ylim=rr,axes=F,ylab="",xlab="",...) abline(a=0,b=1) abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2) abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2) axis(2) box() eqqplot(m.xi[m.treat==0],m.xi[m.treat==1],xlim=rr,ylim=rr,axes=F,ylab="",xlab="",...) abline(a=0,b=1) abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2) abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2) box() if(interactive){ par(ask=T) } else { par(ask=F) } } par(ask=F) } MatchIt/R/matchit.R0000644000176200001440000000733511651316550013573 0ustar liggesusersmatchit <- function(formula, data, method = "nearest", distance = "logit", distance.options=list(), discard = "none", reestimate = FALSE, ...) { #Checking input format #data input mcall <- match.call() if(is.null(data)) stop("Dataframe must be specified",call.=FALSE) if(!is.data.frame(data)){ stop("Data must be a dataframe",call.=FALSE)} if(sum(is.na(data))>0) stop("Missing values exist in the data") # list-wise deletion # allvars <- all.vars(mcall) # varsindata <- colnames(data)[colnames(data) %in% all.vars(mcall)] # data <- na.omit(subset(data, select = varsindata)) ## 7/13/06: Convert character variables to factors as necessary ischar <- rep(0, dim(data)[2]) for (i in 1:dim(data)[2]) if(is.character(data[,i])) data[,i] <- as.factor(data[,i]) ## check inputs if (!is.numeric(distance)) { fn1 <- paste("distance2", distance, sep = "") if (!exists(fn1)) stop(distance, "not supported.") } if (is.numeric(distance)) { fn1 <- "distance2user" } fn2 <- paste("matchit2", method, sep = "") if (!exists(fn2)) stop(method, "not supported.") ## obtain T and X tryerror <- try(model.frame(formula), TRUE) if (distance %in% c("GAMlogit", "GAMprobit", "GAMcloglog", "GAMlog", "GAMcauchit")) { library(mgcv) tt <- terms(mgcv::interpret.gam(formula)$fake.formula) } else { tt <- terms(formula) } attr(tt, "intercept") <- 0 mf <- model.frame(tt, data) treat <- model.response(mf) X <- model.matrix(tt, data=mf) ## estimate the distance measure if (method == "exact") { distance <- out1 <- discarded <- NULL if (!is.null(distance)) warning("distance is set to `NULL' when exact matching is used.") } else if (is.numeric(distance)){ out1 <- NULL discarded <- discard(treat, distance, discard, X) } else { if (is.null(distance.options$formula)) distance.options$formula <- formula if (is.null(distance.options$data)) distance.options$data <- data out1 <- do.call(fn1, distance.options) discarded <- discard(treat, out1$distance, discard, X) if (reestimate) { distance.options$data <- data[!discarded,] distance.options$weights <- distance.options$weights[!discarded] tmp <- out1 out1 <- do.call(fn1, distance.options) tmp$distance[!discarded] <- out1$distance out1$distance <- tmp$distance } distance <- out1$distance } ## full mahalanobis matching if(fn1=="distance2mahalanobis"){ is.full.mahalanobis <- TRUE } else {is.full.mahalanobis <- FALSE} ## matching! out2 <- do.call(fn2, list(treat, X, data, distance=distance, discarded, is.full.mahalanobis=is.full.mahalanobis, ...)) ## no distance for full mahalanobis matching if(fn1=="distance2mahalanobis"){ distance[1:length(distance)] <- NA class(out2) <- c("matchit.mahalanobis","matchit") } ## putting all the results together out2$call <- mcall out2$model <- out1$model out2$formula <- formula out2$treat <- treat if (is.null(out2$X)){ out2$X <- X } out2$distance <- distance out2$discarded <- discarded ## basic summary nn <- matrix(0, ncol=2, nrow=4) nn[1,] <- c(sum(out2$treat==0), sum(out2$treat==1)) nn[2,] <- c(sum(out2$treat==0 & out2$weights>0), sum(out2$treat==1 & out2$weights>0)) nn[3,] <- c(sum(out2$treat==0 & out2$weights==0 & out2$discarded==0), sum(out2$treat==1 & out2$weights==0 & out2$discarded==0)) nn[4,] <- c(sum(out2$treat==0 & out2$weights==0 & out2$discarded==1), sum(out2$treat==1 & out2$weights==0 & out2$discarded==1)) dimnames(nn) <- list(c("All","Matched","Unmatched","Discarded"), c("Control","Treated")) out2$nn <- nn return(out2) } MatchIt/R/match.qoi.R0000644000176200001440000000561510701546462014026 0ustar liggesusers## Function to calculate summary stats qoi <- function(xx,tt,ww, t.plot=NULL, c.plot=NULL, sds=NULL, standardize = FALSE, std=F){ weighted.var <- function(x, w) { sum(w * (x - weighted.mean(x,w))^2)/(sum(w) - 1)} xsum <- matrix(NA,2,7) xsum <- as.data.frame(xsum) row.names(xsum) <- c("Full","Matched") if (standardize) names(xsum) <- c("Means Treated","Means Control", "SD Control", "Std. Mean Diff.", "eCDF Med", "eCDF Mean", "eCDF Max") else names(xsum) <- c("Means Treated","Means Control", "SD Control", "Mean Diff", "eQQ Med", "eQQ Mean", "eQQ Max") x1 <- xx[tt==1] x0 <- xx[tt==0] ww1 <- ww[tt==1] ww0 <- ww[tt==0] xsum[1,1] <- mean(x1,na.rm=T) xsum[1,2] <- mean(x0,na.rm=T) xsum[1,3] <- sd(x0,na.rm=T) X.t.m <- xx[tt==1][ww1>0] X.c.m <- xx[tt==0][ww0>0] xsum[2,1] <- weighted.mean(X.t.m, ww1[ww1>0]) xsum[2,2] <- weighted.mean(X.c.m, ww0[ww0>0]) xsum[2,3] <- sqrt(weighted.var(X.c.m, ww0[ww0>0])) if(!(sum(tt==1)<2|(sum(tt==0)<2))){ xsd <- sd(x1,na.rm=T) qqall <- qqsum(x1,x0,standardize=standardize) xsum[1,5:7] <- c(qqall$meddiff,qqall$meandiff,qqall$maxdiff) if (standardize) if (!is.null(sds)) xsum[1,4] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/sds else xsum[1,4] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/xsd else xsum[1,4] <- mean(x1,na.rm=T)-mean(x0,na.rm=T) if(!is.null(t.plot)) qqmat <- qqsum(xx[t.plot],xx[c.plot],standardize=standardize) else qqmat <- qqsum(x1[ww1>0],x0[ww0>0],standardize=standardize) xsum[2,5:7] <- c(qqmat$meddiff,qqmat$meandiff,qqmat$maxdiff) if (standardize) if (!is.null(sds)) xsum[2,4] <- (xsum[2,1]-xsum[2,2])/sds else xsum[2,4] <- (xsum[2,1]-xsum[2,2])/xsd else xsum[2,4] <- xsum[2,1]-xsum[2,2] } if(!std){ xsum <- xsum[,c(1:2,4:7)] } xsum } ## By subclass qoi.by.sub <- function(xx,tt,ww,qq,standardize=FALSE){ qbins <- max(qq,na.rm=TRUE) q.table <- matrix(0,6,qbins) qn <- matrix(0,3,qbins) matched <- ww!=0 for (i in 1:qbins) { qi <- qq[matched]==i & (!is.na(qq[matched])) qx <- xx[matched][qi] qt <- tt[matched][qi] qw <- as.numeric(ww[matched][qi]!=0) if(sum(qt==1)<2|(sum(qt==0)<2)){ if(sum(qt==1)<2) warning("Not enough treatment units in subclass ",i,call.=FALSE) else if(sum(qt==0)<2) warning("Not enough control units in subclass ",i,call.=FALSE) } qoi.i <- qoi(qx,qt,qw, sds=sd(xx[tt==1],na.rm=T), standardize=standardize) q.table[,i] <- as.numeric(qoi.i[1,]) qn[,i] <- c(sum(qt),sum(qt==0),length(qt)) } q.table <- as.data.frame(q.table) qn <- as.data.frame(qn) names(q.table) <- names(qn) <- paste("Subclass",1:qbins) row.names(q.table) <- names(qoi.i) row.names(qn) <- c("Treated","Control","Total") list(q.table=q.table,qn=qn) } MatchIt/R/match.data.R0000644000176200001440000000251111430774543014142 0ustar liggesusersmatch.data <- function(object, group = "all", distance = "distance", weights = "weights", subclass = "subclass") { if (!is.null(object$model)) { env <- attributes(terms(object$model))$.Environment } else { env <- parent.frame() } data <- eval(object$call$data, envir = env) treat <- object$treat wt <- object$weights vars <- names(data) if (distance %in% vars) stop("invalid input for distance. choose a different name.") else if (!is.null(object$distance)) { dta <- data.frame(cbind(data, object$distance)) names(dta) <- c(names(data), distance) data <- dta } if (weights %in% vars) stop("invalid input for weights. choose a different name.") else if (!is.null(object$weights)){ dta <- data.frame(cbind(data, object$weights)) names(dta) <- c(names(data), weights) data <- dta } if (subclass %in% vars) stop("invalid input for subclass. choose a different name.") else if (!is.null(object$subclass)){ dta <- data.frame(cbind(data, object$subclass)) names(dta) <- c(names(data), subclass) data <- dta } if (group == "all") return(data[wt > 0,]) else if (group == "treat") return(data[wt > 0 & treat == 1,]) else if (group == "control") return(data[wt > 0 & treat == 0,]) else stop("error: invalid input for group.") } MatchIt/R/jitter.pscore.R0000644000176200001440000000351411221036205014715 0ustar liggesusersjitter.pscore <- function(x, interactive,pch=1,cex=NULL,...){ treat <- x$treat pscore <- x$distance weights <- x$weights matched <- weights!=0 q.cut <- x$q.cut jitp <- jitter(rep(1,length(treat)),factor=6)+(treat==1)*(weights==0)-(treat==0)-(weights==0)*(treat==0) cwt <- sqrt(weights) minp <- min(pscore,na.rm=T) maxp <- max(pscore,na.rm=T) plot(pscore,xlim=c(minp,maxp+0.1*(maxp-minp)),ylim=c(-1.5,2.5), type="n",ylab="",xlab="Propensity Score", axes=F,main="Distribution of Propensity Scores",...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} if(is.null(cex)){ points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0], pch=pch,cex=cwt[treat==1&weights!=0],...) points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0], pch=pch,cex=cwt[treat==0&weights!=0],...) points(pscore[treat==1&weights==0],jitp[treat==1&weights==0], pch=pch,cex=1,...) points(pscore[treat==0&weights==0],jitp[treat==0&weights==0], pch=pch,cex=1,...) }else{ points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0], pch=pch,cex=cex,...) points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0], pch=pch,cex=cex,...) points(pscore[treat==1&weights==0],jitp[treat==1&weights==0], pch=pch,cex=cex,...) points(pscore[treat==0&weights==0],jitp[treat==0&weights==0], pch=pch,cex=cex,...) } axis(1) text(sum(range(na.omit(pscore)))/2,2.5,"Unmatched Treatment Units") text(sum(range(na.omit(pscore)))/2,1.5,"Matched Treatment Units") text(sum(range(na.omit(pscore)))/2,0.5,"Matched Control Units") text(sum(range(na.omit(pscore)))/2,-0.5,"Unmatched Control Units") box() if(interactive==TRUE) { print("To identify the units, use first mouse button; to stop, use second.") identify(pscore,jitp,names(treat),atpen=T) } } MatchIt/R/hist.pscore.R0000644000176200001440000000431211221036205014360 0ustar liggesusershist.pscore <- function(x, numdraws=5000, xlab="Propensity Score", main=NULL, freq=F, xlim = NULL,...){ treat <- x$treat pscore <- x$distance weights <- x$weights matched <- weights!=0 q.cut <- x$q.cut cwt <- sqrt(weights) ratio <- x$call$ratio if(is.null(ratio)){ratio <- 1} ## For full or ratio matching, sample numdraws observations using the weights if(identical(x$call$method,"full") | (ratio!=1)) { pscore.treated.matched <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=x$weights[treat==1]) pscore.treated.matched <- pscore[pscore.treated.matched] pscore.control.matched <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=x$weights[treat==0]) pscore.control.matched <- pscore[pscore.control.matched] } else { pscore.treated.matched <- pscore[treat==1 & weights!=0] pscore.control.matched <- pscore[treat==0 & weights!=0] } par(mfrow=c(2,2)) if(!is.null(xlim)){warning("xlim may not be user specified. xlim returned to default.")} xlim <- range(na.omit(pscore)) if(is.null(main)){ hist(pscore[treat==1],xlim=xlim, xlab=xlab, freq=freq, main="Raw Treated", ...) hist(pscore.treated.matched,xlim=xlim, xlab=xlab, freq=freq, main="Matched Treated",...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} hist(pscore[treat==0],xlim=xlim, xlab=xlab, freq=freq, main="Raw Control",...) hist(pscore.control.matched,xlim=xlim, xlab=xlab, freq=freq, main="Matched Control",...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} }else{ hist(pscore[treat==1],xlim=xlim, xlab=xlab, freq=freq, main=main, ...) hist(pscore.treated.matched,xlim=xlim, xlab=xlab, freq=freq, main=main,...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} hist(pscore[treat==0],xlim=xlim, xlab=xlab, freq=freq, main=main,...) hist(pscore.control.matched,xlim=xlim, xlab=xlab, freq=freq, main=main,...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} } } MatchIt/R/help.matchit.R0000644000176200001440000000400210304731730014501 0ustar liggesusershelp.matchit <- function (object=NULL) { under.unix <- !(version$os == "Microsoft Windows" || version$os == "Win32" || version$os == "mingw32") sys <- function(command, text = NULL) { cmd <- if (length(text)) paste(command, text) else command if (under.unix) system(cmd) else shell(cmd, wait = TRUE) } browser <- .Options$help.browser if (!length(browser)) browser <- .Options$browser if (!length(browser)) browser <- getOption("browser") url <- NULL if (is.null(object)) url <- c("http://gking.harvard.edu/matchit") if (!is.null(object)) { if (object == "matchit") url <- c("http://gking.harvard.edu/matchit/docs/Reference_Manual.html") if (object == "exact") url <- c("http://gking.harvard.edu/matchit/docs/Exact_Matching2.html") if (object == "subclass") url <- c("http://gking.harvard.edu/matchit/docs/Subclassification2.html") if (object == "nearest") url <- c("http://gking.harvard.edu/matchit/docs/Nearest_Neighbor_Match2.html") if (object == "optimal") url <- c("http://gking.harvard.edu/matchit/docs/Optimal_Matching2.html") if (object == "full") url <- c("http://gking.harvard.edu/matchit/docs/Full_Matching2.html") if (object == "match.data") url <- c("http://gking.harvard.edu/matchit/docs/_TT_match_data_TT.html") if (object == "summary") url <- c("http://gking.harvard.edu/matchit/docs/_TT_summary_TT.html") if (object == "plot") url <- c("http://gking.harvard.edu/matchit/docs/_TT_plot_TT.html") } if (is.null(url)) { cat("Error:", object, "currently not documented in help.matchit. \n Please check http://gking.harvard.edu/matchit. \n", sep = " ") url <- c("http://gking.harvard.edu/matchit") } if (under.unix) { sys(paste(browser, url, "&")) invisible() } if (!under.unix) { browseURL(url, browser = browser) invisible("") } } MatchIt/R/eqqplot.R0000644000176200001440000000102610323462506013614 0ustar liggesuserseqqplot <- function(x, y, plot.it = TRUE, xlab = deparse(substitute(x)), ylab = deparse(substitute(y)), ...) { ## empirical quantile-quantile plot; hacked from qqplot() in stats. sx <- sort(x) sy <- sort(y) lenx <- length(sx) leny <- length(sy) if (leny < lenx) sx <- approx(1:lenx, sx, n = leny, method = "constant")$y if (leny > lenx) sy <- approx(1:leny, sy, n = lenx, method = "constant")$y if (plot.it) plot(sx, sy, xlab = xlab, ylab = ylab, ...) invisible(list(x = sx, y = sy)) } MatchIt/R/distance2rpart.R0000644000176200001440000000023510265332610015052 0ustar liggesusersdistance2rpart <- function(formula, data, ...) { require(rpart) res <- rpart(formula, data, ...) return(list(model = res, distance = predict(res))) } MatchIt/R/distance2nnet.R0000644000176200001440000000023110265332610014662 0ustar liggesusersdistance2nnet <- function(formula, data, ...) { require(nnet) res <- nnet(formula, data, ...) return(list(model = res, distance = fitted(res))) } MatchIt/R/distance2mahalanobis.R0000644000176200001440000000037411434402563016210 0ustar liggesusersdistance2mahalanobis <- function(formula, data, ...) { X <- model.matrix(formula, data) ## Placeholder where real work is done on a unit by unit basis distance <- rep(1, nrow(X)) return(list(model = NULL, distance = distance)) } MatchIt/R/distance2glm.R0000644000176200001440000000317510265332610014507 0ustar liggesusersdistance2logit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(logit), ...) return(list(model = res, distance = fitted(res))) } distance2linear.logit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(logit), ...) return(list(model = res, distance = predict(res))) } distance2probit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(probit), ...) return(list(model = res, distance = fitted(res))) } distance2linear.probit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(probit), ...) return(list(model = res, distance = predict(res))) } distance2cloglog <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cloglog), ...) return(list(model = res, distance = fitted(res))) } distance2linear.cloglog <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cloglog), ...) return(list(model = res, distance = predict(res))) } distance2log <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(log), ...) return(list(model = res, distance = fitted(res))) } distance2linear.log <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(log), ...) return(list(model = res, distance = predict(res))) } distance2cauchit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cauchit), ...) return(list(model = res, distance = fitted(res))) } distance2linearcauchit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cauchit), ...) return(list(model = res, distance = predict(res))) } MatchIt/R/distance2GAM.R0000644000176200001440000000161210265332610014326 0ustar liggesusersdistance2GAMlogit <- function(formula, data, ...) { require(mgcv) res <- gam(formula, data, family=binomial(logit), ...) return(list(model = res, distance = fitted(res))) } distance2GAMprobit <- function(formula, data, ...) { require(mgcv) res <- gam(formula, data, family=binomial(probit), ...) return(list(model = res, distance = fitted(res))) } distance2GAMcloglog <- function(formula, data, ...) { require(mgcv) res <- gam(formula, data, family=binomial(cloglog), ...) return(list(model = res, distance = fitted(res))) } distance2GAMlog <- function(formula, data, ...) { require(mgcv) res <- gam(formula, data, family=binomial(log), ...) return(list(model = res, distance = fitted(res))) } distance2GAMcauchit <- function(formula, data, ...) { require(mgcv) res <- gam(formula, data, family=binomial(cauchit), ...) return(list(model = res, distance = fitted(res))) } MatchIt/R/discard.R0000644000176200001440000000331612162545334013550 0ustar liggesusersdiscard <- function(treat, pscore, option, X) { n.obs <- length(treat) pmax0 <- max(pscore[treat==0]) pmax1 <- max(pscore[treat==1]) pmin0 <- min(pscore[treat==0]) pmin1 <- min(pscore[treat==1]) if (is.logical(option)) # user input return(option) else if (option == "none") # keep all units discarded <- rep(FALSE, n.obs) else if (option == "both") # discard units outside of common support discarded <- (pscore < max(pmin0, pmin1) | pscore > min(pmax0, pmax1)) else if (option == "control") # discard control units only discarded <- (pscore < pmin1 | pscore > pmax1) else if (option == "treat") # discard treated units only discarded <- (pscore < pmin0 | pscore > pmax0) else if (any(grep(option, c("hull.control", "hull.treat", "hull.both")))) { ## convext hull stuff # if (!("WhatIf" %in% .packages(all = TRUE))) # install.packages("WhatIf") # if (!("lpSolve" %in% .packages(all = TRUE))) # install.packages("lpSolve") require(WhatIf) # require(lpSolve) discarded <- rep(FALSE, n.obs) if (option == "hull.control"){ # discard units not in T convex hull wif <- whatif(cfact = X[treat==0,], data = X[treat==1,]) discarded[treat==0] <- !wif$in.hull } else if (option == "hull.treat") { wif <- whatif(cfact = X[treat==1,], data = X[treat==0,]) discarded[treat==1] <- !wif$in.hull } else if (option == "hull.both"){ # discard units not in T&C convex hull wif <- whatif(cfact = cbind(1-treat, X), data = cbind(treat, X)) discarded <- !wif$in.hull } else stop("invalid input for `discard'") } else stop("invalid input for `discard'") names(discarded) <- names(treat) return(discarded) } MatchIt/NAMESPACE0000644000176200001440000000100310700713530013010 0ustar liggesusersimport(MASS) export(matchit, help.matchit, match.data, user.prompt) S3method(print, matchit) S3method(print, matchit.exact) S3method(print, matchit.subclass) S3method(print, summary.matchit) S3method(print, summary.matchit.exact) S3method(print, summary.matchit.subclass) S3method(plot, matchit) S3method(plot, summary.matchit) S3method(plot, matchit.subclass) S3method(summary, matchit) S3method(summary, matchit.exact) S3method(summary, matchit.subclass) S3method(summary, matchit.full) MatchIt/DESCRIPTION0000644000176200001440000000163712163141375013323 0ustar liggesusersPackage: MatchIt Version: 2.4-21 Date: 2013-06-26 Title: MatchIt: Nonparametric Preprocessing for Parametric Casual Inference Author: Daniel Ho , Kosuke Imai , Gary King , Elizabeth Stuart Maintainer: Kosuke Imai Depends: R (>= 2.6), MASS Suggests: cem, optmatch, Matching, nnet, rpart, mgcv, WhatIf Description: MatchIt selects matched samples of the the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. LazyLoad: yes LazyData: yes License: GPL (>= 2) URL: http://gking.harvard.edu/matchit Packaged: 2013-06-27 21:07:24 UTC; kimai NeedsCompilation: no Repository: CRAN Date/Publication: 2013-06-28 00:55:57