fBasics/0000755000176200001440000000000014660613642011634 5ustar liggesusersfBasics/tests/0000755000176200001440000000000014650724111012767 5ustar liggesusersfBasics/tests/doRUnit.R0000644000176200001440000000151214650724111014475 0ustar liggesusers#### doRUnit.R --- Run RUnit tests ####------------------------------------------------------------------------ ### Origianlly follows Gregor Gojanc's example in CRAN package 'gdata' ### and the corresponding section in the R Wiki: ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit ### MM: Vastly changed: This should also be "runnable" for *installed* ## package which has no ./tests/ ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : if(require("RUnit", quietly=TRUE)) { ## --- Setup --- wd <- getwd() pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) library(package=pkg, character.only=TRUE) path <- system.file("unitTests", package = pkg) stopifnot(file.exists(path), file.info(path.expand(path))$isdir) 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implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # TIME SERIES DESCRIPTION: # tslag Lagged/leading vector/matrix of selected orders # .tslag1 Internal Function used by tslag ################################################################################ tslag <- function(x, k = 1, trim = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Creates a lagged or leading vector/matrix of selected order(s). # Arguments: # x - a vector of data, missing values (NA) are allowed. # k - the number of positions the new series is to lag # or to lead the input series. # trim - a logical flag, if TRUE, the missing values at the # beginning or end of the returned series will be trimmed. # The default value is FALSE. # Details: # With a positive value of "k" we get a lagged series and with # a negative value we get a leading series. # Examples: # tslag(rnorm(10), 2) # tslag(rnorm(10), -2:2) # tslag(rnorm(10), -2:2, trim = TRUE) # FUNCTION: # Bind: ans = NULL for ( i in k) { ans = cbind(ans, .tslag1(x, i)) } # Trim: if (trim) { indexes = (1:length(ans[,1]))[!is.na(apply(ans, 1, sum))] ans = ans[indexes, ] } # As Vector: if (length(k) == 1) ans = as.vector(ans) # Return Value: ans } # ------------------------------------------------------------------------------ .tslag1 <- function(x, k) { # A function implemented by Diethelm Wuertz # Description: # Internal Function used by function tslag. # FUNCTION: y = x if (k > 0) y = c(rep(NA, times = k), x[1:(length(x)-k)]) if (k < 0) y = c(x[(-k+1):length(x)], rep(NA, times = -k)) # Return Value: y } ################################################################################ fBasics/R/test-varianceTest.R0000644000176200001440000001711714324454750015574 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # varianceTest Performs variance tests on two samples # .varfTest F test for differences in variances # .bartlett2Test Bartlett's test for differences in variances # .fligner2Test Fligner-Killeen test for differences in variances ################################################################################ varianceTest <- function(x, y, method = c("varf", "bartlett", "fligner"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs Variance Tests # FUNCTION: # Test: method = match.arg(method) if (method == "varf") { ans = .varfTest(x, y, title = title, description = description) } if (method == "bartlett") { ans = .bartlett2Test(x, y, title = title, description = description) } if (method == "fligner") { ans = .fligner2Test(x, y, title = title, description = description) } # Return Value: ans } # ------------------------------------------------------------------------------ .varfTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs an F test to compare the variances of two samples # from normal populations. # Arguments: # x, y - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Notes: # A modified copy originally from R's ctest package Version 1.8.1 # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Estimate - Hypothesized Equal Variance: ratio = 1 DF.x = length(x) - 1 DF.y = length(y) - 1 VAR.x = var(x) VAR.y = var(y) ESTIMATE = VAR.x / VAR.y / ratio names(ESTIMATE) = "Ratio of Variances" test$estimate = ESTIMATE # Parameter: PARAMETER = c(ratio, DF.x, DF.y) names(PARAMETER) = c( "Hypothesized Ratio", "Numerator df", "Denumerator df") test$parameter = PARAMETER # Statistic: STATISTIC = ESTIMATE / ratio names(STATISTIC) = "F" test$statistic = STATISTIC # P Value: p = pf(STATISTIC, DF.x, DF.y) PVAL = c( two.sided = 2 * min(p, 1 - p), less = p, greater = 1 - p) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Confidence Interval: conf.level = 0.95 B = (1 - conf.level) / 2 two.sided = c(ESTIMATE/qf(1-B, DF.x, DF.y), ESTIMATE/qf(B, DF.x, DF.y)) less = c(0, ESTIMATE/qf(1-conf.level, DF.x, DF.y)) greater = c(ESTIMATE/qf(conf.level, DF.x, DF.y), Inf) CONF.INT = cbind( a = two.sided, b = less, c = greater) dimnames(CONF.INT)[[2]] = c( "Two-Sided", " Less", " Greater") test$conf.int = CONF.INT # Add: if(is.null(title)) title = "F Test of Variances" if(is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ .bartlett2Test <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Bartlett's test for differences in variances # Arguments: # x - a numeric vector of data values. # Note: # # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Settings: x = list(x = x, y = y) k = length(x) n = sapply(x, "length") - 1 v = sapply(x, "var") n.total = sum(n) v.total = sum(n * v) / n.total # Statistic: STATISTIC = ((n.total * log(v.total) - sum(n * log(v))) / (1 + (sum(1 / n) - 1 / n.total) / (3 * (k - 1)))) names(STATISTIC) = "Bartlett's Chi-squared" test$statistic = STATISTIC # P Value: PVAL = 1 - pchisq(STATISTIC, 1) names(PVAL) = "" test$p.value = PVAL # Add: if(is.null(title)) title = "Bartlett Test for Homogeneity of Variances" if(is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ .fligner2Test <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Fligner-Killeen's rank based test for homogeneity of variances # Arguments: # x - a numeric vector of data values. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Settings: x = list(x = x, y = y) k = length(x) l = sapply(x, "length") g = factor(rep(1 : k, l)) x = unlist(x) # Statistic: n = length(x) x = unlist(tapply(x, g, function(u) u - median(u))) a = qnorm((1 + rank(abs(x)) / (n + 1)) / 2) STATISTIC = sum(tapply(a, g, "sum")^2 / tapply(a, g, "length")) STATISTIC = (STATISTIC - n * mean(a)^2) / var(a) names(STATISTIC) = "FK:med chi-squared" test$statistic = STATISTIC # P Value: PVAL = 1 - pchisq(STATISTIC, 1) names(PVAL) = "" test$p.value = PVAL # Add: if(is.null(title)) title = "Fligner-Killeen Test for Homogeneity of Variances" if(is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/utils-getS4.R0000644000176200001440000001036314263246022014300 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR Description. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # getCall Extracts the call slot from a S4 object # getModel Extracts the model slot from a S4 object # getTitle Extracts the title slot from a S4 object # getDescription Extracts the description slot from a S4 object # getSlot Extracts a specified slot from a S4 object # getArgs Shows the arguments of a S4 functiong ################################################################################ if(getRversion() < "2.14") { getCall <- function(x) slot(x, "call") } ## since 2.14.0, getCall() is an S3 generic in 'stats' ## *AND* its default method also works for S4 and a "call" slot # ------------------------------------------------------------------------------ getModel <- function(object) { # A function implemented by Tobias Setz # FUNCTION: # Return Value: UseMethod("getModel") } getModel.default <- function(object) { # A function implemented by Rmetrics # Description: # gets the "model" slot from an object of class 4 # Arguments: # object - an object of class S4 # FUNCTION: # Return Value: slot(object, "model") } # ------------------------------------------------------------------------------ getTitle <- function(object) { # A function implemented by Rmetrics # Description: # gets the "title" slot from an object of class 4 # Arguments: # object - an object of class S4 # FUNCTION: # Return Value: slot(object, "title") } # ------------------------------------------------------------------------------ getDescription <- function(object) { # A function implemented by Rmetrics # Description: # Extracts the "description" slot from an object of class 4 # Arguments: # object - an object of class S4 # FUNCTION: # Return Value: slot(object, "description") } # ------------------------------------------------------------------------------ getSlot <- function(object, slotName) { # a simple wrapper around slot to avoid compatibilty troubles with # other packages. # FUNCTION: slot(object, slotName) } # ------------------------------------------------------------------------------ getArgs <- function(f, signature = character()) { # A function implemented by Diethelm Wuertz # Description: # Shows the arguments of a S4 functiong # Arguments: # f - a generic function or the character-string name of one # signature - the signature of classes to match to the arguments # of f # Note: # such a function is missing in the methods package, # see, e.g he function getMethod() # FUNCTION: # Get arguments: fun <- getMethod(f, signature)@.Data test <- class(try(body(fun)[[2]][[3]], silent = TRUE)) if (test == "function") { ans <- args(body(fun)[[2]][[3]]) } else { ans <- args(fun) } cat(substitute(f), ",", paste(signature, collapse = ","), ":\n", sep = "") # Return Value: ans } ################################################################################ fBasics/R/utils-Heaviside.R0000644000176200001440000001002014263246022015201 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: HEAVISIDE AND RELATED FUNCTIONS: # Heaviside Computes Heaviside unit step function # Sign Another signum function # Delta Computes delta function # Boxcar Computes boxcar function # Ramp Computes ramp function ################################################################################ Heaviside <- function(x, a = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the Heaviside or unit step function. # Arguments: # x - a numeric vector. # a - the location of the break. # Details: # The Heaviside step function is 1 for x>a, 1/2 for x=a, # and 0 for xa, 0 for x=a, # and -1 for x # License: GPL version 2 or newer # Thanks: # to Spencer Grave for contributions. # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (inherits(x, "fREG")) x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if (n < 8) stop("sample size must be greater than 7") var.x <- var(x) if(var.x > 0){ p = pnorm((x - mean(x))/sqrt(var.x)) h = (2 * seq(1:n) - 1) * (log(p) + log(1 - rev(p))) ### DW modified: h = h[is.finite(h)] n = length(h) ### Continue: A = -n - mean(h) AA = (1 + 0.75/n + 2.25/n^2) * A if (AA < 0.2) { PVAL = 1 - exp(-13.436 + 101.14 * AA - 223.73 * AA^2) } else if (AA < 0.34) { PVAL = 1 - exp(-8.318 + 42.796 * AA - 59.938 * AA^2) } else if (AA < 0.6) { PVAL = exp(0.9177 - 4.279 * AA - 1.38 * AA^2) } else { PVAL = exp(1.2937 - 5.709 * AA + 0.0186 * AA^2) } # Result: if (PVAL > 1) { PVAL = 1 # was NA, suggested by Spencer Graves to modify W = NA } } else { A <- Inf PVAL <- 0 } names(PVAL) = "" test = list( statistic = c(A = A), p.value = PVAL, method = "Anderson - Darling Normality Test", data.name = DNAME) # Add: if (is.null(title)) title = "Anderson - Darling Normality Test" if (is.null(description)) description = "" # Return Value: ans = new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description)) } # ------------------------------------------------------------------------------ cvmTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Cramer-von Mises normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (inherits(x, "fREG")) x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if (n < 8) stop("sample size must be greater than 7") p = pnorm((x - mean(x))/sqrt(var(x))) W = (1/(12 * n) + sum((p - (2 * seq(1:n) - 1)/(2 * n))^2)) WW = (1 + 0.5/n) * W # P Value: if (WW < 0.0275) { PVAL = 1 - exp(-13.953 + 775.5 * WW - 12542.61 * WW^2) } else if (WW < 0.051) { PVAL = 1 - exp(-5.903 + 179.546 * WW - 1515.29 * WW^2) } else if (WW < 0.092) { PVAL = exp(0.886 - 31.62 * WW + 10.897 * WW^2) } else { PVAL = exp(1.111 - 34.242 * WW + 12.832 * WW^2) } # Result: if (PVAL > 1) { PVAL = 1 W = NA } names(PVAL) = "" test = list( statistic = c(W = W), p.value = PVAL, method = "Cramer - von Mises Test", data.name = DNAME) # Add: if (is.null(title)) title = "Cramer - von Mises Normality Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ lillieTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Lilliefors (Kolmogorov-Smirnov) normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (inherits(x, "fREG")) x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if (n < 5) stop("sample size must be greater than 4") p = pnorm((x - mean(x))/sqrt(var(x))) Dplus = max(seq(1:n)/n - p) Dminus = max(p - (seq(1:n) - 1)/n) K = max(Dplus, Dminus) if (n <= 100) { Kd = K nd = n } else { Kd = K * ((n/100)^0.49) nd = 100 } # P Value: PVAL = exp(-7.01256 * Kd^2 * (nd + 2.78019) + 2.99587 * Kd * sqrt(nd + 2.78019) - 0.122119 + 0.974598/sqrt(nd) + 1.67997/nd) if (PVAL > 0.1) { KK = (sqrt(n) - 0.01 + 0.85/sqrt(n)) * K if (KK <= 0.302) { PVAL = 1 } else if (KK <= 0.5) { PVAL = 2.76773 - 19.828315 * KK + 80.709644 * KK^2 - 138.55152 * KK^3 + 81.218052 * KK^4 } else if (KK <= 0.9) { PVAL = -4.901232 + 40.662806 * KK - 97.490286 * KK^2 + 94.029866 * KK^3 - 32.355711 * KK^4 } else if (KK <= 1.31) { PVAL = 6.198765 - 19.558097 * KK + 23.186922 * KK^2 - 12.234627 * KK^3 + 2.423045 * KK^4 } else { PVAL = 0 } } names(PVAL) = "" # Result: test = list( statistic = c(D = K), p.value = PVAL, method = "Lilliefors Test", data.name = DNAME) # Add: if (is.null(title)) title = "Lilliefors (KS) Normality Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ pchiTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Pearson chi-square normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Details: # n.classes - an integer value, the classes are build is such # a way that they are equiprobable under the hypothesis # of normality. We use Moore's default value. # adjust - a logical flag, if TRUE (default), the p-value # is computed from a chi-square distribution with # n.classes-3 degrees of freedom, otherwise from a # chi-square distribution with n.classes-1 degrees of # freedom. We print the results for both # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (inherits(x, "fREG")) x = residuals(x) x = as.vector(x) # Call: call = match.call() # Moore's Default Value: n.classes = ceiling(2 * (length(x)^(2/5))) names(n.classes) = "Number of Classes" # Test: n = length(x) dfd = c(2, 0) names(dfd) = c("TRUE", "FALSE") # adjust num = floor(1 + n.classes * pnorm(x, mean(x), sqrt(var(x)))) count = tabulate(num, n.classes) prob = rep(1/n.classes, n.classes) xpec = n * prob h = ((count - xpec)^2)/xpec P = sum(h) # For Splus compatibility: # pvalue = pchisq(P, n.classes - dfd - 1, lower.tail = FALSE) PVAL = c(1 - pchisq(P, n.classes - dfd[1] - 1), 1 - pchisq(P, n.classes - dfd[2] - 1)) names(PVAL) = c("Adhusted", "Not adjusted") # Return Value: test = list( statistic = c(P = P), p.value = PVAL, method = "Pearson Chi-Square Normality Test", data.name = DNAME, parameter = n.classes) # Add: if (is.null(title)) title = "Pearson Chi-Square Normality Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ sfTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs the Shapiro-Francia normality test # Arguments: # x - a numeric vector of data values. # description - a brief description of the porject of type # character. # title - a character string which allows for a project title. # Notes: # A copy from contributed R-package 'nortest' # Source: # Package: nortest # Title: Tests for Normality # Version: 1.0 # Author: Juergen Gross # Description: 5 omnibus tests for the composite hypothesis of normality # Maintainer: Juergen Gross # License: GPL version 2 or newer # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Convert Type: if (inherits(x, "fREG")) x = residuals(x) x = as.vector(x) # Call: call = match.call() # Test: x = sort(x) n = length(x) if ((n < 5 || n > 5000)) stop("sample size must be between 5 and 5000") y = qnorm(ppoints(n, a = 3/8)) W = cor(x, y)^2 u = log(n) v = log(u) mu = -1.2725 + 1.0521 * (v - u) sig = 1.0308 - 0.26758 * (v + 2/u) z = (log(1 - W) - mu)/sig # For Splus compatibility: # pval = pnorm(z, lower.tail = FALSE) PVAL = 1 - pnorm(z) names(PVAL) = "" # Test: test = list( statistic = c(W = W), p.value = PVAL, method = "Shapiro-Francia Normality Test", data.name = DNAME) # Add: if (is.null(title)) title = "Shapiro - Francia Normality Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description)) } ################################################################################ # AUGMENTED FINITE SAMPLE JB TEST: .jb.test <- function(x) { # A function implemented by Diethelm Wuertz # Description: # S3 version type finite sample adjusted JB test # Arguments: # Notes: # S3 Version type of test. # See also the Jarque-Bera test in Adrian Trapletti's # contributed "tseries" R package. # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Check: if (NCOL(x) > 1) stop("x is not a vector or univariate time series") if (any(is.na(x))) stop("NAs in x") # LM Coefficients: n = length(x) c1lm = 6/n c2lm = 3 c3lm = 24/n # ALM Coefficients: c1alm = 6*(n-2) / ((n+1)*(n+3)) c2alm = 3*(n-1) / (n+1) c3alm = 24*n*(n-2)*(n-3) / ((n+1)^2*(n+3)*(n+5)) # Statistic: m1 = sum(x)/n m2 = sum((x - m1)^2)/n m3 = sum((x - m1)^3)/n m4 = sum((x - m1)^4)/n b1 = (m3/m2^(3/2)) b2 = (m4/(m2*m2)) STATISTIC = b1*b1/c1lm + ((b2-c2lm)^2)/c3lm ALM = b1*b1/c1alm + ((b2-c2alm)^2)/c3alm names(STATISTIC) = "LM" # The rest goes as parameters ... pvalLM = 1 - .pjb(STATISTIC, N = n, type = "LM") pvalALM = 1 - .pjb(ALM, N = n, type = "ALM") PARAMETER = c(ALM, n, pvalLM, pvalALM) names(PARAMETER) = c( "ALM", "Sample Size", "LM p-value", "ALM p-value" ) # P Values: PVAL = 1 - pchisq(STATISTIC[1], df = 2) names(PVAL) = "" # Method String: METHOD = "Jarque Bera Test" # Result: Digits = 3 RVAL = list( statistic = round(STATISTIC, digits = Digits), parameter = round(PARAMETER, digits = Digits), p.value = round(PVAL, digits = Digits), method = METHOD, data.name = DNAME) # Return Value: class(RVAL) = "htest" RVAL } # ------------------------------------------------------------------------------ jbTest <- function(x, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs finite sample adjusted Jarque-Bera LM and ALM test # Arguments: # x - a univariate timeSeries object or numeric vector # Notes: # S3 Version type of test. # FUNCTION: # Data Set Name: DNAME = deparse(substitute(x)) # Call: call = match.call() # Convert Type: if (inherits(x, "fREG")) x = residuals(x) x = as.vector(x) # Test: test = .jb.test(x) class(test) = "list" parameter = test$parameter[2] statistic = c(test$statistic, test$parameter[1]) p.value = c(test$parameter[3], test$parameter[4], Asymptotic = test$p.value[[1]]) test$parameter = parameter test$statistic = statistic test$p.value = p.value # Add Title: if (is.null(title)) title = "Jarque - Bera Normality Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/dist-ght.R0000644000176200001440000001131314263246022013673 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dght Density of the generalized hyperbolic Student-t # pght Probability of the GHT # dght Quantiles of the GHT # rght Random variates of the GHT ################################################################################ dght <- function(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density of the generalized hyperbolic Student-t # Arguments: # Example: # x = (-5):5; dght(x) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Density: ans = dgh(x, alpha, beta, delta, mu, lambda, log = log) # Return Value: ans } # ------------------------------------------------------------------------------ pght <- function(q, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities of the generalized hyperbolic Student-t # Arguments: # Example: # q = (-5):5; pght(q) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # Cumulative Probability: ans = NULL for (Q in q) { Integral = integrate(dght, -Inf, Q, stop.on.error = FALSE, beta = beta, delta = delta, mu = mu, nu = nu) ans = c(ans, as.numeric(unlist(Integral)[1]) ) } # Return Value: ans } # ------------------------------------------------------------------------------ qght <- function(p, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the generalized hyperbolic Student-t # Arguments: # Example: # p = (1:9)/10; qght(p); round(pght(qght(p)), digits = 4) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # Internal Functions: .froot <- function(x, beta = beta, delta = delta, mu = mu, nu = nu, p) { pght(q = x, beta = beta, delta = delta, mu = mu, nu = nu) - p } # Loop over all p's: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = .unirootNA(f = .froot, interval = c(lower, upper), beta = beta, delta = delta, mu = mu, nu = nu, p = pp) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } result = c(result, iteration) } # Return Value: ans = result + mu ans } # ------------------------------------------------------------------------------ rght <- function(n, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns random Variates of generalized hyperbolic Student-t # Arguments: # Example: # r = rght(10) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Random Variates: x = rgh(n, alpha, beta = beta, delta = delta, mu = mu, lambda) # Return Value: x } ################################################################################ fBasics/R/stats-basicStats.R0000644000176200001440000000721114263246022015406 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: BASIC STATISTICS: # basicStats Returns a basic statistics summary ################################################################################ basicStats = function(x, ci = 0.95) { # A function implemented by Diethelm Wuertz # Description: # Calculates Basic Statistics # Arguments: # x - an object which can be transformed by the function # as.matrix() into an object of class matrix. # ci - a numeric value setting the confidence interval. # Value: # a two-column data frame, where the first column takes the # value of the statistics, and the second its name, e.g. # "nobs", "NAs", "Minimum", "Maximum", "1. Quartile", # "3. Quartile", "Mean", "Median", "Sum", "SE Mean", # "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness", # "Kurtosis") # FUNCTION: # Univariate/Multivariate: y = as.matrix(x) # Handle Column Names: if (is.null(colnames(y))) { Dim = dim(y)[2] if (Dim == 1) { colnames(y) = paste(substitute(x), collapse = ".") } else if (Dim > 1) { colnames(y) = paste(paste(substitute(x), collapse = ""), 1:Dim, sep = "") } } # Internal Function - CL Levels: cl.vals = function(x, ci) { x = x[!is.na(x)] n = length(x) if(n <= 1) return(c(NA, NA)) se.mean = sqrt(var(x)/n) t.val = qt((1 - ci)/2, n - 1) mn = mean(x) lcl = mn + se.mean * t.val ucl = mn - se.mean * t.val c(lcl, ucl) } # Basic Statistics: nColumns = dim(y)[2] ans = NULL for (i in 1:nColumns) { X = y[, i] # Observations: X.length = length(X) X = X[!is.na(X)] X.na = X.length - length(X) # Basic Statistics: z = c( X.length, X.na, min(X), max(X), as.numeric(quantile(X, prob = 0.25, na.rm = TRUE)), as.numeric(quantile(X, prob = 0.75, na.rm = TRUE)), mean(X), median(X), sum(X), sqrt(var(X)/length(X)), cl.vals(X, ci)[1], cl.vals(X, ci)[2], var(X), sqrt(var(X)), skewness(X), kurtosis(X) ) # Row Names: znames = c( "nobs", "NAs", "Minimum", "Maximum", "1. Quartile", "3. Quartile", "Mean", "Median", "Sum", "SE Mean", "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness", "Kurtosis") # Output as data.frame result = matrix(z, ncol = 1) row.names(result) = znames ans = cbind(ans, result) } # Column Names: colnames(ans) = colnames(y) # Return Value: data.frame(round(ans, digits = 6)) } ################################################################################ fBasics/R/dist-nigMoments.R0000644000176200001440000001246714263246022015244 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigMean Returns true NIG mean # nigVar Returns true NIG variance # nigSkew Returns true NIG skewness # nigKurt Returns true NIG kurtosis # FUNCTION: DESCRIPTION: # nigMoments Returns true NIG moments # FUNCTION: DESCRIPTION: # nigShapeTriangle Plots NIG Shape Triangle ################################################################################ nigMean <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG mean # FUNCTION: # Mean: gamma = sqrt(alpha^2 - beta^2) ans = c(mean = (mu + delta * beta / gamma)[[1]]) # Return Value: ans } # ------------------------------------------------------------------------------ nigVar <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG variance # FUNCTION: # Variance: gamma = sqrt(alpha^2 - beta^2) ans = c(var = (delta * alpha^2 / gamma^3)[[1]]) # Return Value: ans } # ------------------------------------------------------------------------------ nigSkew <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG skewness # FUNCTION: # Skewness: gamma = sqrt(alpha^2 - beta^2) ans = c(skew = (3*beta / ( alpha * sqrt(delta*gamma) ))[[1]] ) # Return Value: ans } # ------------------------------------------------------------------------------ nigKurt <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG kurtosis # FUNCTION: # Skewness: gamma = sqrt(alpha^2 - beta^2) ans = c(kurt = (3 * ( 1 + 4 * beta^2 / alpha^2) / (delta * gamma))[[1]]) # Return Value: ans } ############################################################################### nigMoments <- function(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true moments of the NIG distribution # FUNCTION: # Settings: type = match.arg(type) # Moments: lambda = -1/2 if (type == "raw") { ans = .ghRawMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghCentralMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { ans = .ghMuMoments(order, alpha, beta, delta, mu, lambda) } # Return Value: ans } ################################################################################ nigShapeTriangle <- function(object, add = FALSE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Plots NIG Shape Triangle # Arguments: # object - an object of class 'fDISTFIT' as returned by the # function nigFit() # Example: # nigShapeTriangle(nigFit(rnig(100), doplot = FALSE)) # FUNCTION: # Settings: stopifnot(class(object) == "fDISTFIT") # Plot Frame: if (labels) { xlab = "Asymmetry: chi" ylab = "Steepness: zeta" main = "NIG Shape Traingle" } else { xlab = ylab = main = "" } if (!add) { x = c(-1, 0, 1, -1) y = c( 1, 0, 1, 1) plot(x, y, type = "l", xlab = xlab, ylab = ylab, main = main, ...) for (s in c(0.8, 0.6, 0.4, 0.2)) lines(c(-s, s), c(s, s), lty = 3, col = "grey") lines(c(0, 0), c(0, 1), lty = 3, col = "grey") } # Transform: par = object@fit$estimate names(par) = c("alpha", "beta", "delta", "mu") alpha = par[1] beta = par[2] delta = par[3] mu = par[4] # Add Points: zeta = 1/sqrt(1+delta*sqrt(alpha^2-beta^2)) chi = zeta*(beta/alpha) if (labels) { points(chi, zeta, pch = 19, ...) } else { points(chi, zeta, ...) } # Result: ans = list(chi = chi[[1]], zeta = zeta[[1]]) attr(ans, "control")<-par # Return Value: ans } ################################################################################ fBasics/R/builtin-hypHyperbolicDist.R0000644000176200001440000004764514263246022017302 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .rghyp # .rgigjd # .rgigjd1 # .dhyp1 # .dhyp2 # .dhyp3 # .dhyp4 # .phyp1 # .phyp2 # .phyp3 # .phyp4 # .qhyp1 # .qhyp2 # .qhyp3 # .qhyp4 # .rhyp1 # .rhyperb # .hyperb.change.pars # .rhyp2 # .rhyp3 # .rhyp4 ################################################################################ # This is code borrowed from # David Scott's website. # The functions are also part of previous versions of the R contributed # package Hyperbolic Dist. # Rmetrics: # Note that these functions are not packaged and available on Debian # as of 2007-06-23. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. ################################################################################ .rghyp <- function(n, theta) { # A function implemented by Diethelm Wuertz # Author: # Original Version by David Scott # FUNCTION: # Settings: lambda = theta[1] alpha = theta[2] beta = theta[3] delta = theta[4] mu = theta[5] chi = delta^2 psi = alpha^2 - beta^2 # Ckecks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Random Numbers: if (lambda == 1){ X = .rgigjd1(n, c(lambda, chi, psi)) } else{ X = .rgigjd(n, c(lambda, chi, psi)) } # Result: sigma = sqrt(X) Z = rnorm(n) Y = mu + beta*sigma^2 + sigma*Z # Return Value: Y } # ------------------------------------------------------------------------------ .rgigjd <- function(n, theta) { # A function implemented by Diethelm Wuertz # Author: # Original Version by David Scott # FUNCTION: # Settings: lambda = theta[1] chi = theta[2] psi = theta[3] # Checks: if (chi < 0) stop("chi can not be negative") if (psi < 0) stop("psi can not be negative") if ((lambda >= 0)&(psi==0)) stop("When lambda >= 0, psi must be > 0") if ((lambda <= 0)&(chi==0)) stop("When lambda <= 0, chi must be > 0") if (chi == 0) stop("chi = 0, use rgamma") if (psi == 0) stop("algorithm only valid for psi > 0") alpha = sqrt(psi/chi) beta = sqrt(psi*chi) m = (lambda-1+sqrt((lambda-1)^2+beta^2))/beta g = function(y){ 0.5*beta*y^3 - y^2*(0.5*beta*m+lambda+1) + y*((lambda-1)*m-0.5*beta) + 0.5*beta*m } upper = m while (g(upper) <= 0) upper = 2*upper yM = uniroot(g, interval=c(0,m))$root yP = uniroot(g, interval=c(m,upper))$root a = (yP-m)*(yP/m)^(0.5*(lambda-1))*exp(-0.25*beta*(yP+1/yP-m-1/m)) b = (yM-m)*(yM/m)^(0.5*(lambda-1))*exp(-0.25*beta*(yM+1/yM-m-1/m)) c = -0.25*beta*(m+1/m) + 0.5*(lambda-1)*log(m) output = numeric(n) for(i in 1:n){ need.value = TRUE while(need.value==TRUE){ R1 = runif (1) R2 = runif (1) Y = m + a*R2/R1 + b*(1-R2)/R1 if (Y>0){ if (-log(R1)>=-0.5*(lambda-1)*log(Y)+0.25*beta*(Y+1/Y)+c){ need.value = FALSE } } } output[i] = Y } # Return Value: output/alpha } # ------------------------------------------------------------------------------ .rgigjd1 <- function(n, theta) { # A function implemented by Diethelm Wuertz # Description: # Modified version of rgigjd to generate random observations # from a generalised inverse Gaussian distribution in the # special case where lambda = 1. # Author: # Original Version by David Scott # FUNCTION: if (length(theta) == 2) theta = c(1, theta) # Settings: lambda = 1 chi = theta[2] psi = theta[3] # Checks: if (chi < 0) stop("chi can not be negative") if (psi < 0) stop("psi can not be negative") if (chi == 0) stop("chi = 0, use rgamma") if (psi == 0) stop("When lambda >= 0, psi must be > 0") alpha = sqrt(psi/chi) beta = sqrt(psi*chi) m = abs(beta)/beta g = function(y){ 0.5*beta*y^3 - y^2*(0.5*beta*m+lambda+1) + y*(-0.5*beta) + 0.5*beta*m } upper = m while (g(upper)<=0) upper = 2*upper yM = uniroot(g,interval=c(0,m))$root yP = uniroot(g,interval=c(m,upper))$root a = (yP-m)*exp(-0.25*beta*(yP+1/yP-m-1/m)) b = (yM-m)*exp(-0.25*beta*(yM+1/yM-m-1/m)) c = -0.25*beta*(m+1/m) output = numeric(n) for(i in 1:n){ need.value = TRUE while(need.value==TRUE){ R1 = runif (1) R2 = runif (1) Y = m + a*R2/R1 + b*(1-R2)/R1 if (Y>0){ if (-log(R1)>=0.25*beta*(Y+1/Y)+c){ need.value = FALSE } } } output[i] = Y } # Return Value: output/alpha } # ------------------------------------------------------------------------------ .dhyp1 <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic Density Function PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Density: efun = exp( -alpha*sqrt(delta^2 + (x-mu)^2) + beta*(x-mu) ) sqr = sqrt(alpha^2-beta^2) bK1 = besselK(delta*sqr, nu = 1) prefac = sqr / ( 2 * alpha * delta * bK1) ans = prefac * efun # Return Value: ans } # ------------------------------------------------------------------------------ .dhyp2 <- function(x, zeta = 1, rho = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic density in the 2nd parameterization # FUNCTION: # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = dhyp(x, alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .dhyp3 <- function(x, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic density in the 2nd parameterization # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = dhyp(x, alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .dhyp4 <- function(x, a.bar = 1, b.bar = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic density in the 2nd parameterization # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta # Return Value: ans = dhyp(x, alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .phyp1 <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Return cumulative probability of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Cumulative Probability: ans = NULL for (Q in q) { Integral = integrate(dhyp, -Inf, Q, stop.on.error = FALSE, alpha = alpha, beta = beta, delta = delta, mu = mu, ...) # Works in both, R and SPlus: ans = c(ans, as.numeric(unlist(Integral)[1]) ) } # Return Value: ans } # ------------------------------------------------------------------------------ .phyp2 <- function(q, zeta = 1, rho = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns cumulative probability in the 2nd parameterization # Arguments: # zeta, rho - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = phyp(q, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .phyp3 <- function(q, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns cumulative probability in the 3rd parameterization # Arguments: # xi, xhi - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = phyp(q, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .phyp4 <- function(q, a.bar = 1, b.bar = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns cumulative probability in the 4th parameterization # Arguments: # a.bar, b.bar - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta # Return Value: ans = phyp(q, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .qhyp1 <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Note: # This procedure will not run under Splus. # FUNCTION: # Internal Functions: .froot <- function(x, alpha, beta, delta, p) { phyp(q = x, alpha = alpha, beta = beta, delta = delta, mu = 0) - p } # Loop over all p's: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = .unirootNA(f = .froot, interval = c(lower, upper), alpha = alpha, beta = beta, delta = delta, p = pp) counter = counter + 1 lower = lower-2^counter upper = upper+2^counter } result = c(result, iteration) } # Return Value: ans = result + mu ans } # ------------------------------------------------------------------------------ .qhyp2 <- function(p, zeta = 1, rho = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF in the 2nd parameterization # Arguments: # zeta, rho - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = qhyp(p, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .qhyp3 <- function(p, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF in the 3rd parameterization # Arguments: # zeta, chi - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = qhyp(p, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .qhyp4 <- function(p, a.bar = 1, b.bar = 0, delta = 1, mu = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF in the 4th parameterization # Arguments: # a.bar, b.bar - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = NA beta = NA # Return Value: ans = qhyp(p, alpha, beta, delta, mu, ...) ans } # ------------------------------------------------------------------------------ .rhyp1 <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF # Arguments: # n - number of random deviates to be generated # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Notes: # I have removed my original Fortran program and replaced it by # the dhyperb() function from the HyperbolicDist Package, written # by David Scott, Ai-Wei Lee, Jennifer Tso, Richard Trendall. # License: GPL # FUNCTION: # Result - Use Standard Parameterization: Zeta = delta * sqrt(alpha^2 - beta^2) hyp.Pi = beta / sqrt(alpha^2 - beta^2) theta = c(hyp.Pi, Zeta, delta, mu) # Return Value: ans = .rhyperb(n = n, theta = theta) ans } # ------------------------------------------------------------------------------ .rhyperb <- function(n, theta) { # FUNCTION: # Internal Function: hyp.pi = theta[1] zeta = theta[2] delta = theta[3] mu = theta[4] alpha = as.numeric(.hyperb.change.pars(1, 2, theta))[1] * delta beta = as.numeric(.hyperb.change.pars(1, 2, theta))[2] * delta phi = as.numeric(.hyperb.change.pars(1, 3, theta))[1] * delta gamma = as.numeric(.hyperb.change.pars(1, 3, theta))[2] * delta theta.start = -sqrt(phi * gamma) t = -sqrt(gamma/phi) w = sqrt(phi/gamma) delta1 = exp(theta.start)/phi delta2 = (w - t) * exp(theta.start) delta3 = exp(-gamma * w)/gamma k = 1/(delta1 + delta2 + delta3) r = k * delta1 v = 1 - k * delta3 output = numeric(n) need.value = TRUE for (i in 1:n) { while (need.value == TRUE) { U = runif(1) E = rexp(1) if (U <= r) { x = 1/phi * log(phi * U/k) if (E >= alpha * (sqrt(1 + x^2) + x)) { need.value = FALSE } } if ((U > r) & (U <= v)) { x = t - 1/phi + U * exp(-theta.start)/k if (E >= alpha * sqrt(1 + x^2) - beta * x + theta.start) { need.value = FALSE } } if (U > v) { x = 1/gamma * log(k/gamma) - 1/gamma * log(1 - U) if (E >= alpha * (sqrt(1 + x^2) - x)) { need.value = FALSE } } } output[i] = delta * x + mu need.value = TRUE } # Return Value: output } # ------------------------------------------------------------------------------ .hyperb.change.pars <- function(from, to, theta) { # FUNCTION: # Internal Function: delta <- theta[3] mu <- theta[4] hyperb.pi <- theta[1] zeta <- theta[2] if (from == 1 && to == 2) { alpha <- zeta * sqrt(1 + hyperb.pi^2)/delta beta <- zeta * hyperb.pi/delta output = c(alpha = alpha, beta = beta, delta = delta, mu = mu) } if (from == 1 && to == 3) { phi <- zeta/delta * (sqrt(1 + hyperb.pi^2) + hyperb.pi) gamma <- zeta/delta * (sqrt(1 + hyperb.pi^2) - hyperb.pi) output = c(phi = phi, gamma = gamma, delta = delta, mu = mu) } # Return Value: output } # ------------------------------------------------------------------------------ .rhyp2 <- function(n, zeta = 1, rho = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF in the 2nd parameterization # Arguments: # zeta, rho - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho ans = rhyp(n, alpha, beta, delta, mu) # Return Value: ans } # ------------------------------------------------------------------------------ .rhyp3 <- function(n, xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF in the 3rd parameterization # Arguments: # zeta, chi - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho ans = rhyp(n, alpha, beta, delta, mu) # Return Value: ans } # ------------------------------------------------------------------------------ .rhyp4 <- function(n, a.bar = 1, b.bar = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF in the 4th parameterization # Arguments: # a.bar, b.bar - Shape Parameter, resulting |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta ans = rhyp(n, alpha, beta, delta, mu) # Return Value: ans } ################################################################################ fBasics/R/plot.R0000644000176200001440000002654214264011647013144 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: INTERNAL USED PLOT FUNCTIONS: # .residualsPlot Returns a residual series plot # .acfPlot Returns a autocorrelation function plot # .pacfPlot Returns a partial ACF plot # .mrlPlot Returns a mean residual life plot # FUNCTION: INTERNAL USED BIVARIATE PLOT FUNCTIONS: # .responsesPlot Returns a response series plot # .firePlot Returns a fitted values vs.residuals plot # FUNCTION: INTERNAL THREE-DIMENSIONAL PLOT UTILITIES: # .circlesPlot Returns a circles plot indexing 3rd variable # .perspPlot Returns a perspective plot in 2 dimensions # .contourPlot Returns a contour plot in 2 dimensions # .histStack Returns a stacked histogram plot ################################################################################ .residualsPlot <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns time series graph of residuals # Arguments: # x - an univariate time series of residuals # FUNCTION: # Get Data: x = as.vector(x) # Plot: plot(x, type = "l", ylab = "Residuals", main = "Residual Series", col = "steelblue", ...) rug(x, ticksize = 0.01, side = 4) grid() abline(h = 0, col = "grey") # Return Value: invisible() } # ------------------------------------------------------------------------------ .acfPlot <- function(x, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Convert Type: x = as.vector(x) # ACF: acf(x, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ .pacfPlot <- function(x, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Convert Type: x = as.vector(x) # ACF: pacf(x, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ .mrlPlot <- function(x, ci = 0.95, umin = mean(x), umax = max(x), nint = 100, doplot = TRUE, plottype = c("autoscale", ""), labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Create a mean residual life plot with # confidence intervals. # References: # A function originally written by S. Coles # FUNCTION: # Convert Type: x = as.vector(x) # Settings: plottype = plottype[1] # Convert x to a vector, if the input is a data.frame. if (is.data.frame(x)) x = x[,1] sx = xu = xl = rep(NA, nint) u = seq(umin, umax, length = nint) for (i in 1:nint) { x = x[x >= u[i]] sx[i] = mean(x - u[i]) sdev = sqrt(var(x)) n = length(x) xu[i] = sx[i] + (qnorm((1 + ci)/2) * sdev) / sqrt(n) xl[i] = sx[i] - (qnorm((1 + ci)/2) * sdev) / sqrt(n) } # Plot: if (doplot) { if (labels) { xlab = "Threshold: u" ylab = "Mean Excess: e" main = "Mean Residual Live Plot" } else { main = xlab = ylab = "" } if (plottype == "autoscale") { ylim = c(min(xl[!is.na(xl)]), max(xu[!is.na(xu)])) plot(u, sx, type = "o", pch = 19, col = "steelblue", xlab = xlab, ylab = ylab, ylim = ylim, main = main, ...) } else { plot(u[!is.na(xl)], sx[!is.na(xl)], type = "o", pch = 19, col = "steelblue", xlab = xlab, ylab = ylab, main = main, ...) } lines(u[!is.na(xl)], xl[!is.na(xl)], col = "brown") lines(u[!is.na(xu)], xu[!is.na(xu)], col = "brown") if (labels) { grid() text = paste("ci =", as.character(round(ci, 3))) mtext(text, side = 4, adj = 0, cex = 0.7) } } # Result result = data.frame(threshold = u, mrl = sx) # Return Value: if (doplot) return(invisible(result)) else return(result) } ################################################################################ .responsesPlot <- function(x, y = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns time series graph of responses and fitted values # Arguments: # x - an univariate time series of responses # y - an univariate time series of fitted values # FUNCTION: # Get Data: x = as.vector(x) y = as.vector(y) # Responses Plot: plot(x, type = "l", ylab = "Responses", main = "Responses & Fitted Values", col = "steelblue", ...) rug(x, ticksize = 0.01, side = 4) grid() abline(h = 0, col = "grey") # Add fitted values: if (!is.null(y)) points(y, pch = 19, col = "red") # Return Value: invisible() } # ------------------------------------------------------------------------------ .firePlot <- function(x, y, method = c("scatter", "hist"), ...) { # A function implemented by Diethelm Wuertz # Description: # Returns fitted values vs. residuals plots # Arguments: # x - univariate time series (residuals) # y - univariate time series (fitted) # FUNCTION: # Check Arguments: method = match.arg(method) # Get Data: x = as.vector(x) y = as.vector(y) if (method == "scatter") { # Scatter Plot: plot(x, y, xlab = "Fitted Values", ylab = "Residuals", main = "Residuals vs Fitted", pch = 19, col = "steelblue") panel.smooth(x, y) abline(h = 0, lty = 3, col = "grey") rug(x, ticksize = 0.01, side = 3) rug(y, ticksize = 0.01, side = 4) } else if (method == "hist") { # Histogram Plot: # Save default, for resetting ... def.par = par(no.readonly = TRUE) # Layout: nf = layout(matrix(c(2, 0, 1, 3), 2, 2, byrow = TRUE), c(3, 1), c(1, 3), TRUE) # Scatterplot: par(mar = c(3 ,3, 1, 1)) plot(x, y, xlim = range(x), ylim = range(y), xlab = "", ylab = "", pch = 19, col = "steelblue") panel.smooth(x, y) abline(h = 0, lty = 3, col = "grey") rug(x, side = 3) rug(y, side = 4) # Histogram: xhist = hist(x, nclass = 15, plot = FALSE) yhist = hist(y, nclass = 15, plot = FALSE) top = max(c(xhist$counts, yhist$counts)) # X-Side: par(mar = c(0, 3, 1, 1)) Main = "\n Fitted" barplot(xhist$counts, axes = FALSE, ylim = c(0, top), space = 0, col = "steelblue", border = "white", main = Main) abline(h = 0, lwd = 2, col = "grey") # Y-Side: par(mar = c(3, 0, 1, 1)) barplot(yhist$counts, axes = FALSE, xlim = c(0, top), space = 0, col = "steelblue", , border = "white", horiz = TRUE, main = "Residuals") abline(v = 0, lwd = 2, col = "grey") # Reset: par(def.par) } # Return Value: invisible() } ################################################################################ .circlesPlot <- function(x, y = NULL, z = NULL, scale = 1, points = TRUE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Creates a scatterplot with circle size as third variable # Example: # circlesPlot(x=rnorm(50), y=rnorm(50), z=rnorm(50)) # circlesPlot(x=rnorm(50), y=rnorm(50), z=rnorm(50), labels= FALSE) # FUNCTION: # Transfor Input: if (is.list(x)) x = matrix(unlist(x), ncol = 3) if (is.data.frame(x)) x = as.matrix.data.frame(x) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } nX = length(x) nY = length(y) # nZ = length(z) stopifnot(nX == nY) # stopifnot(nX == nZ || nX*nY == nZ) # Create Circle Plot: if (labels) { plot(x, y, type = "n") } else { plot(x, y, xlab = "", ylab = "", type = "n") } symbols(x, y, add = TRUE, circles = abs(z)^scale, inches = 0.25, fg = "black", bg = "steelblue", ...) X = x[z < 0] Y = y[z < 0] Z = z[z < 0] symbols(X, Y, add = TRUE, circles = abs(Z)^scale, inches = 0.25, fg = "black", bg = "orange", ...) if (points) points(x, y, pch = 19) grid() # Return Value: invisible(NULL) } # ------------------------------------------------------------------------------ .perspPlot <- function(x, y, z, theta = -40, phi = 30, col = "steelblue", ps = 9, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a perspecvtive plot # Notes: # A synonyme call for function 'persp' # FUNCTION: # Perspective Plot: if (inherits(version, "Sversion")) { # we assume SPlus: ans = persp(x = x, y = y, z = z, ...) } else { # R: par(ps = ps) if (!exists("ticktype")) ticktype = "detailed" if (!exists("expand")) expand = 0.6 if (!exists("r")) r = 500 ans = persp(x = x, y = y, z = z, theta = theta, phi = phi, col = col, ticktype = ticktype, expand = expand, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ .contourPlot <- function(x, y, z, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a contour plot # Notes: # A synonyme call for function 'contour' # FUNCTION: # Contour Plot: if (inherits(version, "Sversion")) { # we assume SPlus: ans = contour(x = x, y = y, z = z, ...) } else { # R: ans = contour(x = x, y = y, z = z, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ .histStack <- function(x, y = NULL, space = 0, ylab = "frequency", ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a stacked histogram Plot # Example: # .histStack(rnorm(1000, -1), rnorm(1000, 1)) # FUNCTION: # Compute Histograms: breaks = hist(c(x, y))$breaks bars = rbind( hist(x, breaks = breaks, plot = FALSE)$counts, hist(y, breaks = breaks, plot = FALSE)$counts) # Plot: barplot(bars, space = space, ylab = ylab, ...) at = seq(along = breaks) - 1 modulo = ceiling(length(at)/10) sel = (at%%modulo == 0) axis(side = 1, at = at[sel], labels = paste(breaks)[sel]) # Return Value: invisible() } ################################################################################ fBasics/R/dist-norm.R0000644000176200001440000000251214263246022014065 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dnorm Returns density of the normal distribution # pnorm Returns probabilities of mormal distribution # qnorm Returns quantiles of the normal distribution # rnorm Generates normal distributed random variates ################################################################################ ## The functions are part of R's base package ################################################################################ fBasics/R/dist-sghFit.R0000644000176200001440000001030214324532663014341 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # sghFit Fits parameters of a standardized GH density ################################################################################ sghFit <- function(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { x.orig = x x = as.vector(x) if (scale) x = (x-mean(x)) / sd(x) eps = .Machine$double.eps^0.5 BIG = 1000 if (include.lambda) { # LLH Function: obj.include = function(x, y = x, trace) { f = -sum(log(dsgh(y, x[1], x[2], x[3], log = FALSE))) if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], "\n") } f } # LLH Optimization: r = nlminb( start = c(zeta, rho, lambda), objective = obj.include, lower = c(eps, -0.9999, -2), upper = c(BIG, +0.9999, +5), y = x, trace = trace) names(r$par) <- c("zeta", "rho", "lambda") } else { # LLH Function: obj = function(x, y = x, lambda, trace) { f = -sum(log(dsgh(y, x[1], x[2], lambda, log = FALSE))) if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], "\n") } f } # LLH Optimization: r = nlminb( start = c(zeta, rho), objective = obj, lower = c(eps, -0.9999), upper = c(BIG, +0.9999), y = x, lambda = lambda, trace = trace) r$par = c(r$par, lambda) names(r$par) <- c("zeta", "rho", "fix.lambda") } param = .paramGH(r$par[1], r$par[2], r$par[3]) if (trace) { cat("\n Standardized Parameters:", "\n ") print(r$par) names(param) = c("alpha", "beta", "delta", "mu") cat("\n 1st Parameterization:", "\n ") print(param) } # Default Title and Description: if (is.null(title)) title = "SGH Parameter Estimation" if (is.null(description)) description = "" # Fit: fit = list( estimate = r$par, minimum = -r$objective, code = r$convergence, param = param, mean = mean(x.orig), var = var(x.orig)) # Optional Plot: if (doplot) { if (span == "auto") span = seq(min(x), max(x), length = 101) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dsgh(span, zeta = r$par[1], rho = r$par[2], lambda) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } # Return Value: new("fDISTFIT", call = match.call(), model = "Standarized GH Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } ################################################################################ fBasics/R/dist-nigMode.R0000644000176200001440000000312514263246022014475 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigMode Computes the normal inverse Gaussian mode ################################################################################ nigMode <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Normal Inverse Gaussian PDF # FUNCTION: # Find Maximum: min = qnig(0.01, alpha, beta, delta, mu) max = qnig(0.99, alpha, beta, delta, mu) ans = optimize(f = dnig, interval = c(min, max), alpha = alpha, beta = beta, delta = delta, mu = mu, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/dist-gh.R0000644000176200001440000001224514650724111013514 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dgh Returns density for generalized hyperbolic DF # pgh Returns probability for generalized hyperbolic DF # qgh Returns quantiles for generalized hyperbolic DF # rgh Returns random variates for generalized hyperbolic DF ################################################################################ dgh <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density for the generalized hyperbolic distribution # FUNCTION: # Parameters: if (length(alpha) == 4) { mu = alpha[4] delta = alpha[3] beta = alpha[2] alpha = alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Density: arg = delta*sqrt(alpha^2-beta^2) a = (lambda/2)*log(alpha^2-beta^2) - ( log(sqrt(2*pi)) + (lambda-0.5)*log(alpha) + lambda*log(delta) + log(besselK(arg, lambda, expon.scaled = TRUE)) - arg ) f = ((lambda-0.5)/2)*log(delta^2+(x - mu)^2) # Use exponential scaled form to prevent from overflows: arg = alpha * sqrt(delta^2+(x-mu)^2) k = log(besselK(arg, lambda-0.5, expon.scaled = TRUE)) - arg e = beta*(x-mu) # Put all together: ans = a + f + k + e if(!log) ans = exp(ans) # Return Value: ans } # ------------------------------------------------------------------------------ pgh <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns probability for the generalized hyperbolic distribution # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Probability: ans = NULL for (Q in q) { Integral = integrate(dgh, -Inf, Q, stop.on.error = FALSE, alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda) ans = c(ans, as.numeric(unlist(Integral)[1])) } # Return Value: ans } # ------------------------------------------------------------------------------ qgh <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for the generalized hyperbolic distribution # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Internal Function: .froot <- function(x, alpha, beta, delta, mu, lambda, p) { pgh(q = x, alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda) - p } # Quantiles: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = .unirootNA(f = .froot, interval = c(lower, upper), alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda, p = pp) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } result = c(result, iteration) } # Return Value: result } # ------------------------------------------------------------------------------ rgh <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns random variates for the generalized hyperbolic distribution # FUNCTION: # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Settings: theta = c(lambda, alpha, beta, delta, mu) # Random Numbers: ans = .rghyp(n, theta) # Return Value: ans } ################################################################################ fBasics/R/gui-hypSlider.R0000644000176200001440000000722714263246022014706 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # hypSlider Displays hyperbolic distribution function ################################################################################ hypSlider <- function() { # A function implemented by Diethelm Wuertz # Description: # Displays interactively Hyperbolic Function # Details: # Hyperbolic Distribution: # dhyp(x, alpha = 1, beta = 0, delta = 1, mu = 0, pm = c(1, 2, 3, 4)) # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) delta = .sliderMenu(no = 4) mu = .sliderMenu(no = 5) pm = .sliderMenu(no = 6) # Plot Data: xmin = round(qhyp(0.01, alpha, beta, delta, mu, pm), digits = 2) xmax = round(qhyp(0.99, alpha, beta, delta, mu, pm), digits = 2) s = seq(xmin, xmax, length = N) y1 = dhyp(s, alpha, beta, delta, mu, pm) y2 = phyp(s, alpha, beta, delta, mu, pm) main1 = paste("HYP Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "delta = ", as.character(delta), " | ", "mu = ", as.character(mu) ) main2 = paste("HYP Probability\n", "xmin 0.01% = ", as.character(xmin), " | ", "xmax 0.99% = ", as.character(xmax), " | ", "pm = ", as.character(pm) ) # Frame par(mfrow = c(2, 1), cex = 0.7) # Density: plot(s, y1, type = "l", xlim = c(xmin, xmax), col = "steelblue") abline (h = 0, lty = 3) title(main = main1) # Probability: plot(s, y2, type = "l", xlim = c(xmin, xmax), ylim = c(0, 1), col = "steelblue" ) abline(h = 0.0, lty = 3) abline(h = 1.0, lty = 3) abline(h = 0.5, lty = 3) abline(v = mu, lty = 3, col = "red") title(main = main2) # Reset Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "N","alpha","beta","delta", "mu","pm"), minima = c( 50, 0.00, -2.00, 0.00, -5.0, 1), maxima = c(1000, 2.00, +2.00, 5.00, +5.0, 4), resolutions = c( 50, 0.20, 0.20, 1.00, 1.0, 1), starts = c( 50, 1.00, 0.00, 1.00, 0.0, 1)) } ################################################################################ fBasics/R/matrix-tr.R0000644000176200001440000000252514263246022014104 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # tr Returns the trace of a matrix ################################################################################ tr <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns the trace of a matrix # FUNCTION: # Trace: if (dim(x)[1] != dim(x)[2] ) { return(NA) } else { return(sum(diag(x))) } # Return Value: invisible() } ################################################################################ fBasics/R/gui-sliderMenu.R0000644000176200001440000002175214263246022015051 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ############################################################################### # FUNCTION: SLIDER MENU: # .sliderMenu Opens a teching demo slider menu # .tdSliderMenu Opens a teching demo slider and button menu ############################################################################### .slider.env = new.env() # ------------------------------------------------------------------------------ .sliderMenu <- function(refresh.code, names, minima, maxima, resolutions, starts, title = "Slider", no = 0, set.no.value = 0) { # A function implemented by Diethelm Wuertz # Description: # Starts a slider menu # Source: # Built on code written by Peter Wolf # FUNCTION: # Requirement: if (!requireNamespace("tcltk", quietly = TRUE)) stop("Package 'tcltk' not available (should *not* happen, please report!)\n") # Environment: if (!exists(".slider.env")) { .slider.env <<- new.env() } if (no != 0) { options(show.error.messages = FALSE) ans <- as.numeric(tcltk::tclvalue(get(paste("slider", no, sep = ""), envir = .slider.env))) options(show.error.messages = TRUE) return(ans) } if (set.no.value[1] != 0) { try(eval(parse(text = paste("tclvalue(slider", set.no.value[1], ")<-", set.no.value[2], sep = "")), envir = .slider.env), silent = TRUE) return(set.no.value[2]) } # Toplevel: nt = tcltk::tktoplevel() tcltk::tkwm.title(nt, title) # Slider: for (i in seq(names)) { eval(parse(text = paste("assign(\"slider", i, "\", tclVar(starts[i]), envir = .slider.env)", sep = ""))) tcltk::tkpack(fr<-tcltk::tkframe(nt), anchor = "sw") lab = tcltk::tklabel(fr, text = names[i], anchor = "sw") sc = tcltk::tkscale(fr, command = refresh.code, from = minima[i], to = maxima[i], showvalue = TRUE, resolution = resolutions[i], orient = "horiz") assign("sc", sc, envir = .slider.env) tcltk::tkgrid(sc, lab) eval(parse(text = paste("tkconfigure(sc, variable = slider", i, ")", sep = "")), envir = .slider.env) } tcltk::tkpack(fr<-tcltk::tkframe(nt), anchor = "sw") # Quit: quitButton = tcltk::tkbutton(fr, text = " Quit ", command = function() { tcltk::tkdestroy(nt) } ) # Reset: resetButton = tcltk::tkbutton(fr, text = " Start | Reset ", command = function() { for (i in seq(starts)) eval(parse(text = paste("tclvalue(slider", i, ")<-", starts[i], sep="")), envir = .slider.env) refresh.code() } ) # Compose: tcltk::tkgrid(resetButton, quitButton, sticky = "sew") } # ------------------------------------------------------------------------------ .tdSliderMenu <- function(sl.functions, names, minima, maxima, resolutions, starts, but.functions, but.names, no, set.no.value, obj.name, obj.value, reset.function, title) { # A function implemented by Diethelm Wuertz # Description # Opens a teching demo slider menu # Notes: # Build on ideas and code from: # R Package: TeachingDemos # Title: Demonstrations for teaching and learning # Version: 1.5 # Author: Greg Snow # Description: This package is a set of demonstration functions # that can be used in a classroom to demonstrate statistical # concepts, or on your own to better understand the concepts # or the programming. # Maintainer: Greg Snow # License: Artistic # FUNCTION: # Requirement: if (!requireNamespace("tcltk", quietly = TRUE)) stop("Package 'tcltk' not available (should *not* happen, please report!)\n") # Setup: if(!missing(no)) { return(as.numeric(tcltk::tclvalue(get(paste(".tdSlider", no, sep=""), envir = .slider.env)))) } if(!missing(set.no.value)){ try(eval(parse(text=paste("tclvalue(.tdSlider", set.no.value[1],")<-", set.no.value[2], sep = "")), envir = .slider.env)) return(set.no.value[2]) } if(!exists(".slider.env")) { .slider.env <<- new.env() } if(!missing(obj.name)){ if(!missing(obj.value)) { assign(obj.name, obj.value, envir = .slider.env) } else { obj.value <- get(obj.name, envir = .slider.env) } return(obj.value) } if(missing(title)) { title = "Control Widget" } # GUI Settings: nt <- tcltk::tktoplevel() tcltk::tkwm.title(nt, title) tcltk::tkwm.geometry(nt, "+0+0") # Buttons: tcltk::tkpack( f.but <- tcltk::tkframe(nt), fill = "x") # Quit Button: quitCMD = function() { tcltk::tkdestroy(nt) } tcltk::tkpack( tcltk::tkbutton(f.but, text = "Quit", command = quitCMD, anchor = "sw"), side = "right", fill = "y") # Reset Button: if(missing(reset.function)) { reset.function <- function(...) print("relax") } if(!is.function(reset.function)) { reset.function<-eval(parse(text = paste("function(...){",reset.function,"}"))) } resetCMD = function() { for(i in seq(names)) eval(parse(text = paste("tclvalue(.tdSlider",i,")<-", starts[i], sep = "")), envir = .slider.env) reset.function() } tcltk::tkpack( tcltk::tkbutton(f.but, text = "Reset", command = resetCMD, anchor = "sw"), side = "right", fill = "y") if (missing(but.names)) { but.names <- NULL } for (i in seq(but.names)) { but.fun <- if (length(but.functions) > 1) but.functions[[i]] else but.functions if (!is.function(but.fun)) { but.fun <- eval(parse(text = paste("function(...){", but.fun, "}"))) } tcltk::tkpack( tcltk::tkbutton(f.but, text = but.names[i], command = but.fun, anchor = "nw"), # side = "right", fill = "x" ) } # Sliders: if(missing(names)) { names <- NULL } if(missing(sl.functions)) { sl.functions <- function(...){} } for(i in seq(names)){ eval(parse(text = paste("assign('.tdSlider",i,"', tclVar(starts[i]), env = .slider.env)", sep = ""))) tcltk::tkpack(fr <- tcltk::tkframe(nt)) lab <- tcltk::tklabel(fr, text = names[i], anchor = "sw", width = "35") sc <- tcltk::tkscale(fr, from = minima[i], to = maxima[i], showvalue = TRUE, resolution = resolutions[i], orient = "horiz") tcltk::tkpack(lab, sc, anchor = "sw", side = "right"); assign("sc", sc, envir = .slider.env) eval(parse(text=paste("tkconfigure(sc,variable=.tdSlider",i,")", sep="")), envir = .slider.env) sl.fun <- if(length(sl.functions)>1) sl.functions[[i]] else sl.functions if(!is.function(sl.fun)) sl.fun<-eval(parse(text=paste("function(...){", sl.fun,"}"))) tcltk::tkconfigure(sc, command = sl.fun) } assign("slider.values.old", starts, envir = .slider.env) # Return Value: invisible(nt) } ################################################################################ fBasics/R/plot-counts.R0000644000176200001440000000604014263246022014440 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .counts1Plot Creates a plot of counts # .counts2Plot Creates an alternative plot of counts ################################################################################ .counts1Plot= function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Creates a plot of counts # Arguments: # x - a vector of counts # Note: # From R-help, ref. Gabor Grothendiek # Example: # x <- c(26,26,27,27,27,27,28,28,28,28,28,28,28,28,28,28,28,28, # 28,29,29,29,29,29,29,29,29,29,29,29,30,30,30,30,30,30,30, # 30,31,31,31,31,32,33,33,33,34,34,35,43); .counts1Plot(x) # FUNCTION: # Plot Range: xmin = min(x) xmax = max(x) xlim = c(xmin - 0.1*(xmax-xmin), xmax + 0.1*(xmax-xmin)) # Plot plot(seq(y) - match(y, y) ~ y, list(y = sort(x)), xlim = xlim, ylab = "", yaxt = "n", pch = 19, frame.plot = FALSE, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ .counts2Plot <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Creates a plot of counts # Arguments: # x - a vector of counts # Note: # From R-help, ref. Jinsong Zhao # Example: # x <- c(26,26,27,27,27,27,28,28,28,28,28,28,28,28,28,28,28,28, # 28,29,29,29,29,29,29,29,29,29,29,29,30,30,30,30,30,30,30, # 30,31,31,31,31,32,33,33,33,34,34,35,43); .counts2Plot(x) # FUNCTION: # Tabulate: x.table <- table(x) x.x <- as.numeric(names(x.table)) x.y <- as.numeric(x.table) n <- length(x.x) x.final <- NULL for (i in 1:n) { tmp <- data.frame(x = rep(x.x[i], x.y[i]), y = 1:x.y[i]) x.final <- rbind(x.final, tmp) } # Plot Range: xmin = min(x) xmax = max(x) xlim = c(xmin - 0.1*(xmax-xmin), xmax + 0.1*(xmax-xmin)) # Plot: plot(x.final, yaxt = "n", ylab = "", xlim = xlim, pch = 19, frame.plot = FALSE, ...) # Return Value: invisible(x) } ################################################################################ fBasics/R/gui-stylizedFacts.R0000644000176200001440000004415414264011647015577 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ############################################################################### # FUNCTION: STYLIZED FACTS GUI: # .stylizedFactsGUI Opens a GUI for stylized facts # .lmacfPlot Estimates and displays the long memory ACF # .logpdfPlot Displays a pdf plot on logarithmic scale(s) # .ccfPlot Displays tailored cross correlation function plot # .qqgaussPlot Displays a tailored Gaussian quantile-quantile plot ############################################################################### .lmacfPlot <- function(x, lag.max = max(2, floor(10*log10(length(x)))), ci = 0.95, type = c("both", "acf", "hurst"), labels = TRUE, trace = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Evaluate and display long memory autocorrelation Function. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) X = x # Match Arguments: type = match.arg(type) # Labels: if (labels) { main1 = "" xlab1 = "lag" ylab1 = "ACF" main2 = "" xlab2 = "log lag" ylab2 = "log ACF" } else { main1 = xlab1 = ylab1 = "" main2 = xlab2 = ylab2 = "" } Fitted = list() Hurst = NULL DIM = dim(X)[2] for (i in 1:DIM) { # Get Data: x.ret = as.matrix(X)[, i] x = abs(x.ret) if (labels) main1 = main2 = Units[i] # ACF: z = acf(x, lag.max = lag.max, type = "correlation", plot = FALSE) z$acf[1] = 0 cl = qnorm(0.5 + ci/2)/sqrt(z$n.used) z.min = min(z$acf, -cl) # lin-lin plot excluding one: x = seq(0, lag.max, by = 1) y = z$acf if (type == "both" | type == "acf") { plot(x = x[-1], y = y[-1], type = "l", main = main1, col = "steelblue", xlab = xlab1, ylab = ylab1, xlim = c(0, lag.max), ylim = c(-2*cl, max(y[-1])), ...) # abline(h = 0, lty = 3) if (trace) { cat ("\nLong Memory Autocorrelation Function:") paste (cat ("\n Maximum Lag "), cat(lag.max)) paste (cat ("\n Cut-Off ConfLevel "), cat(cl)) } ACF = acf(x.ret, lag.max = lag.max, plot = FALSE)$acf[,,1] lines(x = 1:lag.max, y = ACF[-1], type = "l", col = "steelblue") lines(x = c(-0.1, 1.1)*lag.max, y = c(+cl, +cl), lty = 3, col = "darkgrey") lines(x = c(-0.1, 1.1)*lag.max, y = c(-cl, -cl), lty = 3, col = "darkgrey") } # log-log Plot of ACF: x = x[y > cl] y = y[y > cl] # log-log: if (length(x) < 10) { Fit = c(NA, NA) hurst = NA cat("\n The time series exhibits no long memory! \n") } else { Fit = lsfit(log(x), log(y)) fit = unlist(Fit)[1:2] hurst = 1 + fit[2]/2 if (type == "both" | type == "hurst") { plot(x = log(x), y = log(y), type = "l", xlab = xlab2, ylab = ylab2, main = main2, col = "steelblue", ...) Text = paste("Hurst Exponent:", signif(hurst, 3)) mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) # Grid: if (labels) grid() } ### fit = l1fit(log(x), log(y))$coefficients abline(fit[1], fit[2], col = 1) if (trace) { paste (cat ('\n Plot-Intercept '), cat(fit[1])) paste (cat ('\n Plot-Slope '), cat(fit[2])) paste (cat ('\n Hurst Exponent '), cat(hurst), cat("\n")) } } # Save Results: if (DIM == 1) Fitted = Fit else Fitted[[i]] = Fit Hurst = c(Hurst, hurst) } # Return Value: invisible(list(fit = Fitted, hurst = Hurst)) } # ------------------------------------------------------------------------------ .logpdfPlot = function(x, breaks = "FD", type = c("lin-log", "log-log"), doplot = TRUE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a pdf plot on logarithmic scale(s) # Details: # Returns a pdf plot on a lin-log scale in # comparison to a Gaussian density plot # and return the break-midpoints and the # counts obtained from the histogram of # the empirical data. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Select Type: type = match.arg(type) # Labels: if (labels) { if (type == "lin-log") { main = "log PDF" xlab = "x" ylab = "log PDF" } else if (type == "log-log") { main = "log PDF" xlab = "log x" ylab = "log PDF" } } else { main = xlab = ylab = "" } X = x DIM = ncol(X) for (i in 1:DIM) { # Get Data: x = as.vector(X[, i]) if (labels) main = Units[i] # Lin-Log Plot: if (type == "lin-log") { # Histogram PDF: result = hist(x, breaks = breaks, plot = FALSE) prob.counts = result$counts/sum(result$counts) / diff(result$breaks)[1] histogram = list(breaks = result$breaks, counts = prob.counts) # Histogram Count & Break-Midpoints: yh = histogram$counts xh = histogram$breaks xh = xh[1:(length(xh)-1)] + diff(xh)/2 xh = xh[yh > 0] yh = log(yh[yh > 0]) if (doplot) { par(err = -1) plot(xh, yh, type = "p", pch = 19, col = "steelblue", main = main, xlab = xlab, ylab = ylab, ...) Text = "Scales: log-log" mtext(Text, side = 4, adj =0, col = "darkgrey", cex = 0.7) } # Compare with a Gaussian Plot: xg = seq(from = xh[1], to = xh[length(xh)], length = 301) yg = log(dnorm(xg, mean(x), sqrt(var(x)))) if (doplot) { par(err = -1) lines(xg, yg, col = "brown") } # Return Value: result = list(breaks = xh, counts = yh, fbreaks = xg, fcounts = yg) } # Log-Log Plot: if (type == "log-log") { # Histogram PDF: result = hist(x, breaks = breaks, plot = FALSE) prob.counts = result$counts/sum(result$counts) / diff(result$breaks)[1] histogram = list(breaks = result$breaks, counts = prob.counts) # Histogram Count & Breaks: yh = histogram$counts xh = histogram$breaks xh = xh[1:(length(xh)-1)] + diff(xh)/2 xh = xh[yh > 0] yh = yh[yh > 0] yh1 = yh[xh < 0] xh1 = abs(xh[xh < 0]) yh2 = yh[xh > 0] xh2 = xh[xh > 0] if (doplot) { plot(log(xh1), log(yh1), type = "p", pch = 19, col = "darkgreen", main = main, xlab = xlab, ylab = ylab, ...) Text = "Scales: log-log" mtext(Text, side = 4, adj =0, col = "darkgrey", cex = 0.7) par(err = -1) points(log(xh2), log(yh2), col = 2, ...) } # Compare with a Gaussian plot: xg = seq(from = min(xh1[1], xh[2]), to = max(xh1[length(xh1)], xh2[length(xh2)]), length = 301) xg = xg[xg > 0] yg = log(dnorm(xg, mean(x), sqrt(var(x)))) if (doplot) { par(err = -1) lines(log(xg), yg, col = "brown") } # Result: result = list(breaks = c(xh1, xh2), counts = c(yh1, yh2), fbreaks = c(-rev(xg), xg), fcounts = c(-rev(yg), yg)) } # Grid: if (labels) grid() } # Return Value: invisible(result) } # ------------------------------------------------------------------------------ .qqgaussPlot <- function(x, span = 5, col = "steelblue", labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a simple Quantile-Quantile plot. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: # if (SPLUSLIKE) splusLikePlot(TRUE) # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "Normal QQ Plot" xlab = "Theoretical Quantiles" ylab = "Sample Quantiles" } else { main = xlab = ylab = "" } X = x DIM = dim(X)[2] for (i in 1:DIM) { # Get Data: x = as.vector(as.matrix(X)[, i]) if (labels) main = Units[i] # Standardized qqnorm(): y = (x-mean(x)) / sqrt(var(x)) # Further Settings: y[abs(y) < span] lim = c(-span, span) # Plot qqnorm: qqnorm(y, main = main, xlab = xlab, ylab = ylab, xlim = lim, ylim = lim, col = col, ...) # Add Line: qqline(y, ...) # Grid: if (labels) grid() } # Return Value: invisible(x) } # ------------------------------------------------------------------------------ .ccfPlot <- function(x, y, lag.max = max(2, floor(10*log10(length(x)))), type = c("correlation", "covariance", "partial"), labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Cross correlation function plot # Arguments: # x - an univariate 'timeSeries' object # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Convert Type: if (inherits(x, "timeSeries")) stopifnot(isUnivariate(x)) if (inherits(y, "timeSeries")) stopifnot(isUnivariate(y)) x = as.vector(x) y = as.vector(y) # Labels: if (labels) { main = "Crosscorrelation Function" xlab = "lag" ylab = "CCF" } else { main = xlab = ylab = "" } # Result: # A copy from R's ccf - So you can use it also under SPlus: X = cbind(x = x, y = y) acf.out = acf(X, lag.max = lag.max, plot = FALSE, type = type[1]) lag = c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2]) y = c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2]) acf.out$acf = array(y, dim = c(length(y), 1, 1)) acf.out$lag = array(lag, dim = c(length(y), 1, 1)) acf.out$snames = paste(acf.out$snames, collapse = " & ") plot(acf.out, main = main, xlab = xlab, ylab = ylab, ...) # Return Value: invisible(acf.out) } ################################################################################ .stylizedFactsGUI <- function(x, mfrow = c(3, 3)) { # A function implemented by Diethelm Wuertz # Description: # Opens a GUI for stylized facts # FUNCTION: # Refresh Code: stylizedFactsRefreshCode <- function(...) { # Settings: selectedAsset = .tdSliderMenu(no = 1) type = as.integer(.tdSliderMenu(obj.name = "stylizedFactsType")) Unit = colnames(x) # ACF Plot: if (type == 1) { if (selectedAsset == 0) { par(mfrow = mfrow) acfPlot(x) } else { par(mfrow = c(1, 1)) acfPlot(x[, selectedAsset]) } } # PACF Plot: if (type == 2) { if (selectedAsset == 0) { par(mfrow = mfrow) pacfPlot(x) } else { par(mfrow = c(1, 1)) pacfPlot(x[, selectedAsset]) } } # Volatility ACF Plot: if (type == 3) { if (selectedAsset == 0) { par(mfrow = mfrow) acfPlot(abs(x)) } else { par(mfrow = c(1, 1)) acfPlot(abs(x[, selectedAsset])) } } # Taylor Effect Plot: if (type == 4) { if (selectedAsset == 0) { par(mfrow = mfrow) teffectPlot(x) } else { par(mfrow = c(1, 1)) teffectPlot(x[, selectedAsset]) } } # Long Memory ACF: if (type == 5) { if (selectedAsset == 0) { par(mfrow = mfrow) .lmacfPlot(abs(x)) } else { par(mfrow = c(1, 1)) .lmacfPlot(abs(x[, selectedAsset])) } } # Lagged Autocorrelation Plot: if (type == 6) { if (selectedAsset == 0) { par(mfrow = mfrow) lacfPlot(x) } else { par(mfrow = c(1, 1)) lacfPlot(x[, selectedAsset]) } } # PDF plot on lin-log Scale: if (type == 7) { if (selectedAsset == 0) { par(mfrow = mfrow) .logpdfPlot(x) } else { par(mfrow = c(1, 1)) .logpdfPlot(x[, selectedAsset]) } } # PDF plot on log-log Scale: if (type == 8) { if (selectedAsset == 0) { par(mfrow = mfrow) .logpdfPlot(x, type = "log-log") } else { par(mfrow = c(1, 1)) .logpdfPlot(x[, selectedAsset], type = "log-log") } } # Simple Normal Quantile-Quantile Plot if (type == 9) { if (selectedAsset == 0) { par(mfrow = mfrow) .qqgaussPlot(x, pch = 19) } else { par(mfrow = c(1, 1)) .qqgaussPlot(x[, selectedAsset], pch = 19) } } # Scaling Law Plot: if (type == 10) { if (selectedAsset == 0) { par(mfrow = mfrow) scalinglawPlot(x, pch = 19) } else { par(mfrow = c(1, 1)) scalinglawPlot(x[, selectedAsset], pch = 19) } } } nAssets = dim(x)[2] .tdSliderMenu( stylizedFactsRefreshCode, names = c("Selected Asset"), minima = c( 0), maxima = c( nAssets), resolutions = c( 1), starts = c( 0), but.functions = list( function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "1") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "2") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "3") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "4") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "5") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "6") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "7") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "8") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "9") stylizedFactsRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "stylizedFactsType", obj.value = "10") stylizedFactsRefreshCode()} ), but.names = c( "1 ACF Function of Returns", "2 Partial ACF of Returns", "3 ACF of absolute Returns", "4 Taylor Effect", "5 Long Memory ACF of abs Returns", "6 Lagged Autocorrelations", "7 Lin-Log Tail Density Plot", "8 Log-Log Lower Tail Density", "9 Simple Normal QQ Plot", "10 Scaling Law Plot"), title = "Stylized Facts GUI" ) .tdSliderMenu(obj.name = "type", obj.value = "1", no = 1) # return Value: invisible() } ################################################################################ fBasics/R/matrix-rk.R0000644000176200001440000000261714263246022014075 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # rk Returns the rank of a matrix ################################################################################ rk <- function(x, method = c("qr", "chol")) { # A function implemented by Diethelm Wuertz # Description: # Returns the rank of a matrix # FUNCTION: # Rank: method = method[1] if (method == "chol") { ans = attr(chol(x, pivot = TRUE), "rank") } else { ans = qr(x)$rank } # Return Value: ans } ################################################################################ fBasics/R/dist-ghMode.R0000644000176200001440000000320714263246022014317 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghMode Computes mode of the generalized hyperbolic DF ################################################################################ ghMode <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Generalized Hyperbolic PDF # FUNCTION: # Find Maximum: min = qgh(0.01, alpha, beta, delta, mu, lambda) max = qgh(0.99, alpha, beta, delta, mu, lambda) ans = optimize(f = dgh, interval = c(min, max), alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/utils-colorTable.R0000644000176200001440000000344114263246022015377 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # colorTable Shows a table of plot color codes ################################################################################ colorTable <- function(cex = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Displays a table of plot colors. # Author: # Unknown, piece of code found on the internet. # Example: # colorTable() # FUNCTION: # Plot: plot(0, 0, xlim = c(-1, 10), ylim = c(0, 10), type = 'n', axes = FALSE, xlab = '', ylab = '', cex = cex, main = "Table of Color Codes") ## DW ## j = -1 j = -2 ## here I have shifted the plot to the left ... DW for(i in 0:99) { if(i %% 10 == 0) {j = j+1; k = 10} k = k-1 points(j, k, pch = 15, col = i, cex = 2) text(j+0.45, k, i, cex = cex)} # Return Value: invisible() } ################################################################################ fBasics/R/matrix-triang.R0000644000176200001440000000323614263246022014743 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # GENERATION: DESCRIPTION: # triang Extracs the lower tridiagonal part from a matrix # Triang Extracs the upper tridiagonal part from a matrix ################################################################################ triang <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns lower triangle matrix # FUNCTION: # Triangulate: x[row(x) < col(x)] = 0 # Return Value: x } # ------------------------------------------------------------------------------ Triang <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns upper triangle matrix # FUNCTION: # Triangulate x[row(x) > col(x)] = 0 # Return Value: x } ################################################################################ fBasics/R/utils-colorRGB.R0000644000176200001440000001057514263246022014770 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .asRGB Converts any R color to RGB (red/green/blue) # .chcode Changes from one to another number system # .hex.to.dec Converts heximal numbers do decimal numbers # .dec.to.hex Converts decimal numbers do heximal numbers ################################################################################ .asRGB <- function(col = rainbowPalette(64), alpha = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Converts any R color to RGB (red/green/blue) # Arguments: # col - vector of any of the three kind of R colors, i.e., either a # color name (an element of colors()), a hexadecimal string of # the form "#rrggbb", or an integer i meaning palette()[i]. # alpha - a logical value indicating whether alpha channel values # should be returned. # FUNCTION: # Color Conversion: result <- col2rgb(col = col, alpha = alpha) # Return Value: t(result) } # ------------------------------------------------------------------------------ .chcode <- function(b, base.in = 2, base.out = 10, digits = "0123456789ABCDEF") { # A function implemented by Diethelm Wuertz # Description: # Changes from one to another number system # Arguments: # b - number specified in the input base # b.in - input base # b.out - output base # digits - digits string # Value: # returns the input in the form represented by the output base # Author: # Peter Wolf Universitaet Bielefeld # from: http://tolstoy.newcastle.edu.au/R/help/05/04/2085.html # FUNCTION: # Change Number System: digits = substring(digits,1:nchar(digits),1:nchar(digits)) if (length(base.in) == 1) base.in = rep(base.in, max(nchar(b) - 1)) if (is.numeric(b)) b = as.character(as.integer(b)) b.num = lapply(strsplit(b, ""), function(x) {match(x, digits)-1} ) result = lapply(b.num, function(x) {cumprod(rev(c(base.in,1))[1:length(x)]) %*% rev(x)} ) number = unlist(result) # DW Print Output Suppressed # cat("decimal representation:",number,"\n") if (length(base.out) == 1) { base.out<-rep(base.out, 1+ceiling(log(max(number), base = base.out))) } n.base = length(base.out) result = NULL for(i in n.base:1){ result = rbind(number %% base.out[i], result) number = floor(number/base.out[i]) } result[]<-digits[result+1] ans = apply(result, 2, paste, collapse = "") # Return Value: ans } # ------------------------------------------------------------------------------ .hex.to.dec <- function(b) { # A function implemented by Diethelm Wuertz # Description: # Converts heximal numbers do decimal numbers # Arguments: # b - a heximal number # Value: # returns a heximal numbers as decimal numbers # FUNCTION: # Hex to Bin: ans = as.numeric(.chcode(b, base.in = 16, base.out = 10)) # Return Value: ans } # ------------------------------------------------------------------------------ .dec.to.hex <- function(b) { # A function implemented by Diethelm Wuertz # Description: # Converts decimal numbers do heximal numbers # Arguments: # x - a decimal number # Value: # returns a decimal numbers as heximal numbers # FUNCTION: # Decimal to Hex: ans = .chcode(b, base.in = 10, base.out = 16) # Return Value: ans } ################################################################################ fBasics/R/plot-subPlot.R0000644000176200001440000001566314263246022014570 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .emptyPlot Creates an empty plot page # .subPlot Creates a sub plot # .cnvrt.coords Converts coordinates ################################################################################ .emptyPlot <- function() { # Description: # Creates an empty plot page # FUNCTION: # Plot: plot(c(0, 1), c(0, 1), type = "n", xlab = "", ylab = "", ann = TRUE, axes = FALSE) # Return Value: invisible() } # ------------------------------------------------------------------------------ # Package: TeachingDemos # Title: Demonstrations for teaching and learning # Version: 1.6 # Author: Greg Snow # Description: This package is a set of demonstration functions that can # be used in a classroom to demonstrate statistical concepts, or on your # own to better understand the concepts or the programming. # Maintainer: Greg Snow # License: Artistic-2.0 # Date: 2007-09-07 # Suggests: tcltk, lattice, tkrplot, MASS, rgl, sgeostat, mapproj # Enhances: maptools, qcc # LazyData: true # Packaged: Wed Nov 28 12:13:53 2007; hornik # ------------------------------------------------------------------------------ # Example: # plot(c(0, 1), c(0, 1)); .subPlot(hist(rnorm(100)), x = 0.5, y = 0.5) # ------------------------------------------------------------------------------ .subPlot <- function(fun, x, y = NULL, size = c(1, 1), vadj = 0.5, hadj = 0.5, pars = NULL) { # Description: # Creates a sub plot # FUNCTION: # Par: old.par <- par(no.readonly=TRUE) on.exit(par(old.par)) if(missing(x)) x <- locator(2) xy <- xy.coords(x,y) if(length(xy$x) != 2){ pin <- par('pin') tmp <- .cnvrt.coords(xy$x[1],xy$y[1],'usr')$plt x <- c( tmp$x - hadj*size[1]/pin[1], tmp$x + (1-hadj)*size[1]/pin[1] ) y <- c( tmp$y - vadj*size[2]/pin[2], tmp$y + (1-vadj)*size[2]/pin[2] ) xy <- .cnvrt.coords(x,y,'plt')$fig } else { xy <- .cnvrt.coords(xy,,'usr')$fig } par(pars) par(plt=c(xy$x,xy$y), new = TRUE) fun tmp.par <- par(no.readonly = TRUE) return(invisible(tmp.par)) } # ------------------------------------------------------------------------------ .cnvrt.coords <- function(x,y=NULL,input=c('usr','plt','fig','dev','tdev')) { # Description: # Converts Coordinates # FUNCTION: input <- match.arg(input) xy <- xy.coords(x,y) cusr <- par('usr') cplt <- par('plt') cfig <- par('fig') cdin <- par('din') comi <- par('omi') cdev <- c(comi[2]/cdin[1],(cdin[1]-comi[4])/cdin[1], comi[1]/cdin[2],(cdin[2]-comi[3])/cdin[2]) if(input=='usr'){ usr <- xy plt <- list() plt$x <- (xy$x-cusr[1])/(cusr[2]-cusr[1]) plt$y <- (xy$y-cusr[3])/(cusr[4]-cusr[3]) fig <- list() fig$x <- plt$x*(cplt[2]-cplt[1])+cplt[1] fig$y <- plt$y*(cplt[4]-cplt[3])+cplt[3] dev <- list() dev$x <- fig$x*(cfig[2]-cfig[1])+cfig[1] dev$y <- fig$y*(cfig[4]-cfig[3])+cfig[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='plt') { plt <- xy usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] fig <- list() fig$x <- plt$x*(cplt[2]-cplt[1])+cplt[1] fig$y <- plt$y*(cplt[4]-cplt[3])+cplt[3] dev <- list() dev$x <- fig$x*(cfig[2]-cfig[1])+cfig[1] dev$y <- fig$y*(cfig[4]-cfig[3])+cfig[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='fig') { fig <- xy plt <- list() plt$x <- (fig$x-cplt[1])/(cplt[2]-cplt[1]) plt$y <- (fig$y-cplt[3])/(cplt[4]-cplt[3]) usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] dev <- list() dev$x <- fig$x*(cfig[2]-cfig[1])+cfig[1] dev$y <- fig$y*(cfig[4]-cfig[3])+cfig[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='dev'){ dev <- xy fig <- list() fig$x <- (dev$x-cfig[1])/(cfig[2]-cfig[1]) fig$y <- (dev$y-cfig[3])/(cfig[4]-cfig[3]) plt <- list() plt$x <- (fig$x-cplt[1])/(cplt[2]-cplt[1]) plt$y <- (fig$y-cplt[3])/(cplt[4]-cplt[3]) usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } if(input=='tdev'){ tdev <- xy dev <- list() dev$x <- (tdev$x-cdev[1])/(cdev[2]-cdev[1]) dev$y <- (tdev$y-cdev[3])/(cdev[4]-cdev[3]) fig <- list() fig$x <- (dev$x-cfig[1])/(cfig[2]-cfig[1]) fig$y <- (dev$y-cfig[3])/(cfig[4]-cfig[3]) plt <- list() plt$x <- (fig$x-cplt[1])/(cplt[2]-cplt[1]) plt$y <- (fig$y-cplt[3])/(cplt[4]-cplt[3]) usr <- list() usr$x <- plt$x*(cusr[2]-cusr[1])+cusr[1] usr$y <- plt$y*(cusr[4]-cusr[3])+cusr[3] tdev <- list() tdev$x <- dev$x*(cdev[2]-cdev[1])+cdev[1] tdev$y <- dev$y*(cdev[4]-cdev[3])+cdev[3] return( list( usr=usr, plt=plt, fig=fig, dev=dev, tdev=tdev ) ) } } ################################################################################ fBasics/R/builtin-testNortest.R0000644000176200001440000000166014263246022016156 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # testNortest ################################################################################ fBasics/R/dist-distErrors.R0000644000176200001440000000306714263246022015260 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ .distStandardErrors <- function(fit, obj, x) { # Add Standard Errors and t-Values: hessian = tsHessian(x = fit$par, fun = obj, y = x, trace = FALSE) colnames(hessian) = rownames(hessian) = names(fit$par) fit$cvar = solve(hessian) fit$se.coef = sqrt(diag(fit$cvar)) if (fit$scale) fit$se.coef = fit$se.coef / fit$scaleParams fit$tval = fit$par/fit$se.coef fit$matcoef = cbind(fit$par, fit$se.coef, fit$tval, 2*(1-pnorm(abs(fit$tval)))) dimnames(fit$matcoef) = list(names(fit$tval), c(" Estimate", " Std. Error", " t value", "Pr(>|t|)")) # Return Value: fit } ################################################################################ fBasics/R/dist-ghtRobMoments.R0000644000176200001440000001023614263246022015704 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtMED Returns true GHT median # ghtIQR Returns true GHT inter quartal range # ghtSKEW Returns true GHT robust skewness # ghtKURT Returns true GHT robust kurtosis ################################################################################ ghtMED <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght median # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Median Q = qght(p=0.5, beta, delta, mu, nu) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ ghtIQR <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght inter quartal range # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Inter Quartile Range Q = numeric() Q[1] = qght(p=0.25, beta, delta, mu, nu) Q[2] = qght(p=0.75, beta, delta, mu, nu) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ ghtSKEW <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght robust ght skewness # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Robust Skewness: Q = numeric() Q[1] = qght(p=0.25, beta, delta, mu, nu) Q[2] = qght(p=0.50, beta, delta, mu, nu) Q[3] = qght(p=0.75, beta, delta, mu, nu) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ ghtKURT <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns true ght robust ght kurtosis # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # ght Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qght(p, beta, delta, mu, nu)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-ghtMode.R0000644000176200001440000000343114263246022014502 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtMode Computes the generalized hyperbolic Student-t mode ################################################################################ ghtMode <- function(beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Generalized Hyperbolic Student-t PDF # Arguments: # beta - skewness parameter # delta - scale parameter # mu - location parameter # nu - shape parameter # Example: # ghtMode() # FUNCTION: # Find Maximum: min = qght(0.01, beta, delta, mu, nu) max = qght(0.99, beta, delta, mu, nu) ans = optimize(f = dght, interval = c(min, max), beta = beta, delta = delta, mu = mu, nu = nu, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/test-scaleTest.R0000644000176200001440000001725714324454314015074 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # scaleTest Performs scale tests on two samples # .ansariTest Ansari-Bradley test for differences in scale # .moodTest Mood test for differences in scale ################################################################################ scaleTest <- function(x, y, method = c("ansari", "mood"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs Scale Tests # FUNCTION: # Test: method = match.arg(method) if (method == "ansari") { ans = .ansariTest(x, y, title = title, description = description) } if (method == "mood") { ans = .moodTest(x, y, title = title, description = description) } # Return Value: ans } # ------------------------------------------------------------------------------ .ansariTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Ansari-Bradley's test for differences in scale # Arguments: # x - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Test: two.sided = .ansari2Test(x = x, y = y, alternative = "two.sided", exact = FALSE, conf.int = TRUE, conf.level = 0.95) less = .ansari2Test(x = x, y = y, alternative = "less", exact = FALSE, conf.int = TRUE, conf.level = 0.95) greater = .ansari2Test(x = x, y = y, alternative = "greater", exact = FALSE, conf.int = TRUE, conf.level = 0.95) two.sided.exact = .ansari2Test(x = x, y = y, alternative = "two.sided", exact = TRUE, conf.int = TRUE, conf.level = 0.95) less.exact = .ansari2Test(x = x, y = y, alternative = "less", exact = TRUE, conf.int = TRUE, conf.level = 0.95) greater.exact = .ansari2Test(x = x, y = y, alternative = "greater", exact = TRUE, conf.int = TRUE, conf.level = 0.95) # Statistic: STATISTIC = c(two.sided$statistic) names(STATISTIC) = "AB" test$statistic = STATISTIC # P Values: PVAL = c( two.sided$p.value, two.sided.exact$p.value, less$p.value, less.exact$p.value, greater$p.value, greater.exact$p.value) names(PVAL) = c( "Alternative Two-Sided ", "Alternative Two-Sided | Exact", "Alternative Less ", "Alternative Less | Exact", "Alternative Greater ", "Alternative Greater | Exact") test$p.values = PVAL # Confidence Levels: CONF.INT = cbind( a = two.sided$conf.int, b = two.sided.exact$conf.int, c = less$conf.int, d = less.exact$conf.int, e = greater$conf.int, f = greater.exact$conf.int) # For Splus compatibility use named a CONF.INT # and dimnames instead of colnames! dimnames(CONF.INT)[[2]] = c( "Two-Sided | Asymptotic ", "Two-Sided | Exact ", "Less | Asymptotic ", "Less | Exact ", "Greater | Asymptotic ", "Greater | Exact ") test$conf.int = CONF.INT # Add: if(is.null(title)) title = "Ansari-Bradley Test for Scale" if(is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ .ansari2Test <- function(x, y, alternative = c("two.sided", "less", "greater"), exact = TRUE, conf.int = FALSE, conf.level = 0.95, ...) { # A function implemented by Diethelm Wuertz # Arguments: # x - numeric vector of data values. # y - numeric vector of data values. # alternative - indicates the alternative hypothesis and must # be one of "two.sided", "greater" or "less". You can specify # just the initial letter. # exact - a logical indicating whether an exact p-value should # be computed. # conf.int - a logical,indicating whether a confidence interval # should be computed. # conf.level - confidence level of the interval. # FUNCTION: # Return Value: ansari.test(x, y, alternative, exact, conf.int, conf.level, ...) } # ------------------------------------------------------------------------------ .moodTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs Mood's two-sample test for a difference in # scale parameters. # Arguments: # x, y - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Notes: # A modified copy originally from R's ctest package Version 1.8.1 # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Check Data: m = length(x) n = length(y) s = m + n if (s < 3) stop("not enough observations") # Statistic: r = rank(c(x, y)) # From R: # z = ((sum((r[1:length(x)] - (s + 1) / 2)^2) - m * (s^2 - 1) / 12) # / sqrt(m * n * (s + 1) * (s + 2) * (s - 2) / 180) ) # To run also under S-Plus use ... a = sum( (r[1:length(x)]-0.5*(s+1))^2 ) - m*(s*s-1)/12 b = sqrt(as.numeric(m) * n * (s + 1) * (s + 2) * (s - 2) / 180) STATISTIC = a/b names(STATISTIC) = "Z" test$statistic = STATISTIC # P Values: p = pnorm(STATISTIC) PVAL = c(2 * min(p, 1 - p), p, 1 - p) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Add: if(is.null(title)) title = "Mood Two-Sample Test of Scale" if(is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/gui-returnSeries.R0000644000176200001440000001166714263246022015440 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GRAPHICAL USER INTERFACE: # returnSeriesGUI pens a GUI for return series plots ################################################################################ returnSeriesGUI <- function(x) { # A function implemented by Diethelm Wuertz # Descriptions: # Opens a GUI for return series plots # Arguments: # x - an uni- or multivariate timeSeries object # FUNCTION: # Check: stopifnot(class(x) == "timeSeries") # Settings: N = ceiling(sqrt(ncol(x))) mfrow = c(N, N) returnSeriesRefreshCode <- function(...) { # Settings: selectedAsset = .tdSliderMenu(no = 1) type = as.integer(.tdSliderMenu(obj.name = "returnSeriesType")) Unit = colnames(x) # Print Basic Return Statistics: if (type == 1) { if (selectedAsset == 0) { print(basicStats(x)) } else { print(basicStats(x[, selectedAsset])) } } # Return Series Plot: if (type == 2) { if (selectedAsset == 0) { par(mfrow = mfrow) seriesPlot(x) } else { par(mfrow = c(1, 1)) seriesPlot(x[, selectedAsset]) } } # Cumulate Return Series Plot if (type == 3) { if (selectedAsset == 0) { par(mfrow = mfrow) seriesPlot(100*exp(colCumsums(x))) } else { par(mfrow = c(1, 1)) seriesPlot(100*exp(colCumsums(x[, selectedAsset]))) abline(h = 100, col = "grey") } } # Histogram Plot: if (type == 4) { if (selectedAsset == 0) { par(mfrow = mfrow) histPlot(x, skipZeros = TRUE) } else { par(mfrow = c(1, 1)) histPlot(x[, selectedAsset], skipZeros = TRUE) } } # Density Plot: if (type == 5) { if (selectedAsset == 0) { par(mfrow = mfrow) densityPlot(x) } else { par(mfrow = c(1, 1)) densityPlot(x[, selectedAsset]) } } # Normal QQ Plot: if (type == 6) { if (selectedAsset == 0) { par(mfrow = mfrow) qqnormPlot(x) } else { par(mfrow = c(1, 1)) qqnormPlot(x[, selectedAsset]) } } } nAssets = dim(x)[2] .tdSliderMenu( returnSeriesRefreshCode, names = c("Selected Asset"), minima = c( 0), maxima = c( nAssets), resolutions = c( 1), starts = c( 0), but.functions = list( function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "1") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "2") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "3") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "4") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "5") returnSeriesRefreshCode()}, function(...){ .tdSliderMenu(obj.name = "returnSeriesType", obj.value = "6") returnSeriesRefreshCode()} ), but.names = c( "1 Basic Return Statistics Table", "2 Return Series Plot", "3 Cumulated Return Series Plot", "4 Histogram Plot", "5 Density Plot", "6 Normal Quantile-Quantile Plot"), title = "Return Series GUI" ) .tdSliderMenu(obj.name = "type", obj.value = "1", no = 1) # Return Value() invisible() } ################################################################################ fBasics/R/dist-fDISTFIT.R0000644000176200001440000000464214324526455014405 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # 'fDISTFIT' S4 Class representation # show.fDISTFIT Prints Results from a Fitted Distribution ################################################################################ setClass("fDISTFIT", representation( call = "call", model = "character", data = "data.frame", fit = "list", title = "character", description = "character" ) ) # ------------------------------------------------------------------------------ setMethod("show", "fDISTFIT", function(object) { ## A function implemented by Diethelm Wuertz ## ## Description: ## Prints Results from a Fitted Distribution ## FUNCTION: ## Title: cat("\nTitle:\n ") cat(object@title, "\n") ## Call: cat("\nCall:\n ") cat(paste(deparse(object@call), sep = "\n", collapse = "\n"), "\n", sep = "") ## Model: cat("\nModel:\n ", object@model, "\n", sep = "") ## Estimate: cat("\nEstimated Parameter(s):\n") print(object@fit$estimate) ## Description: ## GNB: don't print description if it is "" if(!identical(object@description, "")) { cat("\nDescription:\n ") cat(object@description, "\n") } cat("\n") ## Return Value: invisible() }) # ------------------------------------------------------------------------------ ################################################################################ fBasics/R/stats-interpKrige.R0000644000176200001440000000662614263246022015602 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # krigeInterp Kriges irregularly distributed data points # REQUIRE: DESCRIPTION: # spatial Functions for Kriging and Point Pattern ################################################################################ krigeInterp <- function(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE, polDegree = 6) { # A function implemented by Diethelm Wuertz # Description: # Kriges Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points spanning the grid # extrap - a logical, if TRUE then the data points are extrapolated. # polDegree - polynomial degree, an integer ranging between 1 and 6. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Example: # x <- runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans <- krigeInterp(x, y, z, extrap = FALSE) # persp(ans, theta = -50, phi = 30, col = "steelblue") # Note: # Requires recommended R Package "spatial" # FUNCTION: # Arguments: if (is.data.frame(x)) x <- as.matrix.data.frame(x) else if (is.list(x)) x <- matrix(unlist(x), ncol = 3) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolate: krige <- spatial::surf.gls(np = polDegree, covmod = spatial::expcov, x = x, y = y, z = z, d = 0.5, alpha = 1) ans <- spatial::prmat(krige, xl = min(xo), xu = max(xo), yl = min(yo), yu = max(yo), n = gridPoints-1) # Extrapolate ? # - this should be done more efficiently if (!extrap) { E <- akimaInterp(x = x, y = y, z = z, gridPoints = gridPoints, extrap = extrap) ans$z[is.na(E$z)] <- NA } class(ans) <- "gridData" # Return Value: ans } ################################################################################ fBasics/R/builtin-kweightsSandwhich.R0000644000176200001440000000674214263246022017304 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .kweigths ################################################################################ # Code borrowed from # R's contributed package "sandwich" written by Thomas Lumley # and Achim Zeileis. # Rmetrics: # To run these functions under Rmetrics we have them implemented # here as a builtin. # The reason is that the dependences in the original package requires # zoo which may create conflicts with Rmetrics timeDate/timeSeries. # Package: sandwich # Version: 2.2-1 # Date: 2009-02-05 # Title: Robust Covariance Matrix Estimators # Author: Thomas Lumley, Achim Zeileis # Maintainer: Achim Zeileis # Description: Model-robust standard error estimators for cross-sectional, # time series and longitudinal data. # LazyLoad: yes # LazyData: yes # Depends: R (>= 2.0.0), stats, zoo # Suggests: car, lmtest, strucchange, AER, survival, MASS # Imports: stats # License: GPL-2 # Copyright: (C) 2004 Thomas Lumley and Achim Zeileis # ------------------------------------------------------------------------------ .kweights <- function(x, kernel = c("Truncated", "Bartlett", "Parzen", "Tukey-Hanning", "Quadratic Spectral"), normalize = FALSE) { kernel <- match.arg(kernel) if(normalize) { ca <- switch(kernel, "Truncated" = 2, "Bartlett" = 2/3, "Parzen" = .539285, "Tukey-Hanning" = 3/4, "Quadratic Spectral" = 1) } else ca <- 1 switch(kernel, "Truncated" = { ifelse(ca * x > 1, 0, 1) }, "Bartlett" = { ifelse(ca * x > 1, 0, 1 - abs(ca * x)) }, "Parzen" = { ifelse(ca * x > 1, 0, ifelse(ca * x < 0.5, 1 - 6 * (ca * x)^2 + 6 * abs(ca * x)^3, 2 * (1 - abs(ca * x))^3)) }, "Tukey-Hanning" = { ifelse(ca * x > 1, 0, (1 + cos(pi * ca * x))/2) }, "Quadratic Spectral" = { y <- 6 * pi * x/5 ifelse(x < 1e-4, 1, 3 * (1/y)^2 * (sin(y)/y - cos(y))) }) } ################################################################################ fBasics/R/dist-nigRobMoments.R0000644000176200001440000000631314263246022015700 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigMED Returns true NIG median # nigIQR Returns true NIG inter quartal range # nigSKEW Returns true NIG robust skewness # nigKURT Returns true NIG robust kurtosis ################################################################################ nigMED <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG median # FUNCTION: # NIG Median: Q = .qnigC(p=0.5, alpha, beta, delta, mu) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ nigIQR <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG inter quartal range # FUNCTION: # NIG Inter Quartile Range: Q = numeric() Q[1] = .qnigC(p=0.25, alpha, beta, delta, mu) Q[2] = .qnigC(p=0.75, alpha, beta, delta, mu) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ nigSKEW <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG robust skewness # FUNCTION: # NIG Robust Skewness: Q = numeric() Q[1] = .qnigC(p=0.25, alpha, beta, delta, mu) Q[2] = .qnigC(p=0.50, alpha, beta, delta, mu) Q[3] = .qnigC(p=0.75, alpha, beta, delta, mu) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ nigKURT <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true NIG robust kurtosis # FUNCTION: # NIG Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, .qnigC(p, alpha, beta, delta, mu)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/matrix-hilbert.R0000644000176200001440000000252014263246022015103 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # GENERATION: DESCRIPTION: # hilbert Creates a Hilbert matrix ################################################################################ hilbert <- function(n) { # A function implemented by Diethelm Wuertz # Description: # Creates a Hilbert matrix # Arguments: # n - the dimension of the square matrix # FUNCTION: # Hilbert: i = 1:n X = 1 / outer(i - 1, i, "+") # Return Value: X } ################################################################################ fBasics/R/utils-randLCG.R0000644000176200001440000001242114263246022014561 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: PORTABLE INNOVATIONS: # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # FUNCTION: PORTABLE RVS GENERATORS: # runif.lcg Generates uniform linear congruational rvs # rnorm.lcg Generates normal linear congruational rvs # rt.lcg Generates Student-t linear congruational rvs ################################################################################ ################################################################################ # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # .lcg.seed = 4711 # ------------------------------------------------------------------------------ set.lcgseed <- function(seed = 4711) { # A function implemented by Diethelm Wuertz # Description: # Sets the random seed for the linear congruential # random number generator # Notes: # A Simple Portable Random Number Generator for Use in R and Splus # Use this generator only for comparisons of Programs in R and Splus !!! # Method: A linear congruential generator with # LCG(a=13445, c=0, m=2^31-1, X0) # Note, this is a random number generator which passes the bitwise # randomness test. # Reference: # http://csep1.phy.ornl.gov/rn/node13.html # N. S. Altman. ``Bitwise Behavior of Random Number Generators,'' # SIAM J. Sci. Stat. Comput., 9(5), September, pps. 941-949, 1988 # Example: # set.lcgseed(4711) # cbind(runif.lcg(100), rnorm.lcg(100), rt.lcg(100, df=4)) # FUNCTION: # Return Value: # .lcg.seed <<- seed setRmetricsOptions(lcg.seed = seed) } # ------------------------------------------------------------------------------ get.lcgseed <- function() { # A function implemented by Diethelm Wuertz # Description: # Returns the random seed for the linear congruential # random number generator # FUNCTION # Return Value: getRmetricsOptions("lcg.seed") } ################################################################################ # runif.lcg Uniform linear congruational generator # rnorm.lcg Normal linear congruational generator # rt.lcg Student-t linear congruational generator runif.lcg <- function(n, min = 0, max = 1) { # A function implemented by Diethelm Wuertz # Description: # A linear congruential generator for uniform distributed # random numbers # Notes: # Important - Use this generator only for comparisons of # Programs in R and SPlus (and not for production) !!! # Portable Random Numbers: # A linear congruential generator # LCG(a=13445, c=0, m=2^31-1, X0) # This is a random number generator which # passes the bitwise randomness test # References: # http://csep1.phy.ornl.gov/rn/node13.html # N. S. Altman. ``Bitwise Behavior of Random Number Generators,'' # SIAM J. Sci. Stat. Comput., 9(5), September, pps. 941-949, 1988 # FUNCTION: # Initialize: # if(!exists(".lcg.seed")) .lcg.seed <<- 4711 # Generate: r.lcg = rep(0, times = n) a = 13445 c = 0 m = 2^31-1 for (i in 1:n) { lcg.seed <- getRmetricsOptions("lcg.seed") setRmetricsOptions(lcg.seed = (a * lcg.seed + c) %% m) r.lcg[i] = getRmetricsOptions("lcg.seed") / m } r.lcg = (max-min)*r.lcg + min # Return Value: r.lcg } # ------------------------------------------------------------------------------ rnorm.lcg <- function(n, mean = 0, sd = 1) { # A function implemented by Diethelm Wuertz # Description: # A linear congruential generator for normal distributed # random numbers # FUNCTION: # This is slow, but portable between R and SPlus (qnorm(runif.lcg(n = n, min = 0, max = 1)) - mean)/sd^2 } # ------------------------------------------------------------------------------ rt.lcg <- function(n, df) { # A function implemented by Diethelm Wuertz # Description: # A linear congruential generator for Sudent-t distributed # random numbers # FUNCTION: # This is slow, but portable between R and SPlus qt(runif.lcg(n = n, min = 0, max = 1), df = df) } ################################################################################ fBasics/R/stats-sampleLmoments.R0000644000176200001440000000472414263246022016314 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # sampleLmoments Computes sample L-moments ################################################################################ sampleLmoments <- function(x, rmax=4) { # A function implemented by Diethelm Wuertz # Description # Computes sample L-moments # Note: # This function is borrowed from package ... # Author: # FUNCTION: # L Moments: data <- as.matrix(x) n <- dim(data)[1] p <- dim(data)[2] x <- array(, c(p, n)) L <- array(, c(p, rmax)) for (i in 1:p) x[i, ] <- sort(data[, i]) if (rmax == 1) return(rowMeans(x)) bcoef <- array(, c(rmax, n)) bcoefm <- array(, c(rmax, p, n)) b <- array(, c(p, rmax)) bcoef[1, ] <- seq(0, 1, by = (1/(n-1))) bcoefm[1, , ] <- t(array(rep(bcoef[1, ], p), c(n, p))) b[, 1] <- rowMeans(x) b[, 2] <- rowMeans(bcoefm[1, , ] * x) L[, 1] = b[, 1] if (rmax > 2) { for (r in 2:(rmax-1)) { rr <- r+1 bcoef[r, ]<-bcoef[r-1,]*seq((-(r-1)/(n-r)),1, by = (1/(n-r))) bcoefm[r, , ]<-t(array(rep(bcoef[r,],p),c(n, p))) b[, rr] <- rowMeans(bcoefm[r, , ]*x) } } for (r in 1:(rmax-1)) { L[, r+1] <- 0 for (k in 0:r) { kk <- k+1 L[, r+1] <- L[, r+1]+(-1)^(r-k)*gamma(r+k+1) / (gamma(k+1)^2) / gamma(r-k+1)*b[, kk] } } L = as.vector(L) names(L) = paste("L", 1:rmax, sep = "") # Return Value: L } ################################################################################ fBasics/R/utils-listFunctions.R0000644000176200001440000000475014263246022016161 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # listFunctionss Lists all functions in a package # countFunctions Counts all functions in a package ################################################################################ listFunctions <- function(package, character.only = FALSE) { # A function implemented by Diethelm Wuertz & Yohan Chalabi # Description: # Lists all functions in a package # Arguments: # package - a character string, the name of the Package # Author: # Original code from B. Ripley # FUNCTION: # List: if (!character.only) package <- as.character(substitute(package)) pkgNm <- paste("package", package, sep = ":") ## return if(!is.na(match(pkgNm, search())) || require(package, character.only = TRUE, quietly = TRUE)) { nm <- ls(env <- as.environment(pkgNm), all.names = TRUE) nm[unlist(lapply(nm, function(n) exists(n, where = env, mode = "function", inherits = FALSE)))] } else { character(0) } } # ------------------------------------------------------------------------------ countFunctions <- function(package, character.only = FALSE) { # A function implemented by Diethelm Wuertz & Yohan Chalabi # Description: # Counts all functions in a package # Arguments: # package - a character string, the name of the Package # FUNCTION: # Count: if (!character.only) package <- as.character(substitute(package)) ans = length(listFunctions(package, character.only = TRUE)) names(ans) = package # Return Value: ans } ################################################################################ fBasics/R/dist-hypMoments.R0000644000176200001440000000715314650724111015263 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # hypMean Returns true hyperbolic mean # hypVar Returns true hyperbolic variance # hypSkew Returns true hyperbolic skewness # hypKurt Returns true hyperbolic kurtosis # FUNCTION: DESCRIPTION: # hypMoments Returns true hyperbolic moments ################################################################################ hypMean <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic mean # FUNCTION: # Return Value: ghMean(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypVar <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic variance # FUNCTION: # Return Value: ghVar(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypSkew <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic skewness # FUNCTION: # Return Value: ghSkew(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypKurt <- function(alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Description: # Returns true hyperbolic kurtosis # FUNCTION: # Return Value: ghKurt(alpha, beta, delta, mu, lambda=1) } # ------------------------------------------------------------------------------ hypMoments <- function(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0) { # A function implemented by Diethelm Wuertz # Modified by Georgi N. Boshnakov # Descriptions: # Returns true moments of the hyperbolic distribution # FUNCTION: # Settings: type <- match.arg(type) # Moments: lambda = 1 ans <- if (type == "raw") { .ghRawMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { .ghCentralMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { .ghMuMoments(order, alpha, beta, delta, mu, lambda) } names(ans) <- paste0("m", order, type) # Return Value: ans } ################################################################################ fBasics/R/dist-normFit.R0000644000176200001440000000605714324527027014545 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .normFit Fits parameters of a Normal density ################################################################################ # normFit is now in fBasics # ------------------------------------------------------------------------------ .normFit <- function(x, doplot = TRUE, span = "auto", title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Return Maximum log-likelihood estimated # Paramters for Normal Distribution # Notes: # Function Calls: nlminb(), density() # The function normFit() can be found in the Rmetrics # chapter GarchDistributions. # FUNCTION: # Transform: x.orig = x x = as.vector(x) # Settings: CALL = match.call() # MLE: N = length(x) mean = sum(x)/N sd = sqrt(sum((x-mean)^2)/N) # Optional Plot: if (doplot) { if (span == "auto") { span.min = qnorm(0.001, mean, sd) span.max = qnorm(0.999, mean, sd) span = seq(span.min, span.max, length = 100) } par(err = -1) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnorm(span, mean, sd) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NORMAL: Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") if (exists("grid")) grid() } # Add Title and Description: if (is.null(title)) title = "Normal Parameter Estimation" if (is.null(description)) description = "" # Fit: fit = list( estimate = c(mean = mean, sd = sd), minimum = sum(log(dnorm(x, mean, sd))), code = NA) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = "" ) } # ------------------------------------------------------------------------------ nFit <- .normFit ################################################################################ fBasics/R/gui-ghSlider.R0000644000176200001440000000632714263246022014504 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghSlider Displays generalized hyperbolic distribution function ################################################################################ ghSlider <- function() { # A function implemented by Diethelm Wuertz # Generalized Hyperbolic Distribution: # dhyp(x, alpha = 1, beta = 0, delta = 1, lamba = 1) # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) delta = .sliderMenu(no = 4) mu = .sliderMenu(no = 5) lambda = .sliderMenu(no = 6) # Plot Data: xmin = round(qgh(0.01, alpha, beta, delta, mu, lambda), digits = 2) xmax = round(qgh(0.99, alpha, beta, delta, mu, lambda), digits = 2) s = seq(xmin, xmax, length = N) y1 = dgh(s, alpha, beta, delta, mu, lambda) y2 = pgh(s, alpha, beta, delta, mu, lambda) main1 = paste("GH Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "delta = ", as.character(delta), " | ", "mu = ", as.character(mu), "|", "lambda = ", as.character(lambda) ) main2 = paste("GH Probability\n", "xmin 0.01% = ", as.character(xmin), " | ", "xmax 0.99% = ", as.character(xmax)) # Frame par(mfrow = c(2, 1), cex = 0.7) # Density: plot(s, y1, type = "l", xlim = c(xmin, xmax), col = "steelblue") abline (h = 0, lty = 3) title(main = main1) # Probability: plot(s, y2, type = "l", xlim = c(xmin, xmax), ylim = c(0, 1), col = "steelblue" ) abline(h = 0.0, lty = 3) abline(h = 1.0, lty = 3) abline(h = 0.5, lty = 3) abline(v = mu, lty = 3, col = "red") title(main = main2) # Reset Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "N","alpha","beta","delta", "mu","lambda"), minima = c( 50, 0.00, -2.00, 0.00, -5.0, -4), maxima = c(1000, 2.00, +2.00, 5.00, +5.0, 4), resolutions = c( 50, 0.20, 0.20, 1.00, 1.0, 0.5), starts = c( 50, 1.00, 0.00, 1.00, 0.0, 1)) } ################################################################################ fBasics/R/dist-ghMoments.R0000644000176200001440000003125414650724111015060 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghMean Returns true GH mean # ghVar Returns true GH variance # ghSkew Returns true GH skewness # ghKurt Returns true GH kurtosis # FUNCTION: DESCRIPTION: # ghMoments Returns true GH moments # FUNCTION: UTILITY FUNCTION: # .aRecursionGH Computes the moment coefficients a recursively # .besselZ Computes Bessel/Power Function ratio # FUNCTION: MOMENTS ABOUT MU: # .ghMuMoments Computes mu-moments from formula # .ghMuMomentsIntegrated Computes mu-moments by integration # .checkGHMuMoments Checks mu-moments # FUNCTION: MOMENTS ABOUT ZERO: # .ghRawMoments Computes raw moments from formula # .ghRawMomentsIntegrated Computes raw moments by Integration # .checkGHRawMoments Checks raw moments # FUNCTION: MOMENTS ABOUT MEAN: # .ghCentralMoments Computes central moments from formula ################################################################################ ghMean <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: zeta = delta * sqrt(alpha*alpha-beta*beta) mean = mu + beta * delta^2 * .kappaGH(zeta, lambda) c(mean = mean) } # ------------------------------------------------------------------------------ ghVar <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: var = .ghCentralMoments(k=2, alpha, beta, delta, mu, lambda)[[1]] c(var = var) } # ------------------------------------------------------------------------------ ghSkew <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Moments: k2 = .ghCentralMoments(k=2, alpha, beta, delta, mu, lambda)[[1]] k3 = .ghCentralMoments(k=3, alpha, beta, delta, mu, lambda)[[1]] # Return Value: skew = k3/(k2^(3/2)) c(skew = skew) } # ------------------------------------------------------------------------------ ghKurt <- function(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # FUNCTION: # Moments: k2 = .ghCentralMoments(k=2, alpha, beta, delta, mu, lambda)[[1]] k4 = .ghCentralMoments(k=4, alpha, beta, delta, mu, lambda)[[1]] # Return Value: kurt = k4/k2^2 - 3 c(kurt = kurt) } ################################################################################ ghMoments <- function(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) { # A function implemented by Diethelm Wuertz # Modified by Georgi N. Boshnakov # Descriptions: # Returns moments of the GH distribution # FUNCTION: # Settings: type <- match.arg(type) ## Moments: ans <- if (type == "raw") { .ghRawMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "central") { .ghCentralMoments(order, alpha, beta, delta, mu, lambda) } else if (type == "mu") { .ghMuMoments(order, alpha, beta, delta, mu, lambda) } names(ans) <- paste0("m", order, type) # Return Value: ans } ################################################################################ .aRecursionGH <- function(k = 12, trace = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Computes the moment coefficients recursively # Example: # .aRecursionGH() # [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] # [1,] 1 0 0 0 0 0 0 0 0 0 0 0 # [2,] 1 1 0 0 0 0 0 0 0 0 0 0 # [3,] 0 3 1 0 0 0 0 0 0 0 0 0 # [4,] 0 3 6 1 0 0 0 0 0 0 0 0 # [5,] 0 0 15 10 1 0 0 0 0 0 0 0 # [6,] 0 0 15 45 15 1 0 0 0 0 0 0 # [7,] 0 0 0 105 105 21 1 0 0 0 0 0 # [8,] 0 0 0 105 420 210 28 1 0 0 0 0 # [9,] 0 0 0 0 945 1260 378 36 1 0 0 0 #[10,] 0 0 0 0 945 4725 3150 630 45 1 0 0 #[11,] 0 0 0 0 0 10395 17325 6930 990 55 1 0 #[12,] 0 0 0 0 0 10395 62370 51975 13860 1485 66 1 # FUNCTION: # Setting Start Values: a = matrix(rep(0, times = k*k), ncol = k) a[1, 1] = 1 if (k > 1) a[2, 1] = 1 # Compute all Cofficients by Recursion: if (k > 1) { for (d in 2:k) { for (l in 2:d) { a[d,l] = a[d-1, l-1] + a[d-1, l]*(2*l+1-d) } } } rownames(a) = paste("k=", 1:k, sep = "") colnames(a) = paste("l=", 1:k, sep = "") # Trace: if (trace) { cat("\n") print(a) cat("\n") } for (K in 1:k) { L = trunc((K+1)/2):K M = 2*L-K s1 = as.character(a[K, L]) s2 = paste("delta^", 2*L, sep = "") s3 = paste("beta^", M, sep = "") s4 = paste("Z_lambda+", L, sep = "" ) if (trace) { cat("k =", K, "\n") print(s1) print(s2) print(s3) print(s4) cat("\n") } } if (trace) cat("\n") # Return Value: list(a = a[k, L], delta = 2*L, beta = M, besselOffset = L) } # ------------------------------------------------------------------------------ .besselZ <- function(x, lambda) { # A function implemented by Diethelm Wuertz # Description: # Computes Bessel/Power Function ratio # Ratio: ratio = besselK(x, lambda)/x^lambda # Return Value: ratio } ################################################################################ .ghMuMoments <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu moments from formula # Note: # mu is not used. # FUNCTION: # Check parameters: if (lambda >= 0) stopifnot(abs(beta) < alpha) if (lambda < 0) stopifnot(abs(beta) <= alpha) if (lambda > 0) stopifnot(delta >= 0) if (lambda <= 0) stopifnot(delta > 0) zeta = delta*sqrt(alpha^2-beta^2) cLambda = 1/.besselZ(zeta, lambda) a = .aRecursionGH(k) coeff = a$a deltaPow = a$delta betaPow = a$beta besselIndex = lambda + a$besselOffset M = coeff * delta^deltaPow * beta^betaPow * .besselZ(zeta, besselIndex) ans = cLambda * sum(M) attr(ans, "M") <- M # Return Value: ans } # ------------------------------------------------------------------------------ .ghMuMomentsIntegrated <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu moments by integration # FUNCTION: # Check parameters: if (lambda >= 0) stopifnot(abs(beta) < alpha) if (lambda < 0) stopifnot(abs(beta) <= alpha) if (lambda > 0) stopifnot(delta >= 0) if (lambda <= 0) stopifnot(delta > 0) mgh <- function(x, k, alpha, beta, delta, lambda) { x^k * dgh(x, alpha, beta, delta, mu=0, lambda) } M = integrate(mgh, -Inf, Inf, k = k, alpha = alpha, beta = beta, delta = delta, lambda = lambda, subdivisions = 1000, rel.tol = .Machine$double.eps^0.5)[[1]] # Return Value: M } # ------------------------------------------------------------------------------ .checkGHMuMoments <- function(K = 10) { # A function implemented by Diethelm Wuertz # Description: # Checks mu moments # Example: # .checkGHMuMoments() # FUNCTION: # Compute Raw Moments: mu = NULL for (k in 1:K) { mu = c(mu, .ghMuMoments(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(mu) = 1:K int = NULL for (k in 1:K) { int = c(int, .ghMuMomentsIntegrated(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(int) = NULL # Return Value: rbind(mu, int) } # ------------------------------------------------------------------------------ .ghRawMoments <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu-moments from formula # FUNCTION: # Settings: binomCoeff = choose(k, 0:k) # Compute Mu Moments: M = 1 for (l in 1:k) M = c(M, .ghMuMoments(l, alpha, beta, delta, mu, lambda)) muPower = mu^(k:0) muM = binomCoeff * muPower * M # Return Value: sum(muM) } # ------------------------------------------------------------------------------ .ghRawMomentsIntegrated <- function(k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Computes mu-moments by integration # FUNCTION: # Check parameters: if (lambda >= 0) stopifnot(abs(beta) < alpha) if (lambda < 0) stopifnot(abs(beta) <= alpha) if (lambda > 0) stopifnot(delta >= 0) if (lambda <= 0) stopifnot(delta > 0) # Compute Mu Moments by Integration: mgh <- function(x, k, alpha, beta, delta, mu, lambda) { x^k * dgh(x, alpha, beta, delta, mu, lambda) } M = integrate(mgh, -Inf, Inf, k = k, alpha = alpha, beta = beta, delta = delta, mu = mu, lambda = lambda, subdivisions = 1000, rel.tol = .Machine$double.eps^0.5)[[1]] # Return Value: M } # ------------------------------------------------------------------------------ .checkGHRawMoments <- function(K = 10) { # A function implemented by Diethelm Wuertz # Description: # Checks raw-moments # Example: # .checkGHRawMoments() # FUNCTION: raw = NULL for (k in 1:K) { raw = c(raw, .ghRawMoments(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(raw) = 1:K int = NULL for (k in 1:K) { int = c(int, .ghRawMomentsIntegrated(k, alpha = 1.1, beta = 0.1, delta = 0.9, mu = 0.1)) } names(int) = NULL # Return Value: rbind(raw,int) } ################################################################################ .ghCentralMoments <- function (k = 4, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { zeta = delta * sqrt(alpha*alpha-beta*beta) mean = mu + beta * delta^2 * .kappaGH(zeta, lambda) M = 1 for (i in 1:k) M = c(M, .ghMuMoments(i, alpha, beta, delta, mu, lambda)) binomCoeff <- choose(k, 0:k) centralPower <- (mu - mean)^(k:0) centralM <- binomCoeff * centralPower * M sum(centralM) } ################################################################################ fBasics/R/dist-normRobMoments.R0000644000176200001440000000723414263246022016101 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # normMED Returns true normal median # normIQR Returns true normal inter quartal range # normSKEW Returns true normal robust skewness # normKURT Returns true normal robust kurtosis ################################################################################ normMED <- function(mean =0, sd = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh median # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # FUNCTION: # Median: Q = qnorm(p=0.5, mean, sd) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ normIQR <- function(mean=0, sd=1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh inter quartal range # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # FUNCTION: # Inter Quartile Range: Q = numeric() Q[1] = qnorm(p=0.25, mean, sd) Q[2] = qnorm(p=0.75, mean, sd) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ normSKEW <- function(mean=0, sd=1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh skewness # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # FUNCTION: # Robust Skewness: Q = numeric() Q[1] = qnorm(p=0.25, mean, sd) Q[2] = qnorm(p=0.50, mean, sd) Q[3] = qnorm(p=0.75, mean, sd) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ normKURT <- function(mean=0, sd=1) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh kurtosis # Arguments: # mean - a numeric value, the location parameter # sd - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qnorm(p, mean, sd)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/stats-sampleRobMoments.R0000644000176200001440000000552514263246022016603 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # sampleMED Returns sample median # sampleIQR Returns sample inter quartal range # sampleSKEW Returns robust sample skewness # sampleKURT Returns robust sample kurtosis ################################################################################ sampleMED <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns sample median # FUNCTION: # Sample Median: x = as.vector(x) med = c(MED = quantile(x, 0.50)[[1]]) # Return Value: med } # ------------------------------------------------------------------------------ sampleIQR = function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns sample inter quartal range # FUNCTION: # Sample Inter Quartile Range x = as.vector(x) iqr = c(IQR = diff(quantile(x, c(0.25, 0.75)))[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ sampleSKEW = function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns robust sample skewness # FUNCTION: # Robust Sample Skew x = as.vector(x) q = quantile(x, c(0.25, 0.5, 0.75)) skew = c(SKEW = q[[3]] + q[[1]] - 2*q[[2]]) / (q[[3]]-q[[1]]) # Return Value: skew } # ------------------------------------------------------------------------------ sampleKURT = function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns robust sample kurtosis # FUNCTION: # Robust Sample Kurtosis x = as.vector(x) q = quantile(x, (1:7)/8) kurt = c(KURT = (q[[7]] - q[[5]] + q[[3]] - q[[1]]) / (q[[6]] - q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/utils-colIds.R0000644000176200001440000000630514263246022014530 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # GENERATION: DESCRIPTION: # colIds Retrieves column names of a matrix-like object # rowIds Retrieves row names of a matrix-like object # colIds<- Sets column names of a matrix-like object # rowIds<- Sets row names of a matrix-like object ################################################################################ colIds <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Retrieves row names of a matrix-like object # FUNCTION: # Convert to Matrix x = as.matrix(x) # Return Value: colnames(x, ...) } # ------------------------------------------------------------------------------ rowIds <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Retrieves row names of a matrix-like object # FUNCTION: # Convert to Matrix x = as.matrix(x) # Return Value: rownames(x, ...) } # ------------------------------------------------------------------------------ "colIds<-" <- function(x, value) { # A function implemented by Diethelm Wuertz # Description: # Sets column names of a matrix-like object # FUNCTION: # Column Names: dn = dimnames(x) if(is.null(dn)) { if(is.null(value)) return(x) if((nd = length(dim(x))) < 2) stop("Object has less than two dimensions") dn = vector("list", nd) } if(length(dn) < 2) stop("Object has less than two dimensions") if(is.null(value)) dn[2] = list(NULL) else dn[[2]] = value dimnames(x) = dn # Return Value: x } # ------------------------------------------------------------------------------ "rowIds<-" <- function(x, value) { # A function implemented by Diethelm Wuertz # Description: # Sets row names of a matrix-like object # FUNCTION: # Row names: dn = dimnames(x) if(is.null(dn)) { if(is.null(value)) return(x) if((nd = length(dim(x))) < 1) stop("attempt to set rownames on object with no dimensions") dn = vector("list", nd) } if(length(dn) < 1) stop("attempt to set rownames on object with no dimensions") if(is.null(value)) dn[1] = list(NULL) else dn[[1]] = value dimnames(x) = dn # Return Value: x } ################################################################################ fBasics/R/matrix-vech.R0000644000176200001440000000373414263246022014407 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # Function: DESCRIPTION: # vec Stacks a matrix as column vector # vech Stacks a lower triangle matrix ################################################################################ vec <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Stacks a matrix as column vector # Details: # vec(X) = (X11, X21, ..., XN1, X12, X22, ..., XNN)' # Note: # Example for a 3x3 Matrix: # X11, X21, X22, X31, X32, X33 # FUNCTION: # Return Value: t(t(as.vector(x))) } # ------------------------------------------------------------------------------ vech <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Stacks a lower triangle matrix # Details: # vech is the operator that stacks the lower triangle # of a NxN matrix as an N(N+1)/2x1 vector: # vech(X) =(X11, X21, X22, X31, ..., XNN)' # Note: # Example for a 3x3 Matrix: # X11, X21, X22, X31, X32, X33 # FUNCTION: # Return Value: t(x[!upper.tri(x)]) } ################################################################################ fBasics/R/builtin-gmmGmm.R0000644000176200001440000004454414264011647015055 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .gmm # .HAC # .weightsAndrews2 # .bwAndrews2 # .summary.gmm # .lintest ################################################################################ # Code borrowed from # R's contributed package "gmm" written by Pierre Chausse. # Rmetrics: # Note that gmm is not available on Debian as of 2009-04-28. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. # We also made modifications for tailored usage with Rmetrics. # Note that the dependences in the original package requires zoo # which may create conflicts with Rmetrics timeDate/timeSeries. # Package: gmm # Version: 1.0-4 # Date: 2009-03-10 # Title: Generalized Method of Moments and Generalized Empirical Likelihood # Author: Pierre Chausse # Maintainer: Pierre Chausse # Description: It is a complete suite to estimate models based on moment # conditions. It includes the two step Generalized method of moments # (GMM) of Hansen(1982), the iterated GMM and continuous updated # estimator (CUE) of Hansen-Eaton-Yaron(1996) and several methods that # belong to the Generalized Empirical Likelihood (GEL) family of estimators, # as presented by Smith(1997), Kitamura(1997), Newey-Smith(2004) and # Anatolyev(2005). # Depends: R (>= 2.0.0), sandwich, tseries, mvtnorm # Imports: stats # License: GPL (>= 2) # ------------------------------------------------------------------------------ .gmm <- function(g, x, t0=NULL, gradv=NULL, type=c("twoStep","cue","iterative"), wmatrix = c("optimal","ident"), vcov=c("HAC","iid"), kernel=c("Quadratic Spectral","Truncated", "Bartlett", "Parzen", "Tukey-Hanning"),crit=10e-7,bw = .bwAndrews2, prewhite = FALSE, ar.method = "ols", approx="AR(1)",tol = 1e-7, itermax=100,intercept=TRUE,optfct=c("optim","optimize"), ...) { type <- match.arg(type) kernel <- match.arg(kernel) vcov <- match.arg(vcov) wmatrix <- match.arg(wmatrix) optfct <- match.arg(optfct) typeg=0 if (is(g,"formula")) { typeg=1 dat <- .get_dat(g,x,intercept=intercept) x <- dat$x g <- function(tet,x,ny=dat$ny,nh=dat$nh,k=dat$k) { tet <- matrix(tet,ncol=k) if (intercept) { e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } if (!intercept) e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e*x[,ny+k+1] if (nh > 1) { for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } return(gt) } gradv <- function(x,ny=dat$ny,nh=dat$nh,k=dat$k) { dgb <- -(t(x[,(ny+k+1):(ny+k+nh)])%*%x[,(ny+1):(ny+k)])%x% diag(rep(1,ny))/nrow(x) return(dgb) } tetlin <- function(x,w,ny=dat$ny,nh=dat$nh,k=dat$k) { n <- nrow(x) ym <- as.matrix(x[,1:ny]) xm <- as.matrix(x[,(ny+1):(ny+k)]) hm <- as.matrix(x[,(ny+k+1):(ny+k+nh)]) whx <- solve(w,(crossprod(hm,xm)%x%diag(ny))) wvecyh <- solve(w,matrix(crossprod(ym,hm),ncol=1)) dg <- gradv(x) xx <- crossprod(dg,whx) par <- solve(xx,crossprod(dg,wvecyh)) gb <- matrix(colSums(g(par,x))/n,ncol=1) value <- crossprod(gb,solve(w,gb)) res <- list(par=par,value=value) return(res) } } if (optfct == "optimize") { n = nrow(g(t0[1],x)) q = ncol(g(t0[1],x)) k = 1 k2 <- k df <- q-k } else { if (typeg) { k <- dat$k k2 <- k*dat$ny n <- nrow(x) q <- dat$ny*dat$nh df <- q-k*dat$ny } else { n = nrow(g(t0,x)) q = ncol(g(t0,x)) k = length(t0) k2 <- k df <- q-k } } obj1 <- function(thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) obj <- crossprod(gbar,solve(w,gbar)) return(obj) } iid <- function(thet) { gt <- g(thet,x) v <- crossprod(gt,gt)/n return(v) } Gf <- function(thet) { myenv <- new.env() assign('x',x,envir=myenv) assign('thet',thet,envir=myenv) barg <- function(x,thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) return(gbar) } G <- attr(numericDeriv(quote(barg(x,thet)),"thet",myenv),"gradient") return(G) } if (q == k2 | wmatrix == "ident") { if (typeg) { w <- diag(q) res <- tetlin(x,w) z = list(par=res$par,objective=res$value) } else { w=diag(rep(1,q)) if (optfct == "optim") res <- optim(t0,obj1, ...) else { res <- optimize(obj1,t0, ...) res$par <- res$minimum res$value <- res$objective } z = list(par=res$par,objective=res$value) } } else { if (type=="twoStep") { w=diag(rep(1,q)) if (typeg) { res1 <- tetlin(x,w) } else { if (optfct == "optim") res1 <- optim(t0,obj1, ...) else { res1 <- optimize(obj1,t0, ...) res1$par <- res1$minimum res1$value <- res1$objective } } if (vcov == "iid") w <- iid(res1$par) if (vcov == "HAC") w <- .HAC(g(res1$par,x), kernel=kernel, bw=bw, prewhite=prewhite,ar.method=ar.method,approx=approx,tol=tol) if (typeg) { res2 <- tetlin(x,w) } else { if (optfct == "optim") res2 <- optim(res1$par,obj1, ...) else { res2 <- optimize(obj1,t0, ...) res2$par <- res2$minimum res2$value <- res2$objective } } z = list(par=res2$par,objective=res2$value) } if (type=="cue") { obj_cue <- function(thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) if (vcov == "iid") w2 <- iid(thet) if (vcov == "HAC") w2 <- .HAC(g(thet,x), kernel=kernel, bw=bw, prewhite=prewhite,ar.method=ar.method,approx=approx,tol=tol) obj <- crossprod(gbar,solve(w2,gbar)) return(obj) } if (typeg) { if (is.null(t0)) t0 <- tetlin(x,diag(rep(1,q)))$par if (optfct == "optim") { res2 <- optim(t0,obj_cue, ...) } else { res2 <- optimize(obj_cue,t0, ...) res2$par <- res2$minimum res2$value <- res2$objective } } else { if (optfct == "optim") res2 <- optim(t0,obj_cue, ...) else { res2 <- optimize(obj_cue,t0, ...) res2$par <- res2$minimum res2$value <- res2$objective } } z = list(par=res2$par,objective=res2$value) } if (type=="iterative") { w=diag(rep(1,q)) if (typeg) res <- tetlin(x,w) else { if (optfct == "optim") res <- optim(t0,obj1, ...) else { res <- optimize(obj1,t0, ...) res$par <- res$minimum res$value <- res$objective } } ch <- 100000 j <- 1 while(ch>crit) { tet <- res$par if (vcov == "iid") w2 <- iid(tet) if (vcov == "HAC") w2 <- .HAC(g(tet,x), kernel=kernel, bw=bw, prewhite=prewhite,ar.method=ar.method,approx=approx,tol=tol) if (typeg) res <- tetlin(x,w2) else { if (optfct == "optim") res <- optim(tet,obj1, ...) else { res <- optimize(obj1,t0, ...) res$par <- res$minimum res$value <- res$objective } } ch <- crossprod(abs(tet-res$par)/tet,abs(tet-res$par)/tet) if (j>itermax) { cat("No convergence after ",itermax," iterations") ch <- crit } j <- j+1 } z = list(par=res$par,objective=res$value) } } if (!is.function(gradv)) G <- Gf(z$par) else if (typeg) G <- gradv(x) else G <- gradv(z$par,x) if (vcov == "iid") v <- iid(z$par)/n else v <- .HAC(g(z$par,x), kernel=kernel, bw=bw,prewhite=prewhite, ar.method=ar.method,approx=approx,tol=tol)/n if (wmatrix == "optimal") { z$vcov <- solve(crossprod(G,solve(v,G))) } else { GGG <- solve(crossprod(G),t(G)) z$vcov <- GGG%*%v%*%t(GGG) } z$gt <- g(z$par,x) if (typeg==0) names(z$par) <- paste("Theta[",1:k,"]",sep="") if (typeg == 1) { namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)]) nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)]) if (dat$ny > 1) { namey <- colnames(dat$x[,1:dat$ny]) names(z$par) <- paste(rep(namey,dat$k),"_",rep(namex,rep(dat$ny,dat$k)),sep="") colnames(z$gt) <- paste(rep(namey,dat$nh),"_",rep(nameh,rep(dat$ny,dat$nh)),sep="") } if (dat$ny == 1) { names(z$par) <- namex colnames(z$gt) <- nameh } } dimnames(z$vcov) <- list(names(z$par),names(z$par)) z$df <- df z$k <- k z$n <- n z$met <- type z$kernel <- kernel class(z) <- "gmm" z } # ------------------------------------------------------------------------------ .HAC <- function(x, weights = .weightsAndrews2, bw = .bwAndrews2, prewhite = FALSE, ar.method = "ols", kernel=c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"), approx="AR(1)", tol = 1e-7) { n.orig <- n <- nrow(x) k <- ncol(x) kernel=match.arg(kernel) if(prewhite > 0) { var.fit <- ar(x, order.max = prewhite, demean = FALSE, aic = FALSE, method = ar.method) if(k > 1) D <- solve(diag(ncol(x)) - apply(var.fit$ar, 2:3, sum)) else D <- as.matrix(1/(1 - sum(var.fit$ar))) x <- as.matrix(na.omit(var.fit$resid)) n <- n - prewhite } weights <- weights(x, ar.method = ar.method,kernel=kernel,bw=bw, approx = approx, prewhite = 1, tol = tol) if (length(weights) > n) { warning("more weights than observations, only first n used") weights <- weights[1:n] } utu <- 0.5 * crossprod(x) * weights[1] wsum <- n * weights[1]/2 w2sum <- n * weights[1]^2/2 if (length(weights) > 1) { for (ii in 2:length(weights)) { utu <- utu + weights[ii] * crossprod(x[1:(n - ii + 1), , drop = FALSE], x[ii:n, , drop = FALSE]) wsum <- wsum + (n - ii + 1) * weights[ii] w2sum <- w2sum + (n - ii + 1) * weights[ii]^2 } } utu <- utu + t(utu) if(prewhite > 0) { utu <- crossprod(t(D), utu) %*% t(D) } wsum <- 2 * wsum w2sum <- 2 * w2sum bc <- n^2/(n^2 - wsum) df <- n^2/w2sum rval <- utu/n.orig return(rval) } # ------------------------------------------------------------------------------ .weightsAndrews2 <- function(x, bw = .bwAndrews2, kernel = c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"), approx = c("AR(1)", "ARMA(1,1)"), prewhite = 1, ar.method = "ols", tol = 1e-7, verbose = FALSE) { kernel <- match.arg(kernel) approx=match.arg(approx) if (is.function(bw)) bw <- bw(x, kernel = kernel, prewhite = prewhite, ar.method = ar.method, approx=approx) n <- NROW(x) weights <- .kweights(0:(n - 1)/bw, kernel = kernel) weights <- weights[1:max(which(abs(weights) > tol))] return(weights) } # ------------------------------------------------------------------------------ .bwAndrews2 <- function(x, kernel = c("Quadratic Spectral", "Truncated", "Bartlett", "Parzen", "Tukey-Hanning"), approx = c("AR(1)", "ARMA(1,1)"), prewhite = 1, ar.method = "ols") { kernel <- match.arg(kernel) approx <- match.arg(approx) n <- nrow(x) k <- ncol(x) if (approx == "AR(1)") { fitAR1 <- function(x) { rval <- ar(x, order.max = 1, aic = FALSE, method = "ols") rval <- c(rval$ar, sqrt(rval$var.pred)) names(rval) <- c("rho", "sigma") return(rval) } ar.coef <- apply(x, 2, fitAR1) denum <- sum((ar.coef["sigma", ]/(1 - ar.coef["rho", ]))^4) alpha2 <- sum(4 * ar.coef["rho", ]^2 * ar.coef["sigma", ]^4/(1 - ar.coef["rho", ])^8)/denum alpha1 <- sum(4 * ar.coef["rho", ]^2 * ar.coef["sigma", ]^4/((1 - ar.coef["rho", ])^6 * (1 + ar.coef["rho", ])^2))/denum } else { fitARMA11 <- function(x) { rval <- arima(x, order = c(1, 0, 1), include.mean = FALSE) rval <- c(rval$coef, sqrt(rval$sigma2)) names(rval) <- c("rho", "psi", "sigma") return(rval) } arma.coef <- apply(x, 2, fitARMA11) denum <- sum(((1 + arma.coef["psi", ]) * arma.coef["sigma", ]/(1 - arma.coef["rho", ]))^4) alpha2 <- sum(4 * ((1 + arma.coef["rho", ] * arma.coef["psi", ]) * (arma.coef["rho", ] + arma.coef["psi", ]))^2 * arma.coef["sigma", ]^4/(1 - arma.coef["rho", ])^8)/denum alpha1 <- sum(4 * ((1 + arma.coef["rho", ] * arma.coef["psi", ]) * (arma.coef["rho", ] + arma.coef["psi", ]))^2 * arma.coef["sigma", ]^4/((1 - arma.coef["rho", ])^6 * (1 + arma.coef["rho", ])^2))/denum } rval <- switch(kernel, Truncated = { 0.6611 * (n * alpha2)^(1/5) }, Bartlett = { 1.1447 * (n * alpha1)^(1/3) }, Parzen = { 2.6614 * (n * alpha2)^(1/5) }, "Tukey-Hanning" = { 1.7462 * (n * alpha2)^(1/5) }, "Quadratic Spectral" = { 1.3221 * (n * alpha2)^(1/5) }) return(rval) } # ------------------------------------------------------------------------------ .summary.gmm <- function(object, interval=FALSE, ...) { z <- object se <- sqrt(diag(z$vcov)) par <- z$par tval <- par/se j <- z$objective*z$n ans <- list(met=z$met,kernel=z$kernel,algo=z$algo) names(ans$met) <- "GMM method" names(ans$kernel) <- "kernel for cov matrix" ans$par <- round(cbind(par,se, tval, 2 * pnorm(abs(tval), lower.tail = FALSE)),5) dimnames(ans$par) <- list(names(z$par), c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) ans$J_test <- noquote(paste("Test-J degrees of freedom is ",z$df,sep="")) ans$j <- noquote(cbind(j,ifelse(z$df>0,pchisq(j,z$df,lower.tail = FALSE),"*******"))) dimnames(ans$j) <- list("Test E(g)=0: ",c("J-test","Pz(>j)")) if (interval != FALSE) { zs <- qnorm((1-interval)/2,lower.tail=FALSE) ch <- zs*se ans$interval <- cbind(par-ch,par+ch) dimnames(ans$interval) <- list(names(par),c("Theta_lower","Theta_upper")) } class(ans) <- "summary.gmm" ans } # ------------------------------------------------------------------------------ .lintest <- function(object,R,c) { z <- object dl <- nrow(R) rest <- R%*%z$par - c vcov_par <- if (inherits(z, "gmm")) z$vcov else z$vcov_par vcov <- R%*%vcov_par%*%t(R) h0 <- matrix(rep(NA,nrow(R)),ncol=1) for (i in 1:nrow(R)) { testnames <- names(z$par)[R[i,]!=0] rn <- R[i,][R[i,]!=0] if (rn[1] != 1) h0[i] <- paste(rn[1],"*",testnames[1],sep="") else h0[i] <- testnames[1] if (length(rn) > 1) { for (j in 2:length(rn)) { if (rn[j] >= 0) { if (rn[j] != 1) h0[i] <- paste(h0[i]," + ",rn[j],"*",testnames[j],sep="") else h0[i] <- paste(h0[i]," + ",testnames[j],sep="") } else { if (abs(rn[j]) != 1) h0[i] <- paste(h0[i]," - ",abs(rn[j]),"*",testnames[j],sep="") else h0[i] <- paste(h0[i]," - ",testnames[j],sep="") } } } h0[i] <- paste(h0[i]," = ",c[i],sep="") } rh <- solve(vcov,rest) ans <- list(description <- "Wald test for H0: R(Theta)=c") ans$H0 <- noquote(h0) colnames(ans$H0) <- "Null Hypothesis" wt <- crossprod(rest,rh) pv <- pchisq(wt,dl,lower.tail=FALSE) res <- cbind(wt,pv) dimnames(res) <- list("Wald test", c("Statistics","P-Value")) ans$result <- res ans } ################################################################################ fBasics/R/utils-unitrootNA.R0000644000176200001440000000471014263246022015413 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .unirootNA Computes zero of a function without error exit ################################################################################ .unirootNA <- function(f, interval, lower = min(interval), upper = max(interval), tol = .Machine$double.eps^0.25, ...) { # A function implemented by Diethelm Wuertz # Description: # Searches the interval from lower to upper for a # root (i.e., zero) of the function f with respect # to its first argument. # Arguments: # see 'uniroot' # Value: # Returns the x value of f where the root is located. If # now root exists NA will be returned. In the last case # the function doesn't terminate with an error like in the # case of the standard function uniroot. # Details: # R: # uniroot(f, interval, lower = min(interval), upper = max(interval), # tol = .Machine$double.eps^0.25, # maxiter = 1000, ...) # uniroot(f, interval, lower = min(interval), upper = max(interval), # tol = .Machine$double.eps^.25, # keep.xy = F, f.lower = NA, f.upper = NA, ...) # Example: # .unirootNA(sin, c(1, 2)); .unirootNA(sin, c(-1, 1)) # FUNCTION: # There is no Root: if (is.null(args(f))) { if (f(lower) * f(upper) >=0) return(NA) } else { if (f(lower, ...) * f(upper, ...) >= 0) return(NA) } # There is a Root: ans = uniroot(f = f, interval = interval, lower = lower, upper = upper, tol = tol, ...) # Return Value: ans$root } ################################################################################ fBasics/R/test-jbALM.R0000644000176200001440000172071614263246022014071 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # DATA: DESCRIPTION: # .jbALM Jarque Bera Augmented Lagrange Multiplier Data ################################################################################ .jbALM <- function() { structure(list("5" = c(3.6559e-05, 3.6676e-05, 3.8408e-05, 3.9805e-05, 4.0283e-05, 4.0549e-05, 4.062e-05, 4.1218e-05, 4.2351e-05, 4.2355e-05, 4.2797e-05, 4.3593e-05, 4.3764e-05, 4.3945e-05, 4.4328e-05, 4.4677e-05, 4.4694e-05, 4.5125e-05, 4.5133e-05, 4.5314e-05, 4.5404e-05, 4.6125e-05, 4.7878e-05, 4.791e-05, 4.8059e-05, 4.8158e-05, 4.8537e-05, 4.8671e-05, 4.9346e-05, 5.2005e-05, 5.2045e-05, 5.2874e-05, 5.4154e-05, 5.4401e-05, 5.6475e-05, 5.7065e-05, 5.7268e-05, 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"0.999986", "0.9999862", "0.9999864", "0.9999865", "0.9999867", "0.9999868", "0.999987", "0.9999871", "0.9999873", "0.9999874", "0.9999876", "0.9999877", "0.9999878", "0.999988", "0.9999881", "0.9999883", "0.9999884", "0.9999885", "0.9999886", "0.9999888", "0.9999889", "0.999989", "0.9999892", "0.9999893", "0.9999894", "0.9999895", "0.9999896", "0.9999898", "0.9999899", "0.99999" ), class = "data.frame") } ################################################################################ fBasics/R/utils-as.matrix.ts.R0000644000176200001440000000370714263246022015651 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # as.matrix.ts Converts univariate ts to 1-column matrix # as.matrix.mts Converts multivariate ts to matrix ################################################################################ ## as.matrix.ts <- ## function(x, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Coerces a "ts" object into a matrix ## # FUNCTION: ## # Transform: ## ans = as.matrix.default(unclass(x)) ## attr(ans, "tsp")<-NULL ## rownames(ans)<-NULL ## colnames(ans)<-NULL ## # Return Value: ## ans ## } ## # ------------------------------------------------------------------------------ ## as.matrix.mts <- ## function(x, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Coerces a multivariate "ts" object into a matrix ## # FUNCTION: ## # Transform: ## ans = as.matrix.default(unclass(x)) ## attr(ans, "tsp")<-NULL ## rownames(ans)<-NULL ## colnames(ans)<-NULL ## # Return Value: ## ans ## } ################################################################################ fBasics/R/builtin-posdefCorpcor.R0000644000176200001440000000165314263246022016432 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # posdef ################################################################################ fBasics/R/utils-interactivePlot.R0000644000176200001440000001121114650724111016457 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: PLOT UTILITIES: # interactivePlot Plots several graphs interactively ################################################################################ interactivePlot <- function(x, choices = paste("Plot", 1:9), plotFUN = paste("plot.", 1:9, sep = ""), which = "all", ...) { # A function implemented by Diethelm Wuertz. # Modified by GNB to accept a list of functions for argument 'which' # (previously it had to be a character vector) # Description: # Plot method for an object of class "template". # Arguments: # x - an object to be plotted # choices - the character string for the choice menu # plotFUN - the names of the plot functions # which - plot selection, which graph should be # displayed. If a character string named "ask" the # user is interactively asked which to plot, if # a logical vector of length N, those plots which # are set "TRUE" are displayed, if a character string # named "all" all plots are displayed. # Note: # At maximum 9 plots are supported. # FUNCTION: # Some cecks: if (length(choices) != length(plotFUN)) stop("Arguments choices and plotFUN must be of same length.") if (length(which) > length(choices)) stop("Arguments which has incorrect length.") if (length(which) > length(plotFUN)) stop("Arguments which has incorrect length.") if (length(choices) > 9) stop("Sorry, only 9 plots at max are supported.") # Internal "askPlot" Function: multPlot = function(x, choices, ...) { ## Selective Plot: selectivePlot <- function(x, choices, FUN, which){ ## Internal Function: askPlot <- function(x, choices, FUN) { ## Pick and Plot: pick = 1 setRmetricsOptions(n.plots = length(choices)) while (pick > 0) { pick = menu (choices = paste("plot:", choices), title = "\nMake a plot selection (or 0 to exit):") if (pick > 0) match.fun(FUN[[pick]])(x) } } if (as.character(which[1]) == "ask") { askPlot(x, choices = choices, FUN = FUN, ...) } else { for (i in 1:getRmetricsOptions("n.plots")) if (which[i]) match.fun(FUN[[i]])(x) } invisible() } # match Functions, up to nine ... if (length(plotFUN) < 9) plotFUN = c(plotFUN, rep(plotFUN[[1]], times = 9 - length(plotFUN))) plot.1 = match.fun(plotFUN[[1]]); plot.2 = match.fun(plotFUN[[2]]) plot.3 = match.fun(plotFUN[[3]]); plot.4 = match.fun(plotFUN[[4]]) plot.5 = match.fun(plotFUN[[5]]); plot.6 = match.fun(plotFUN[[6]]) plot.7 = match.fun(plotFUN[[7]]); plot.8 = match.fun(plotFUN[[8]]) plot.9 = match.fun(plotFUN[[9]]) pick = 1 while (pick > 0) { pick = menu (choices = paste(" ", choices), title = "\nMake a plot selection (or 0 to exit):") ## up to 9 plot functions ... switch (pick, plot.1(x), plot.2(x), plot.3(x), plot.4(x), plot.5(x), plot.6(x), plot.7(x), plot.8(x), plot.9(x) ) } } # Plot: if (is.numeric(which)) { Which = rep(FALSE, times = length(choices)) Which[which] = TRUE which = Which } if (which[1] == "all") { which = rep(TRUE, times = length(choices)) } if (which[1] == "ask") { multPlot(x, choices, ...) } else { for ( i in 1:length(which) ) { FUN = match.fun(plotFUN[[i]]) if (which[i]) FUN(x) } } # Return Value: invisible(x) } ################################################################################ fBasics/R/dist-snig.R0000644000176200001440000000750314263246022014057 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dsnig Returns density of the SNIG distribution # psnig Returns probabilities of the SNIG distribution # qsnig Returns quantiles of the SNIG distribution # rsnig Generates SNIG distributed random variates # FUNCTION: DESCRIPTION: # .psnigC Fast psnig from C code # .qsnigC Fast qsnig from C code ################################################################################ dsnig <- function(x, zeta = 1, rho = 0, log = FALSE) { # Description: # Returns density of the snig distribution # FUNCTION: # Parameters: if (length(zeta) == 2) { rho = zeta[2] zeta = zeta[1] } # Compute Density - Quick and Dirty: ans = dsgh(x, zeta, rho, lambda = -0.5, log = log) # Log: if(log) ans = log(ans) # Return Value: ans } # ------------------------------------------------------------------------------ psnig <- function(q, zeta = 1, rho = 0) { # Description: # Returns probabilities of the snig distribution # FUNCTION: # Compute Probabilities - Quick and Dirty: ans = psgh(q, zeta, rho, lambda = -0.5) # Return Value: ans } # ------------------------------------------------------------------------------ qsnig <- function(p, zeta = 1, rho = 0) { # Description: # Returns quantiles of the snig distribution # FUNCTION: # Compute Quantiles: ans = qsgh(p, zeta, rho, lambda = -0.5) # Return Value: ans } # ------------------------------------------------------------------------------ rsnig <- function(n, zeta = 1, rho = 0) { # Description: # Generates snig distributed random variates # FUNCTION: # Generate Random Numbers: ans = rsgh(n, zeta, rho, lambda = -0.5) # Return Value: ans } ################################################################################ .psnigC <- function(q, zeta = 1, rho = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the snig distribution # FUNCTION: # Compute Quantiles: param = .paramGH(zeta, rho, lambda = -0.5) ans = .pnigC(q, param[1], param[2], param[3], param[4]) # Return Value: ans } # ------------------------------------------------------------------------------ .qsnigC <- function(p, zeta = 1, rho = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the snig distribution # FUNCTION: # Compute Quantiles: param = .paramGH(zeta, rho, lambda = -0.5) ans = .qnigC(p, param[1], param[2], param[3], param[4]) # Return Value: ans } ################################################################################ fBasics/R/test-correlationTest.R0000644000176200001440000002350414324450633016316 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # correlationTest Performs correlation tests on two samples # pearsonTest Pearson product moment correlation coefficient # kendallTest Kendall's tau correlation test # spearmanTest Spearman's rho correlation test ################################################################################ ## GNB: changed the default 'description = date()' to 'description = ""', to ## avoid getting different objects in repeated calls. correlationTest <- function(x, y, method = c("pearson", "kendall", "spearman"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs Correlation Tests # FUNCTION: # Test: method = match.arg(method) if (method[1] == "pearson") { ans = pearsonTest(x, y, title = title, description = description) } if (method[1] == "kendall") { ans = kendallTest(x, y, title = title, description = description) } if (method[1] == "spearman") { ans = spearmanTest(x, y, title = title, description = description) } # Return Value: ans } # ------------------------------------------------------------------------------ pearsonTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # A test for association between paired samples # Arguments: # x - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) stopifnot(length(x) == length(y)) # Test: two.sided = cor.test(x = x, y = y, alternative = "two.sided", method = "pearson") less = cor.test(x = x, y = y, alternative = "less", method = "pearson") greater = cor.test(x = x, y = y, alternative = "greater", method = "pearson") # Sample Estimates: ESTIMATE = two.sided$estimate names(ESTIMATE) = "Correlation" test$estimate = ESTIMATE # Parameter DF = two.sided$parameter names(DF) = "Degrees of Freedom" test$parameter = DF # P Values: PVAL = c( two.sided$p.value, less$p.value, greater$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Confidences Levels: if (!is.null(two.sided$conf.int)) { CONF.INT = cbind( a = two.sided$conf.int, b = less$conf.int, c = greater$conf.int) dimnames(CONF.INT)[[2]] = c( "Two-Sided", " Less", " Greater") test$conf.int = CONF.INT } # Statistic: STATISTIC = two.sided$statistic names(STATISTIC) = "t" test$statistic = STATISTIC # Add: if (is.null(title)) title = "Pearson's Correlation Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ kendallTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # A test for association between paired samples # Arguments: # x - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) stopifnot(length(x) == length(y)) # Test: two.sided = cor.test(x = x, y = y, alternative = "two.sided", method = "kendall") less = cor.test(x = x, y = y, alternative = "less", method = "kendall") greater = cor.test(x = x, y = y, alternative = "greater", method = "kendall") # Exact Test: if (!inherits(version, "Sversion")) { two.sided.exact = cor.test(x = x, y = y, exact = TRUE, alternative = "two.sided", method = "kendall") less.exact = cor.test(x = x, y = y, exact = TRUE, alternative = "less", method = "kendall") greater.exact = cor.test(x = x, y = y, exact = TRUE, alternative = "greater", method = "kendall") } else { two.sided.exact = list() two.sided.exact$p.value = two.sided.exact$statistic = NA less.exact = list() less.exact$p.value = less.exact$statistic = NA greater.exact = list() greater.exact$p.value = greater.exact$statistic = NA } # Sample Estimates: ESTIMATE = two.sided$estimate names(ESTIMATE) = "tau" test$estimate = ESTIMATE # P Values: PVAL = c( two.sided$p.value, two.sided.exact$p.value, less$p.value, less.exact$p.value, greater$p.value, greater.exact$p.value) if (is.na(two.sided.exact$p.value)) { names(PVAL) = c( "Alternative Two-Sided", "Alternative Two-Sided | Exact", "Alternative Less", "Alternative Less | Exact", "Alternative Greater", "Alternative Greater | Exact") } else { names(PVAL) = c( "Alternative Two-Sided", "Alternative Two-Sided | Exact", "Alternative Less", "Alternative Less | Exact", "Alternative Greater", "Alternative Greater | Exact") } test$p.value = PVAL # Statistic: # STATISTIC = c( # two.sided$statistic, two.sided.exact$statistic, # less$statistic, less.exact$statistic, # greater$statistic, greater.exact$statistic) STATISTIC = c( two.sided$statistic, two.sided.exact$statistic) # names(STATISTIC) = c( # "z | Two-Sided", "T | Two-Sided | Exact", # "z | Less", "T | Less | Exact", # "z | Greater", "T | Greater | Exact") names(STATISTIC) = c( "z", "T | Exact") test$statistic = STATISTIC # Add: if (is.null(title)) title = "Kendall's tau Correlation Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ spearmanTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # A test for association between paired samples # Arguments: # x - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Note: # # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) stopifnot(length(x) == length(y)) # Test: two.sided = cor.test(x = x, y = y, alternative = "two.sided", method = "spearman") less = cor.test(x = x, y = y, alternative = "less", method = "spearman") greater = cor.test(x = x, y = y, alternative = "greater", method = "spearman") # Sample Estimates: ESTIMATE = two.sided$estimate names(ESTIMATE) = "rho" test$estimate = ESTIMATE # P Values: PVAL = c( two.sided$p.value, less$p.value, greater$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Statistic: STATISTIC = two.sided$statistic names(STATISTIC) = "S" test$statistic = STATISTIC # Add: if (is.null(title)) title = "Spearman's rho Correlation Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/zzz.R0000644000176200001440000000343714263246022013015 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ .onAttach <- function(libname, pkgname) { # do whatever needs to be done when the package is loaded # some people use it to bombard users with # messages using # packageStartupMessage( "\n" ) # packageStartupMessage( "Rmetrics Package fBasics" ) # packageStartupMessage( "Analysing Markets and calculating Basic Statistics" ) # packageStartupMessage( "Copyright (C) 2005-2014 Rmetrics Association Zurich" ) # packageStartupMessage( "Educational Software for Financial Engineering and Computational Science" ) # packageStartupMessage( "Rmetrics is free software and comes with ABSOLUTELY NO WARRANTY." ) # packageStartupMessage( "https://www.rmetrics.org --- Mail to: info@rmetrics.org" ) } ################################################################################ .onLoad <- function(libname, pkgname) { setRmetricsOptions(.x.save = NA) } ################################################################################ fBasics/R/dist-sgh.R0000644000176200001440000001270614263246022013701 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dsgh Returns density of the standardized gh distribution # psgh Returns probabilities of the sgh distribution # qsgh Returns quantiles of the sgh distribution # rsgh Generates sgh distributed random variates # FUNCTION: DESCRIPTION: # .kappaGH Returns modified Bessel function ratio # .deltaKappaGH Returns difference of Bessel function ratios # .paramGH Change parameterization to alpha, beta, delta mu ################################################################################ dsgh <- function(x, zeta = 1, rho = 0, lambda = 1, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density of the sgh distribution # FUNCTION: # Parameters: if (length(zeta) == 3) { lambda = zeta[3] rho = zeta[2] zeta = zeta[1] } # Compute Density: param = .paramGH(zeta, rho, lambda) ans = dgh(x, param[1], param[2], param[3], param[4], lambda, log) # Return Value: ans } # ------------------------------------------------------------------------------ psgh <- function(q, zeta = 1, rho = 0, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities of the sgh distribution # FUNCTION: # Compute Probabilities: param = .paramGH(zeta, rho, lambda) ans = pgh(q, param[1], param[2], param[3], param[4], lambda) # Return Value: ans } # ------------------------------------------------------------------------------ qsgh <- function(p, zeta = 1, rho = 0, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the sgh distribution # FUNCTION: # Compute Quantiles: param = .paramGH(zeta, rho, lambda) ans = qgh(p, param[1], param[2], param[3], param[4], lambda) # Return Value: ans } # ------------------------------------------------------------------------------ rsgh <- function(n, zeta = 1, rho = 0, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Generates sgh distributed random variates # FUNCTION: # Generate Random Numbers: param = .paramGH(zeta, rho, lambda) ans = rgh(n, param[1], param[2], param[3], param[4], lambda) # Return Value: ans } ################################################################################ .kappaGH <- function(x, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns modified Bessel function ratio # FUNCTION: # Check: stopifnot(x >= 0) stopifnot(length(lambda) == 1) # Ratio: if (lambda == -0.5) { # NIG: kappa = 1/x } else { # GH: kappa = ( besselK(x, lambda+1, expon.scaled = TRUE) / besselK(x, lambda, expon.scaled = TRUE) ) / x } # Return Value: kappa } # ------------------------------------------------------------------------------ .deltaKappaGH <- function(x, lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns difference of Bessel functions ratios # FUNCTION: # Difference in Ratios: if (lambda == -0.5) { # NIG: # Replace this with the recursion relation ... deltaKappa = .kappaGH(x, lambda+1) - .kappaGH(x, lambda) } else { # GH: deltaKappa = .kappaGH(x, lambda+1) - .kappaGH(x, lambda) } # Return Value: deltaKappa } ################################################################################ .paramGH <- function(zeta = 1, rho = 0 , lambda = 1) { # A function implemented by Diethelm Wuertz # Description: # Change parameterizations to alpha(zeta, rho, lambda) # FUNCTION: # Transformation: Rho2 = 1 - rho^2 alpha = zeta^2 * .kappaGH(zeta, lambda) / Rho2 alpha = alpha * ( 1 + rho^2 * zeta^2 * .deltaKappaGH(zeta, lambda) / Rho2) alpha = sqrt(alpha) beta = alpha * rho delta = zeta / ( alpha * sqrt(Rho2) ) mu = -beta * delta^2 * .kappaGH(zeta, lambda) # Return Value: c(alpha = alpha, beta = beta, delta = delta, mu = mu) } ################################################################################ fBasics/R/matrix-pascal.R0000644000176200001440000000376314263246022014727 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # pascal Creates a Pascal matrix ################################################################################ pascal <- function(n) { # A function implemented by Diethelm Wuertz # Description: # Creates a Pascal matrix # Arguments: # n - the dimension of the square matrix # Details: # http://mathworld.wolfram.com/PascalMatrix.html # Pascal matrices are symmetric and positive definite. # The determinant of a Pascal matrix is 1. # The inverse of a Pascal matrix has integer entries. # If lambda is an eigenvalue of a Pascal matrix, # then 1/lambda is also an eigenvalue of the matrix. # The Cholesky factor of a Pascal matrix consists of # the elements of Pascal's triangle # FUNCTION: # Pascal: N = n-1 n.over.r = function(n, r) { prod(1:n) / (prod(1:(n-r)) * prod(1:r) ) } X = rep(1, N) for ( i in 1:N ) for ( j in 1:N ) X = c(X, n.over.r(i+j, j)) X = cbind(rep(1, N+1), matrix(X, byrow = TRUE, ncol = N)) # Return Value: X } ################################################################################ fBasics/R/dist-ghFit.R0000644000176200001440000000707314324531667014174 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # ghFit Fits parameters of a generalized hyperbolic DF ################################################################################ ghFit <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a generalized hyperbolic density # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Log-likelihood Function: obj = function(x, y = x, trace){ if (NA %in% x) return(1e99) if (abs(x[2]) >= x[1]) return(1e99) f = -sum(dgh(y, x[1], x[2], x[3], x[4], x[5], log = TRUE)) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x, "\n") } f } # Minimization: r = # Variable Transformation and Minimization: eps = 1e-10 BIG = 1000 f = obj(x = c(alpha, beta, delta, mu, lambda), y = x, trace = FALSE) r = nlminb(start = c(alpha, beta, delta, mu, lambda), objective = obj, lower = c(eps, -BIG, eps, -BIG, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu", "lambda") # Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD, 1) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("GH Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } # Add Title and Description: if (is.null(title)) title = "Generalized Hyperbolic Parameter Estimation" if (is.null(description)) description = "" # Fit: fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Generalized Hyperbolic Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } ################################################################################ fBasics/R/utils-symbolTable.R0000644000176200001440000000420414650724111015564 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # symbolTable Shows a table of plot symbols from a given font ################################################################################ symbolTable <- function(font = par('font'), cex = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Shows a table of plot characters from a given font # Example: # symbolTable() # Author: # Unknown, piece of code found on the internet. # FUNCTION: # Table: plot(0, 0, xlim = c(-1, 11), ylim = c(0, 26), type = 'n', axes = FALSE, xlab = '', ylab = '', main = "Table of Plot Characters") j = -1 for(i in 0:255) { if(i %% 25 == 0) {j = j+1; k = 26} k = k-1 ## 2023-10-07 GNB: fix to work in UTF locale as latin1 chars. ## ## Note: according to ?points, R ignores values in 26:31. Indeed, it ## issues warnings but actually plots them! ## Some other symbols are replaced by boxes. ch.i <- rawToChar(as.raw(i)) Encoding(ch.i) <- "latin1" points(j, k, pch = ch.i, font = font, cex = cex, col = 2) text(j + 0.50, k, i, cex = cex) } # Return Value: invisible(font) } ################################################################################ fBasics/R/utils-decor.R0000644000176200001440000000777314263246022014421 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # decor Adds horizontal grid and L shaped box # hgrid Adds horizontal grid lines # vgrid Adds vertical grid lines # boxL Adds L-shaped box # box_ Adds unterlined box # .xrug Adds rugs on x axis # .yrug Adds rugs on y axis # copyright Adds copyright notice ################################################################################ decor <- function() { # A function implemented by Diethelm Wuertz # Description: # Adds a horizontal grid and L shaped box # FUNCTION: # Add: hgrid() boxL() # Return Value: invisible() } ################################################################################ hgrid <- function(ny = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Adds horizontal grid lines # FUNCTION: # Add: grid(NA, ny, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ vgrid <- function(nx = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Adds vertical grid lines # FUNCTION: # Add: grid(nx, NA, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ boxL <- function(col = "white") { # A function implemented by Diethelm Wuertz # Description: # Adds L-shaped box # Add: box() box(bty = "7", col = col) # Return Value: invisible() } # ------------------------------------------------------------------------------ box_ <- function(col = c("white", "black")) { # A function implemented by Diethelm Wuertz # Description: # Adds unterlined box # FUNCTION: # Add: box(bty = "c", col = col[1]) box(bty = "]", col = col[2]) box(bty = "7", col = col[1]) # Return Value: invisible() } # ------------------------------------------------------------------------------ .xrug <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Adds rugs on x axis # FUNCTION: # Add: rug(as.vector(x), ticksize = 0.01, side = 1, quiet = TRUE) # Return Value: invisible() } # ------------------------------------------------------------------------------ .yrug <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Adds rugs on y axis # FUNCTION: # Add: rug(as.vector(x), ticksize = 0.01, side = 2, quiet = TRUE) # Return Value: invisible() } # ------------------------------------------------------------------------------ copyright <- function() { # A function implemented by Diethelm Wuertz # Description: # Adds copyright notice # FUNCTION: # Add: Year = substr(Sys.Date(), 1, 4) mtext(paste("(c) Rmetrics", Year), side = 4, line = 0, adj = 0, font = 1, cex = 0.7*par("cex"), col = "grey") # Return Value: invisible() } ################################################################################ fBasics/R/matrix-kron.R0000644000176200001440000000241214263246022014423 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # LINEAR ALGEBRA: DESCRIPTION: # kron Returns the Kronecker product ################################################################################ kron <- function(x, y) { # A function implemented by Diethelm Wuertz # Description: # Returns the Kronecker product # FUNCTION: # Kronecker Product: ans = x %x% y # Return Value: ans } ################################################################################ fBasics/R/dist-hyp.R0000644000176200001440000001440114650724111013712 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dhyp Returns density for hyperbolic DF # phyp Returns probability for hyperbolic DF # qhyp Returns quantiles for hyperbolic DF # rhyp Returns random variates for hyperbolic DF ################################################################################ dhyp <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns Hyperbolic Density Function PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameters: if (length(alpha) == 4) { mu <- alpha[4] delta <- alpha[3] beta <- alpha[2] alpha <- alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Density: ans <- if (pm == 1) .dhyp1(x, alpha, beta, delta, mu) else if (pm == 2) .dhyp2(x, alpha, beta, delta, mu) else if (pm == 3) .dhyp3(x, alpha, beta, delta, mu) else if (pm == 4) .dhyp4(x, alpha, beta, delta, mu) else stop("argument 'pm' should be one of the numbers 1, 2, 3, or 4") # Return value: if (log) log(ans) else ans } # ------------------------------------------------------------------------------ phyp <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1, ...) { # A function implemented by Diethelm Wuertz # Description: # Return cumulative probability of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Parameters: if (length(alpha) == 4) { mu <- alpha[4] delta <- alpha[3] beta <- alpha[2] alpha <- alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Return Value: if (pm == 1) .phyp1(q, alpha, beta, delta, mu, ...) else if (pm == 2) .phyp2(q, alpha, beta, delta, mu, ...) else if (pm == 3) .phyp3(q, alpha, beta, delta, mu, ...) else if (pm == 4) .phyp4(q, alpha, beta, delta, mu, ...) else stop("argument 'pm' should be one of the numbers 1, 2, 3, or 4") } # ------------------------------------------------------------------------------ qhyp <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of Hyperbolic PDF # Arguments: # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Note: # This procedure will not run under Splus. # FUNCTION: # Parameters: if (length(alpha) == 4) { mu <- alpha[4] delta <- alpha[3] beta <- alpha[2] alpha <- alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Return Value: if (pm == 1) .qhyp1(p, alpha, beta, delta, mu, ...) else if (pm == 2) .qhyp2(p, alpha, beta, delta, mu, ...) else if (pm == 3) .qhyp3(p, alpha, beta, delta, mu, ...) else if (pm == 4) .qhyp4(p, alpha, beta, delta, mu, ...) else stop("argument 'pm' should be one of the numbers 1, 2, 3, or 4") } # ------------------------------------------------------------------------------ rhyp <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1) { # A function implemented by Diethelm Wuertz # Description: # Returns random deviates of Hyperbolic PDF # Arguments: # n - number of random deviates to be generated # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # Notes: # I have removed my original Fortran program and replaced it by # the dhyperb() function from the HyperbolicDist Package, written # by David Scott, Ai-Wei Lee, Jennifer Tso, Richard Trendall. # License: GPL # FUNCTION: # Parameters: if (length(alpha) == 4) { mu <- alpha[4] delta <- alpha[3] beta <- alpha[2] alpha <- alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Result: if (pm == 1) .rhyp1(n, alpha, beta, delta, mu) else if (pm == 2) .rhyp2(n, alpha, beta, delta, mu) else if (pm == 3) .rhyp3(n, alpha, beta, delta, mu) else if (pm == 4) .rhyp4(n, alpha, beta, delta, mu) else stop("argument 'pm' should be one of the numbers 1, 2, 3, or 4") } ################################################################################ fBasics/R/matrix-posDefinite.R0000644000176200001440000000776114264011647015743 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # isPositiveDefinite Checks if the matrix X is positive definite # makePositiveDefinite Forces the matrix x to be positive definite ################################################################################ isPositiveDefinite <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Checks if the matrix x is positive definite # Arguments: # x - a symmetric matrix or any other rectangular object # describing a covariance matrix which can be converted into # a matrix by the function 'as.matrix'. # FUNCTION: # Transform: x = as.matrix(x) # Check if matrix is positive definite: ans = .is.positive.definite(m = x) # Return Value: ans } # ------------------------------------------------------------------------------ .is.positive.definite <- function(m, tol, method = c("eigen", "chol")) { # Author: # Copyright 2003-05 Korbinian Strimmer # Rank, condition, and positive definiteness of a matrix # GNU General Public License, Version 2 method = match.arg(method) if (!is.matrix(m)) { m = as.matrix(m) } if (method == "eigen") { eval = eigen(m, only.values = TRUE)$values if( missing(tol) ) { tol = max(dim(m))*max(abs(eval))*.Machine$double.eps } if (sum(eval > tol) == length(eval)) { return(TRUE) } else { return(FALSE) } } else if (method == "chol") { val = try(chol(m), silent = TRUE) if (inherits(val, "try-error")) { return(FALSE) } else { return(TRUE) } } } # ------------------------------------------------------------------------------ makePositiveDefinite <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Forces the matrix x to be positive definite # Arguments: # x - a symmetric matrix or any other rectangular object # describing a covariance matrix which can be converted into # a matrix by the function 'as.matrix'. # FUNCTION: # Make Positive Definite: ans = .make.positive.definite(m = x) # Return Value: ans } # ------------------------------------------------------------------------------ .make.positive.definite <- function(m, tol) { # Author: # Copyright 2003-05 Korbinian Strimmer # Rank, condition, and positive definiteness of a matrix # GNU General Public License, Version 2 # Method by Higham 1988 if (!is.matrix(m)) { m = as.matrix(m) } d = dim(m)[1] if ( dim(m)[2] != d ) { stop("Input matrix is not square!") } es = eigen(m) esv = es$values if (missing(tol)) { tol = d*max(abs(esv))*.Machine$double.eps } delta = 2*tol # factor two is just to make sure the resulting # matrix passes all numerical tests of positive definiteness tau = pmax(0, delta - esv) dm = es$vectors %*% diag(tau, d) %*% t(es$vectors) # print(max(DA)) # print(esv[1]/delta) return( m + dm ) } ################################################################################ fBasics/R/test-ks2Test.R0000644000176200001440000000577314324451244014503 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ks2Test Performs a two sample Kolmogorov-Smirnov test ################################################################################ ks2Test <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Two-sample Kolmogorov-Smirnov test. # Arguments: # x - a numeric vector of data values. # description - a brief description of the project of type # character. # title - a character string which allows for a project title. # Note: # A function partly copied from "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Compute Test: two.sided = ks.test(x = x, y = y, alternative = "two.sided") exact = ks.test(x = x, y = y, exact = TRUE, alternative = "two.sided") less = ks.test(x = x, y = y, alternative = "less") greater = ks.test(x = x, y = y, alternative = "greater") # P Value: PVAL = c( two.sided$p.value, exact$p.value, less$p.value, greater$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Exact Two-Sided", "Alternative Less", "Alternative Greater") test$p.value = PVAL # Statistic: STATISTIC = c( two.sided$statistic, less$statistic, greater$statistic) names(STATISTIC) = c( "D | Two Sided", " D^- | Less", "D^+ | Greater") test$statistic = STATISTIC # Add: if (is.null(title)) title = "Kolmogorov-Smirnov Two Sample Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/dist-nig.R0000644000176200001440000002011214650724111013663 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dnig Returns density for inverse Gaussian DF # pnig Returns probability for for inverse Gaussian DF # qnig Returns quantiles for for inverse Gaussian DF # rnig Returns random variates for inverse Gaussian DF # FUNCTION: DESCRIPTION: # .pnigC Fast C implementation # .qnigC Fast C implementation ################################################################################ .dnig <- function(x, alpha, beta, delta, mu, log = FALSE) { x. <- x - mu log.a <- delta*sqrt(alpha^2-beta^2) + log(delta*alpha/pi) Sqrt <- sqrt(delta^2 + x.^2) log.K1 <- log(besselK(alpha * Sqrt, 1, expon.scaled = TRUE)) - alpha*Sqrt dnig <- log.a -log(Sqrt) + log.K1 + beta*x. if(log) dnig else exp(dnig) } dnig <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density for inverse Gaussian DF # Example: # x = rnorm(1000); u = dgh(x, 1.1, 0.2, 0.8, 0.4, -0.5) # v = dnig(rnorm(x), 1.1, 0.2, 0.8, 0.4); u -v # FUNCTION: # Parameters: if (length(alpha) == 4) { mu = alpha[4] delta = alpha[3] beta = alpha[2] alpha = alpha[1] } # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") .dnig(x, alpha=alpha, beta=beta, delta=delta, mu=mu, log=log) } # ------------------------------------------------------------------------------ pnig <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns probability for for inverse Gaussian DF # Function: # Probability: # pgh(q = q, alpha = alpha, beta = beta, delta = delta, mu = mu, # lambda = -0.5) if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") ans <- q for(i in seq_along(q)) { ans[i] <- integrate(.dnig, -Inf, q[i], stop.on.error = FALSE, alpha = alpha, beta = beta, delta = delta, mu = mu)$value } ans } # ------------------------------------------------------------------------------ qnig <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for for inverse Gaussian DF # FUNCTION: # Quantiles: # qgh(p = p, alpha = alpha, beta = beta, delta = delta, mu = mu, # lambda = -0.5) # Checks: if (alpha <= 0) stop("alpha must be greater than zero") if (delta <= 0) stop("delta must be greater than zero") if (abs(beta) >= alpha) stop("abs value of beta must be less than alpha") # Internal Function: .froot <- function(x, alpha, beta, delta, mu, p) pnig(q = x, alpha = alpha, beta = beta, delta = delta, mu = mu) - p # Quantiles: ans <- p for(i in seq_along(p)) { lower = -1 upper = +1 counter = 0 v <- NA while(is.na(v) && counter < 1000) { v <- .unirootNA(f = .froot, interval = c(lower, upper), alpha = alpha, beta = beta, delta = delta, mu = mu, p = p[i]) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } ans[i] <- v } # Return Value: ans } # ------------------------------------------------------------------------------ rnig <- function(n, alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Return normal inverse Gaussian distributed random variates # Arguments: # n - number of deviates to be generated # alpha, beta - Shape Parameter, |beta| <= alpha # delta - Scale Parameter, 0 <= delta # mu - Location Parameter # FUNCTION: # Settings: gamma = sqrt(alpha*alpha - beta*beta) # GAMMA: if (gamma == 0) { # GAMMA = 0: V = rnorm(n)^2 Z = delta*delta / V X = sqrt(Z)*rnorm(n) } else { # GAMMA > 0: U = runif(n) V = rnorm(n)^2 # FIXED ... z1 <- function(v, delta, gamma) { delta/gamma + v/(2*gamma^2) - sqrt(v*delta/(gamma^3) + (v/(2*gamma^2))^2 ) } z2 <- function(v, delta, gamma) { (delta/gamma)^2 / z1(v = v, delta = delta, gamma = gamma) } pz1 <- function(v, delta, gamma) { delta / (delta + gamma * z1(v = v, delta = delta, gamma = gamma) ) } s = (1-sign(U-pz1(v = V, delta = delta, gamma = gamma)))/2 Z = z1(v = V, delta = delta, gamma = gamma)*s + z2(v = V, delta = delta, gamma = gamma)*(1-s) X = mu + beta*Z + sqrt(Z)*rnorm(n) } # Return Value: X } ################################################################################ .qnigC <- function(p, alpha = 1, beta = 0, delta = 1, mu = 0) { # Description: # Returns quantiles for for inverse Gaussian DF # Example: # p = runif(10); .qnigC(p) # FUNCTION: # Checks: if(alpha <= 0) stop("Invalid parameters: alpha <= 0.0\n") if(alpha^2 <= beta^2) stop("Invalid parameters: alpha^2 <= beta^2.0\n") if(delta <= 0) stop("Invalid parameters: delta <= 0.0\n") if((sum(is.na(p)) > 0)) stop("Invalid probabilities:\n",p,"\n") else if(sum(p < 0)+sum(p > 1) > 0) stop("Invalid probabilities:\n",p,"\n") # Evaluate NIG cdf by calling C function n <- length(p) q <- rep(0, n) retValues <- .C(C_qNIG, as.double(p), as.double(mu), as.double(delta), as.double(alpha), as.double(beta), as.integer(n), as.double(q)) quantiles <- retValues[[7]] quantiles[quantiles <= -1.78e+308] <- -Inf quantiles[quantiles >= 1.78e+308] <- Inf # Return Value: quantiles } # ------------------------------------------------------------------------------ .pnigC <- function(q, alpha = 1, beta = 0, delta = 1, mu = 0) { # Description: # Returns probabilities for for inverse Gaussian DF # IMPORTANT NOTE: # DW: C program fails, remains to check # Example: # q = sort(rnorm(10)); .pnigC(q) # FAILS # FUNCTION: # Checks: if(alpha <= 0) stop("Invalid parameters: alpha <= 0.0\n") if(alpha^2 <= beta^2) stop("Invalid parameters: alpha^2 <= beta^2.0\n") if(delta <= 0) stop("Invalid parameters: delta <= 0.0\n") if(sum(is.na(q)) > 0) stop("Invalid quantiles:\n", q) # Evaluate NIG cdf by calling C function n <- length(q) p <- rep(0, n) retValues <- .C(C_pNIG, as.double(q), as.double(mu), as.double(delta), as.double(alpha), as.double(beta), as.integer(n), as.double(p)) probs <- retValues[[7]] # Return Value: probs } ################################################################################ fBasics/R/dist-hypFit.R0000644000176200001440000000647414324532532014372 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # hypFit Fits parameters of a hyperbolic density ################################################################################ hypFit <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a hyperbolic density # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Log-likelihood Function: ehypmle = function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e99) f = -sum(log(dhyp(y, x[1], x[2], x[3], x[4]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Minimization: eps = 1e-10 BIG = 1000 r = nlminb(start = c(alpha, beta, delta, mu), objective = ehypmle, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu") # Add Title and Description: if (is.null(title)) title = "Hyperbolic Parameter Estimation" if (is.null(description)) description = "" # Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = ehypmle(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dhyp(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("HYP Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Hyperbolic Distribution", data = as.data.frame(x.orig), fit = fit, title = title, description = description ) } ################################################################################ fBasics/R/stats-interpAkima.R0000644000176200001440000001232514264011647015560 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # akimaInterp Interpolates irregularly spaced points # akimaInterpp Interpolates and smoothes pointwise ################################################################################ akimaInterp <- function(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE) { # A function implemented by Diethelm Wuertz ### ==> ../man/stats-interpAkima.Rd ### ~~~~~~~~~~~~~~~~~~~~ # Description: # Interpolates and Smoothes Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points spanning the grid # extrap - a logical, if TRUE then the data points are extrapolated.\ # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = akimaInterp(x, y, z, extrap = FALSE) # persp(ans, theta = -50, phi = 30, col = "steelblue") # ans = akimaInterp(x, y, z, extrap = TRUE) # persp(ans, theta = -50, phi = 30, col = "steelblue") # FUNCTION: if (!requireNamespace("interp", quietly = TRUE)) stop("Needs Package 'interp' which is not installed") # Arguments: if (is.data.frame(x)) x <- as.matrix.data.frame(x) else if (is.list(x)) x <- matrix(unlist(x), ncol = 3) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolation: ans <- interp::interp(x, y, z, xo, yo, linear = FALSE, extrap = extrap, duplicate = "median", dupfun = NULL) colnames(ans$z) <- as.character(signif(ans$x, round(log(gridPoints), 0))) rownames(ans$z) <- as.character(signif(ans$y, round(log(gridPoints), 0))) class(ans) <- "gridData" # Return Value: ans } # ------------------------------------------------------------------------------ akimaInterpp <- function(x, y = NULL, z = NULL, xo, yo, extrap = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Interpolates and Smoothes Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points for pointwise interpolation # Note: # Extrapolation is not possible in the case of linear interpolation. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = akimaInterpp(x, y, z, c(mean(x), 0, 100), c(mean(y), 0, 100)) # persp(ans, theta = -50, phi = 30, col = "steelblue") # FUNCTION: if (!requireNamespace("interp", quietly = TRUE)) stop("Needs package 'interp' which is not installed") # Arguments: if (is.data.frame(x)) x <- as.matrix.data.frame(x) else if (is.list(x)) x <- matrix(unlist(x), ncol = 3) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Settings: duplicate = "median" dupfun = NULL linear = TRUE # Interpolation: interpp <- eval(parse(text=paste0("interp",":::","interpp"))) ans <- interpp(x, y, z, xo, yo, linear = FALSE, extrap = extrap, duplicate = "median", dupfun = NULL) ans <- data.frame(matrix(unlist(t(ans)), 3)) colnames(ans) = c("x", "y", "z") # Return Value: ans } ################################################################################ fBasics/R/plot-boxPlot.R0000644000176200001440000001320114263246022014551 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # boxPlot Produces a side-by-side standard box plot # boxPercentilePlot Produces a side-by-side box-percentile plot ################################################################################ boxPlot <- function(x, col = "steelblue", title = TRUE, ...) { # A function Implemented by Diethelm Wuertz # Description: # Produces a standard box plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.matrix()' into an object of class 'matrix'. # Optional Arguments: # las, oma - allows to change style of X labels and creates # required space below plot. # Try: e.g. las = 3, and oma = c(9, 0, 0, 0) # FUNCTION: # Settings: x = as.matrix(x) assetNames = colnames(x) # Plot: ans = boxplot(as.data.frame(x), col = col, ...) abline(h = 0 , lty = 3) # Add Title: if (title) { title(main = "Box Plot", ylab = "Value") } # Result: colnames(ans$stats) = ans$names rownames(ans$stats) = c("lower whisker", "lower hinge", "median", "upper hinge", "upper whisker") # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ boxPercentilePlot <- function(x, col = "steelblue", title = TRUE, ...) { # A modified copy from Hmisc # Description: # Produces a side-by-side box-percentile plot # Details: # Box-percentile plots are similiar to boxplots, except box-percentile # plots supply more information about the univariate distributions. At # any height the width of the irregular "box" is proportional to the # percentile of that height, up to the 50th percentile, and above the # 50th percentile the width is proportional to 100 minus the percentile. # Thus, the width at any given height is proportional to the percent of # observations that are more extreme in that direction. As in boxplots, # the median, 25th and 75th percentiles are marked with line segments # across the box. [Source: Hmisc] # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.matrix()' into an object of class 'matrix'. # FUNCTION: # Settings: x = as.matrix(x) assetNames = colnames(x) n = ncol(x) all.x = list() for (i in 1:n) all.x[[i]] = as.vector(x[, i]) centers = seq(from = 0, by = 1.2, length = n) ymax = max(sapply(all.x, max, na.rm = TRUE)) ymin = min(sapply(all.x, min, na.rm = TRUE)) xmax = max(centers) + 0.5 xmin = -0.5 # Plot: if (length(col) == 1) col = rep(col, times = n) plot(c(xmin, xmax), c(ymin, ymax), type = "n", xlab = "", ylab = "", xaxt = "n", ...) xpos = NULL for (i in 1:n) { # plot.values = .bpxAssetsPlot(all.x[[i]], centers[i]) y = all.x[[i]] offset = centers[i] y = y[!is.na(y)] n = length(y) delta = 1/(n + 1) prob = seq(delta, 1 - delta, delta) quan = sort(y) med = median(y) q1 = median(y[y < med]) q3 = median(y[y > med]) first.half.p = prob[quan <= med] second.half.p = 1 - prob[quan > med] plotx = c(first.half.p, second.half.p) options(warn = -1) qx = approx(quan, plotx, xout = q1)$y q1.x = c(-qx, qx) + offset qx = approx(quan, plotx, xout = q3)$y options(warn = 0) q3.x = c(-qx, qx) + offset q1.y = c(q1, q1) q3.y = c(q3, q3) med.x = c(-max(first.half.p), max(first.half.p)) + offset med.y = c(med, med) plot.values = list(x1 = (-plotx) + offset, y1 = quan, x2 = plotx + offset, y2 = quan, q1.y = q1.y, q1.x = q1.x, q3.y = q3.y, q3.x = q3.x, med.y = med.y, med.x = med.x) # Continue: xpos = c(xpos, mean(plot.values$med.x)) x.p = c(plot.values$x1, plot.values$x2) y.p = c(plot.values$y1, plot.values$y2) polygon(x.p, y.p, col = col[i], border = "grey", ...) lines(plot.values$x1, plot.values$y1) lines(plot.values$x2, plot.values$y2) lines(plot.values$q1.x, plot.values$q1.y) lines(plot.values$q3.x, plot.values$q3.y) lines(plot.values$med.x, plot.values$med.y) } axis(side = 1, at = xpos, labels = assetNames, ...) abline(h = 0, lty = 3, col = "black") # Add Title: if (title) { title(main = "Box Percentiles", ylab = "Value") } # Return Value: invisible() } ################################################################################ fBasics/R/gui-stableSlider.R0000644000176200001440000000667514263246022015366 0ustar liggesusers # This R package is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This R package is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: STABLE SLIDERS: # stableSlider Displays stable distribution function ################################################################################ stableSlider <- function(col= "steelblue", col.med = "gray30") { # A function implemented by Diethelm Wuertz # Description: # Displays the stable distribution # FUNCTION: # Internal Function: refresh.code <- function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) gamma = .sliderMenu(no = 4) delta = .sliderMenu(no = 5) pm = .sliderMenu(no = 6) # Compute Data: x.rng <- round( stabledist::qstable(c(1,99)/100, alpha, beta, gamma, delta, pm), digits = 2) xmin <- x.rng[1]; xmax <- x.rng[2] s = seq(xmin, xmax, length = N) f = stabledist::dstable(s, alpha, beta, gamma, delta, pm) F = stabledist::pstable(s, alpha, beta, gamma, delta, pm) med <- stabledist::qstable(0.5, alpha, beta, gamma, delta, pm) main1 <- paste("Stable Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "gamma = ", as.character(gamma), " | ", "delta = ", as.character(delta)) main2 <- paste("Stable Probability\n", "xmin [ 1%] = ", as.character(xmin), " | ", "xmax [99%] = ", as.character(xmax), " | ", "pm = ", as.character(pm)) # Frame: op <- par(mfrow = c(2, 1), cex = 0.7) ; on.exit(par(op)) # Density: plot(s, f, type = "l", main = main1, xlim = x.rng, col = col) abline (h = 0, lty = 3) # Probability: plot(s, F, type = "l", xlim = x.rng, ylim = c(0, 1), col = col, main = main2) abline(h = 0:1, lty = 3) lines(c(par("usr")[1],med,med), c(0.5 ,0.5, 0), lty = 2, col=col.med) text(med, 0.1, "median", adj=0, col=col.med) axis(1, labels=expression(delta), at = delta, col = "red", col.axis="red", lwd=1.5, line = .5, tck = 1/8, hadj = -1, padj = -4) } # Open Slider Menu: .sliderMenu(refresh.code, names = c("no. points", "alpha","beta","gamma","delta", "pm"), minima = c( 50, 0. , -1. , 0. , -5. , 0), maxima = c( 1000, 2. , +1. , 5. , +5. , 2), resolutions = c( 50, 0.1, 0.1, 0.5, 0.5, 1), starts = c( 50, 1.8, 0. , 1. , 0. , 0)) } ################################################################################ fBasics/R/dist-gld.R0000644000176200001440000001246514263246022013670 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dgld Returns density for Generalized Lambda DF # pgld Returns probability for Generalized Lambda DF # qgld Returns quantiles for Generalized Lambda DF # rgld Returns random variates for Generalized Lambda DF ################################################################################ dgld <- function(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Details: # This a simple wrapper for the GLD using the RS parameterisation # in Region 4. The parameter range is restricted to negative lambda3 # and lambda4 parameters # Note: # Uses gld functions from package gld # Example: # dgld( (-25:25)/10 ) # FUNCTION: # Parameters: if (length(lambda1) == 4) { lambda4 = lambda1[4] lambda3 = lambda1[3] lambda2 = lambda1[2] lambda1 = lambda1[1] } # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Density: d = .dgld(x, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Log: if(log) d = log(d) # Return Value: d } # ------------------------------------------------------------------------------ pgld <- function(q, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns probability for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # Uses gld functions from package gld # Example: # pgld( (-25:25)/10 ) # FUNCTION: # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Probability: p = .pgld(q, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Return Value: p } # ------------------------------------------------------------------------------ qgld <- function(p, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # Uses gld functions from package gld # Example: # qgld((1:99)/100) # FUNCTION: # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Quantiles: q = .qgld(p, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Return Value: q } # ------------------------------------------------------------------------------ rgld <- function(n = 100, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns random variates for Generalized Lambda DF # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # Uses gld functions from package gld # FUNCTION: # Check Parameters: stopifnot (lambda2 < 0) stopifnot (lambda3 < 0) stopifnot (lambda4 < 0) # Random Variates: r = .rgld(n, lambda1=lambda1, lambda2=lambda2, lambda3=lambda3, lambda4=lambda4, param="rs") # Return Value: r } ################################################################################ fBasics/R/dist-hypMode.R0000644000176200001440000000707714650724111014532 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # hypMode Computes the hyperbolic mode # FUNCTION: DESCRIPTION: # .hyp[1234]Mode Internal functions called by 'hypMode' ################################################################################ hypMode <- function(alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1) { # A function implemented by Diethelm Wuertz # Modified by Georgi N. Boshnakov # Description: # Computes the mode of the Hyperbolic PDF # FUNCTION: # Return Value: if (pm == 1) .hyp1Mode(alpha, beta, delta, mu) else if (pm == 2) .hyp2Mode(alpha, beta, delta, mu) else if (pm == 3) .hyp3Mode(alpha, beta, delta, mu) else if (pm == 4) .hyp4Mode(alpha, beta, delta, mu) else stop("argument 'pm' must be one of the numbers 1,2,3 or 4") } # ------------------------------------------------------------------------------ .hyp1Mode <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the mode of the Hyperbolic PDF # FUNCTION: # Mode: ans = mu + delta * beta / sqrt(alpha^2 - beta^2) # Return Value: ans } # ------------------------------------------------------------------------------ .hyp2Mode <- function(zeta = 1, rho = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the hyperbolic mode in the 2nd parameterization # FUNCTION: # Parameter Change: alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho # Return Value: ans = hypMode(alpha, beta, delta, mu) ans } # ------------------------------------------------------------------------------ .hyp3Mode <- function(xi = 1/sqrt(2), chi = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the hyperbolic mode in the 3rd parameterization # FUNCTION: # Parameter Change: rho = chi / xi zeta = 1/xi^2 - 1 alpha = zeta / ( delta * sqrt(1 - rho*rho) ) beta = alpha * rho ans = hypMode(alpha, beta, delta, mu) # Return Value: ans } # ------------------------------------------------------------------------------ .hyp4Mode <- function(a.bar = 1, b.bar = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Computes the hyperbolic mode in the 4th parameterization # FUNCTION: # Parameter Change: alpha = a.bar / delta beta = b.bar / delta ans = hypMode(alpha, beta, delta, mu) # Return Value: ans } ################################################################################ fBasics/R/matrix-norm.R0000644000176200001440000000465314263246022014436 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # norm2 Returns the norm2 of a matrix ################################################################################ # IMPORTANT NOTICE: # The function fBasics::norm() has become obsolete, use instead: # base::norm(x, type) - Note, the arguments are different. # The original Rmetrics function is still available as fBasics:norm2() norm2 <- function(x, p = 2) { # A function implemented by Diethelm Wuertz # Description: # Returns the spectral norm of a matrix # Details: # http://mathworld.wolfram.com/MatrixNorm.html: # For p = 1 # The maximum absolute column sum norm |A|_1 is defined # as the maximum of the sum of the absolute valued elements # of columns of the matrix. # For p = 2: # The spectral |A|_2 norm is "the" of a matrix. This value # is computed as the square root of the maximum eigenvalue # of A^H A where A^H is the conjugate transpose. # For p = Inf: # The maximum absolute row sum norm |A|_inf is defined # as the maximum of the sum of the absolute valued elements # of rows of the matrix. # FUNCTION: # Compute Norm: ans <- NA if (p == 1) { x <- abs(x) ans <- max(apply(x, 2, sum)) } if (p == 2) { ans <- sqrt(max(eigen(t(x) %*% x)$values)) } if (p == Inf) { x <- abs(x) ans <- max(apply(x, 1, sum)) } if (is.na(ans)) stop("Invalid value for p") # Return value: ans } ################################################################################ fBasics/R/stats-interpLinear.R0000644000176200001440000001157714264011647015760 0ustar liggesusers# This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # linearInterp Interpolates linearly irregularly spaced data points # linearInterpp Interpolates linearly pointwise ################################################################################ linearInterp <- function(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints)) { # A function implemented by Diethelm Wuertz # Description: # Interpolates Linearly Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points spanning the grid # Note: # Extrapolation is not possible in the case of linear interpolation. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = linearInterp(x, y, z) # persp(ans, theta = -50, phi = 30, col = "steelblue") # FUNCTION: if (!requireNamespace("interp", quietly = TRUE)) stop("Needs Package 'interp' which is notinstalled") # Arguments: if (is.data.frame(x)) x <- as.matrix.data.frame(x) else if (is.list(x)) x <- matrix(unlist(x), ncol = 3) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolation: ans <- interp::interp(x, y, z, xo, yo, linear = TRUE, extrap = FALSE, duplicate = "median", dupfun = NULL) colnames(ans$z) <- as.character(signif(ans$x, round(log(gridPoints), 0))) rownames(ans$z) <- as.character(signif(ans$y, round(log(gridPoints), 0))) class(ans) <- "gridData" # Return Value: ans } # ------------------------------------------------------------------------------ linearInterpp <- function(x, y = NULL, z = NULL, xo, yo) { # A function implemented by Diethelm Wuertz # Description: # Interpolates Linearly Irregularly Distributed Data Points # Arguments: # x, y, z - either three numeric vectors of equal length or if # y and z are NULL, a list with entries x, y, a, or named # data.frame with x in the first, y in the second, and z in # the third column. # gridPoints - number of grid points in x and y direction. # xo, yo, a sequence of data points for pointwise interpolation # Note: # Extrapolation is not possible in the case of linear interpolation. # Value: # A list with three elements, $x and $y which are vectors of length # 'gridPoints' and $z which is a matrix of size 'gridPoints^2'. # Example: # set.seed(1953) # x = runif(999)-0.5; y = runif(999)-0.5; z = cos(2*pi*(x^2+y^2)) # ans = linearInterpp(x, y, z, c(mean(x), 0, 100), c(mean(y), 0, 100)) # persp(ans, theta = -50, phi = 30, col = "steelblue") # FUNCTION: if (!requireNamespace("interp", quietly = TRUE)) stop("Needs Package 'interp' which is not installed") # Arguments: if (is.data.frame(x)) x <- as.matrix.data.frame(x) else if (is.list(x)) x <- matrix(unlist(x), ncol = 3) if (is.matrix(x)) { z = x[, 3] y = x[, 2] x = x[, 1] } # Interpolation: interpp <- eval(parse(text=paste0("interp",":::","interpp"))) ans <- interpp(x, y, z, xo, yo, linear = TRUE, extrap = FALSE, duplicate = "median", dupfun = NULL) ## Return data.frame(x = ans$x, y = ans$y, z = ans$z) } ################################################################################ fBasics/R/matrix-colVec.R0000644000176200001440000000344614263246022014675 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # colVec Creates a column vector from a data vector # rowVec Creates a row vector from a data vector ################################################################################ colVec <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Converts a vector to a column vector # Details: # A column vector is a matrix with one column. # Return Value: # FUNCTION: # Double Transpose: ans = t(t(x)) # Return Value: ans } # ------------------------------------------------------------------------------ rowVec <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Converts a vector to a row vector # Details: # A row vector is a matrix with one row. # FUNCTION: # Transpose: ans = t(x) # Return Value: ans } ################################################################################ fBasics/R/utils-characterTable.R0000644000176200001440000000630314650724111016215 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # characterTable Shows a table of character codes ################################################################################ characterTable <- function(font = 1, cex = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Prints numeric equivalents to all latin characters. ## ## 2023-10-07 GNB: Actually the function had been showing the characters ## having codes below 256 in the current locale and working properly ## only for some of them. Today's explicitly sets latin1. ## ## TODO: This function could be safely removed, I think. # Notes: # The printed version doesn't allways corresponds to the # screen display. The character on line "xy" and column # "z" of the table has code "xyz". These codes can be # used as any other characters. # e.g. title("\347\340 et \340") # Note on S: # As the command line window of Splus can't print special # characters # cat("\347\340 et \340") # will not print the special characters, at least under # 4.5 and under 2000. # Author: # Source from Pierre Joyet, pierre.joyet@bluewin.ch # Example: # for (i in 1:20) characterTable(font = i) # FUNCTION: # Table: v = 40:377 v = v[v %% 100 < 80 & v %% 10 < 8] par(mar = c(5, 5, 4, 2) + 0.1) plot(-1:7, seq(4, 33, length = 9), type = "n", axes = FALSE, xlab = "", ylab = "", cex = cex, main = "Table of Characters") k = 1 for(i in 4:31) for(j in 0:7) { ## 2023-10-07 GNB: include encoding in the string to fix error on ## CRAN, see Hornik's report around this date. Note that the ## function had never worked properly in UTF8 locales but no ## errors were raised before. ch <- eval(parse(text = paste("\"\\", v[k], "\"", sep = ""))) Encoding(ch) <- "latin1" text(j, 35 - i, ch, font = font, cex = cex) k = k + 1 } text(0:7, rep(33, 7), as.character(0:7), font = 3, cex = cex) text(rep(-1, 28), 31:4, as.character(c(4:7, 10:17, 20:27, 30:37)), font = 3, cex = cex) # Return Value: invisible(font) } ################################################################################ fBasics/R/matrix-gridVector.R0000644000176200001440000000427714263246022015575 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gridVector Creates from two vectors rectangular grid coordinates ################################################################################ gridVector <- function(x, y = NULL) { # A function implemented by Diethelm Wuertz # Description: # Creates from two vectors rectangular grid coordinates # Arguments: # x, y - two numeric vectors of length m and n which span the # rectangular grid of size m times n. # Details: # The two vectors x and y span a rectangular grid with nx=length(x) # times ny=length(y) points which are returned as a matrix of size # (nx*ny) times 2. # Value: # returns a list with two elements X and Y each of length m # times n # Example: # > gridVector(1:3, 1:2) # [,1] [,2] # [1,] 1 1 # [2,] 2 1 # [3,] 3 1 # [4,] 1 2 # [5,] 2 2 # [6,] 3 2 # FUNCTION: # DW: if (is.null(y)) y = x # Prepare for Input: nx = length(x) ny = length(y) xoy = cbind(rep(x, ny), as.vector(matrix(y, nx, ny, byrow = TRUE))) X = matrix(xoy, nx * ny, 2, byrow = FALSE) # Return Value: list(X = X[,1], Y = X[,2]) } ################################################################################ fBasics/R/dist-tFit.R0000644000176200001440000000632514324527130014026 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # tFit Fits parameters of a Student-t density ################################################################################ tFit <- function(x, df = 4, doplot = TRUE, span = "auto", trace = FALSE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Return Maximum log-likelihood estimated # Note: # Function Calls: nlminb(), density() # Example: # tFit(rt(1000, df=4)) # FUNCTION: # Transform: x.orig = x x = as.vector(x) # Settings: CALL = match.call() # Log-likelihood Function: obj = function(x, y = x, trace) { # Prevent from negative df's if (x[1] <= 0) x[1] = getRmetricsOptions(".x.save") f = -sum(log(dt(y, x[1]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Students df Estimate: ", x[1], "\n") } setRmetricsOptions(.x.save = x[1]) f } # Minimization: r = nlm(f = obj, p = c(df), y = x, trace = trace) # Optional Plot: if (doplot) { if (span == "auto") { df = r$estimate[1] span.min = qt(0.001, df) span.max = qt(0.999, df) span = seq(span.min, span.max, length = 100) } par(err = -1) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dt(span, df = r$estimate[1]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("STUDENT-T: Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") if (exists("grid")) grid() } # Add Title and Description: if (is.null(title)) title = "Student-t Parameter Estimation" if (is.null(description)) description = "" # Fit: fit = list(estimate = c(df = r$estimate), minimum = -r$minimum, code = r$code, gradient = r$gradient) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Student-t Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } ################################################################################ fBasics/R/dist-gldFit.R0000644000176200001440000004751614324532103014334 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gldFit Fits parameters of a GLD # .gldFit.mle Fits parameters of a GLD using maximum log-likelihood # .gldFit.mps Fits parameters of a GLD using maximum product spacings # .gldFit.gof Fits parameters of a GLD using GoF Statistics # .ksGLD Kolmogorov Smirnov Statistics # .cvmGLD Cramer von Mise Statistics # .adGLD Anderson Darling Statistics # .gldFit.hist Fits parameters of a GLD using a histogram fit # type="fd" Freedman-Diaconis binning # type="scott" Scott binning # type="sturges" Sturges binning # .gldFit.rob Fits parameters of a GLD using robust moments fit ################################################################################ gldFit <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, method = c("mle", "mps", "gof", "hist", "rob"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using maximum log-likelihood # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Note: # The GLD uses RS parameterization and parameters in Region 4, # i.e. lambda[3,4]<0, lambda2<0 # FUNCTION: # Check Parameters: stopifnot(lambda2 < 0) stopifnot(lambda3 < 0) stopifnot(lambda4 < 0) # Settings: method = match.arg(method) # Parameter Fit: if (method == "mle") { ans = .gldFit.mle(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "mps") { ans = .gldFit.mps(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "gof") { ans = .gldFit.gof(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "hist") { ans = .gldFit.hist(x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } else if (method == "rob") { ans = .gldFit.rob (x, lambda1, lambda2, lambda3, lambda4, scale, doplot, add, span, trace, title, description, ...) } # Return Value: ans } # ------------------------------------------------------------------------------ .gldFit.mle <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using maximum log-likelihood # Example: # set.seed(4711); x=rgld(5000); fit=gldFit.mle(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Objective Function: obj <- function(x, y = x, trace) { DGLD = try(dgld(y, x[1], x[2], x[3], x[4]), silent = TRUE) if (inherits(DGLD, "try-error")) return(1e9) f = -sum(log(DGLD)) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), y = x, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 Parameter Estimation" if (is.null(description)) description = "" # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .gldFit.mps <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, type = c("sum", "mean", "max", "median", "var"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using maximum product spacings # Example: # set.seed(4711); x=rgld(5000); fit=.gldFit.mps(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE type = match.arg(type) CALL = match.call() # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Objective Function: TYPE = toupper(type) if(type == "sum") { typeFun = sum } else if (type == "mean") { typeFun = mean } else if (type == "median") { typeFun = median } else if (type == "max") { typeFun = function(x) -max(x) } else if (type == "var") { typeFun = function(x) -var(x) } obj = function(x, y = x, typeFun, trace) { PGLD = try(pgld(sort(y), x[1], x[2], x[3], x[4]), silent = TRUE) if (inherits(PGLD, "try-error")) return(1e9) DH = diff(c(0, na.omit(PGLD), 1)) f = try(-typeFun(log(DH[DH > 0])), silent = TRUE) if (inherits(PGLD, "try-error")) return(1e9) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), y = x, typeFun = typeFun, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 MPS Estimation" if (is.null(description)) description = "" # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .ksGLD <- function(N, PFGL) { D = 1/(2*N) + max ( abs( PFGL - ((1:N)-0.5)/N ) ) D } .cvmGLD <- function(N, PFGL) { W2 = 1/(12*N) + sum ( PFGL - ((1:N)-0.5)/N )^2 W2 } .adGLD <- function(N, PFGL) { A2 = -N -(1/N) * sum( (2*(1:N)-1) * ( log(PFGL) + log(1-rev(PFGL)) ) ) A2 } # ------------------------------------------------------------------------------ .gldFit.gof <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, type = c("ad", "ks", "cvm"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using GoF Statistics # Example: # set.seed(4711); x=rgld(5000); fit=.gldFit.gof(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE type = match.arg(type) CALL = match.call() # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Objective Function: TYPE = toupper(type) ## DJS 20/02/2010 ## typeFun <- match.fun(paste(".", type, sep = "")) typeFun <- match.fun(paste(".", type, "GLD", sep = "")) obj = function(x, y = x, typeFun, trace) { PFGL = try(pgld(sort(y), x[1], x[2], x[3], x[4]), silent = TRUE) if (inherits(PFGL, "try-error")) return(1e9) PFGL = PFGL[PFGL > 0] PFGL = PFGL[PFGL < 1] N = length(PFGL) f = typeFun(N, PFGL) if (is.na(f)) return(1e9) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4],"\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), y = x, typeFun = typeFun, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = paste("GLD Region 4", TYPE, "Estimation") if (is.null(description)) description = "" # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .gldFit.hist <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, type = c("fd", "sturges", "scott"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using a histogram fit # Example: # set.seed(4711); x=rgld(5000); fit=gldFit.hist(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE CALL = match.call() type = match.arg(type) if (type == "fd") type = "FD" else if (type == "scott") type = "Scott" else if (type == "sturges") type = "Sturges" # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Histogram: HIST = hist(x, breaks = type, plot = FALSE) # Objective Function: obj = function(x, hist, trace) { DFGL = try(dgld(hist$mids, x[1], x[2], x[3], x[4]), silent = TRUE) if (inherits(DFGL, "try-error")) return(1e9) DST = (hist$density - DFGL)^2 f = sum(DST) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), hist = HIST, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 Histogram Estimation" if (is.null(description)) description = "" # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .gldFit.rob <- function(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a GLD using robust moments (quantile) fit # Example: # set.seed(4711); x=rgld(5000); fit=.gldFit.rob(x)@fit$estimate; fit # FUNCTION: # Settings: scale = FALSE # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() xMED = sampleMED(x) xIQR = sampleIQR(x) xSKEW = sampleSKEW(x) xKURT = sampleKURT(x) # Objective Function: obj = function(x, xMED, xIQR, xSKEW, xKURT, trace) { lambda1 = x[1] lambda2 = x[2] lambda3 = x[3] lambda4 = x[4] ROB = try(sqrt( (xMED -gldMED (lambda1, lambda2, lambda3, lambda4))^2 + (xIQR -gldIQR (lambda1, lambda2, lambda3, lambda4))^2 + (xSKEW-gldSKEW(lambda1, lambda2, lambda3, lambda4))^2 + (xKURT-gldKURT(lambda1, lambda2, lambda3, lambda4))^2 ), silent = TRUE) if(inherits(ROB, "try-error")) return(1e9) f = ROB # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 100 r = nlminb( start = c(lambda1, lambda2, lambda3, lambda4), objective = obj, lower = c(-BIG, -BIG, -BIG, -BIG), upper = c(+BIG, -eps, -eps, -eps), xMED = xMED, xIQR = xIQR, xSKEW = xSKEW, xKURT = xKURT, trace = trace) names(r$par) <- c("lambda1", "lambda2", "lambda3", "lambda4") # Add Title and Description: if (is.null(title)) title = "GLD Region 4 Robust Moment Estimation" if (is.null(description)) description = "" # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list(estimate = r$par, minimum = -r$objective, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dgld(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), ...) } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main = title) lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "GLD Region 4 Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } ################################################################################ fBasics/R/plot-histPlot.R0000644000176200001440000002307314650724111014740 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: TAILORED DENSITY FUNCTIONS: # histPlot Returns a tailored histogram plot # densityPlot Returns a tailored kernel density estimate plot # logDensityPlot Returns a tailored log kernel density estimate plot # .plot.histogram Replaces here the function plot.histogram ################################################################################ histPlot <- function(x, labels = TRUE, col = "steelblue", fit = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a probability histogram plot for each column of a # timeSeries object # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Histogram Plots: for (i in 1:N) { # Histogram: X = as.vector(x[, i]) if (skip) X = X[X != 0] # Plot: if (labels) { H = hist(x = X, , breaks = "FD", plot = FALSE) .plot.histogram(H, col = col[i], freq = FALSE, ...) box() } else { H = hist(x = X, plot = FALSE, ...) .plot.histogram(H, col = col[i], freq = FALSE, ...) } # Add Title: if(title) { title(main = paste(Units[i], "Histogram"), xlab = "Value", ylab = "Probability") } # Add Fit: ## 2023-10-07 GNB: moved assignment to mean outside if(fit){...}, ## as otherwise v = mean(X) further below is wrong when fit = FALSE mean = mean(X) if (fit) { sd = sd(X) xlim = range(H$breaks) s = seq(xlim[1], xlim[2], length = 201) lines(s, dnorm(s, mean, sd), lwd = 2, col = "brown") } # Add Mean: if (labels) { abline(v = mean(X), lwd = 2, col = "orange") Text = paste("Mean:", signif(mean, 3)) mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) } # Add Grid: if (grid) grid() # Add Zero Line: if(labels) { abline(h = 0, col = "grey") } # Add Rug Plot: if(rug) { rug(X, ticksize = 0.01, quiet = TRUE) } } # Return Value: invisible() } # ------------------------------------------------------------------------------ densityPlot <- function(x, labels = TRUE, col = "steelblue", fit = TRUE, hist = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns density plots for each column of a timeSeries object # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Density Plots: for (i in 1:N) { # Density: X = as.vector(x[, i]) if (skip) X = X[X != 0] # Underlay Histogram: if (hist) { H = hist(x = X, , breaks = "FD", plot = FALSE) plot(x = H$mids, y = H$density, type = "h", lwd = 2, main = "", xlab = "", ylab = "", col = "grey") } # Plot Density: D = density(X, ...) if (hist) { lines(D$x, D$y, lwd = 2, col = "brown") } else { plot(D, col = col[i], ann = FALSE, ...) } # Add Title: if (title) { title(main = Units[i], xlab = "Value", ylab = "Density") } # Add Fit: if (fit) { mean = mean(X) sd = sd(X) xlim = range(H$breaks) s = seq(xlim[1], xlim[2], length = 201) lines(s, dnorm(s, mean, sd), lwd = 2, col = "darkgrey") } # Add Mean: if (labels) { abline(v = mean, lwd = 2, col = "orange") Text = paste("Mean:", signif(mean, 3)) mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) } # Add Grid: if (grid) { grid() } # Add Zero Line: if(labels) { abline(h = 0, col = "grey") } # Add Rug Plot: if(rug) { rug(X, ticksize = 0.01, quiet = TRUE) } } # Return Value: invisible() } # ------------------------------------------------------------------------------ logDensityPlot <- function(x, labels = TRUE, col = "steelblue", robust = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a pdf plot on logarithmic scale # Details: # Returns a pdf plot on a lin-log scale in comparison to a Gaussian # density plot Two type of fits are available: a normal density with # fitted sample mean and sample standard deviation, or a normal # density with Hubers robust mean and standard deviation corfrected # by the bandwidth of the Kernel estimator. # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Log Density: for (i in 1:N) { # Transform Data: X = as.vector(x[, i]) if (skip) X = X[X != 0] # Kernel and Histogram Estimators: Density = density(X, ...) Histogram = hist(X, breaks = "FD", plot = FALSE) # Plot Frame: plot(Histogram$mids, log(Histogram$density), type = "n", ann = FALSE, xlim = range(Density$x), ylim = log(range(Density$y)), ...) # Add Title: if(title) { title(main = paste(Units[i], "Log Density"), xlab = "Value", ylab = "Log Density") } # Add Kernel Density Estimated Points: points(Density$x, log(Density$y), pch = 19, cex = 0.7, col = "grey") # Sample Line Fit: s = seq(min(Density$x), max(Density$x), length = 1001) # Robust Fit: if (robust) { h = MASS::hubers(X) logDensity = log(dnorm(s, mean = h[[1]], sd = sqrt(h[[2]]^2+Density$bw^2))) minLogDensity = log(min(Density$y)) lines( x = s[logDensity > minLogDensity], y = logDensity[logDensity > minLogDensity], col = "red", lwd = 2) # Standard Fit: lines(s, log(dnorm(s, mean(X), sd(X))), col = "orange", lwd = 2) } # Plot Histogram: points(Histogram$mids, log(Histogram$density), pch = 151, col = "steelblue") # Grid: if (grid) { grid() } # Add Rug Plot: if(rug) { rug(x, ticksize = 0.01, quiet = TRUE) } } # Return Value: invisible() } ################################################################################ .plot.histogram <- function(x, freq = equidist, density = NULL, angle = 45, col = NULL, border = "white", lty = NULL, main = NULL, sub = NULL, xlab = NULL, ylab = NULL, xlim = range(x$breaks), ylim = NULL, axes = TRUE, labels = FALSE, add = FALSE, ...) { # This replacement of plot.histogram() suppresses title # printing which would be otherwise not possible! equidist <- if(is.logical(x$equidist)) x$equidist else { h <- diff(x$breaks) ; diff(range(h)) < 1e-7 * mean(h) } if(freq && !equidist) warning("the AREAS in the plot are wrong -- rather use freq=FALSE") y <- if (freq) x$counts else { ## x$density -- would be enough, but ## for back compatibility y <- x$density; if(is.null(y)) x$intensities else y} nB <- length(x$breaks) if(is.null(y) || 0 == nB) stop("'x' is wrongly structured") if(!add) { if(is.null(ylim)) ylim <- range(y, 0) if (missing(ylab)) ylab <- if (!freq) "Density" else "Frequency" plot.new() plot.window(xlim, ylim, "") #-> ylim's default from 'y' # title(main = main, sub = sub, xlab = xlab, ylab = ylab, ...) if(axes) { axis(1, ...) axis(2, ...) } } rect(x$breaks[-nB], 0, x$breaks[-1], y, col = col, border = border, angle = angle, density = density, lty = lty) if((logl <- is.logical(labels) && labels) || is.character(labels)) text(x$mids, y, labels = if(logl) { if(freq) x$counts else round(x$density,3) } else labels, adj = c(0.5, -0.5)) invisible() } ################################################################################ fBasics/R/plot-seriesPlot.R0000644000176200001440000001044614263246022015263 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # seriesPlot Dispalys a time Series Plot # cumulatedPlot Displays a cumulated series given the returns # returnPlot Displays returns given the cumulated series # drawdownPlot Displays drawdown series from returns ################################################################################ seriesPlot <- function(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Dispalys a time Series Plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) N = NCOL(x) Units = colnames(x) if (length(col) == 1) col = rep(col, times = N) # Series Plots: for (i in 1:N) { X = x[, i] plot(x = X, type = type, col = col[i], ann = FALSE, ...) # Add Title: if (title) { title(main = Units[i], xlab = "Time", ylab = "Value") } else { title(...) } # Add Grid: if(grid) grid() # Add Box: if(box) box() # Add Rugs: if(rug) rug(as.vector(X), ticksize = 0.01, side = 2, quiet = TRUE) } # Return Value: invisible() } # ------------------------------------------------------------------------------ cumulatedPlot <- function(x, index = 100, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a cumulated series given the returns # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) x = index * exp(colCumsums(x)) seriesPlot(x, labels = labels, type = type, col = col, title = title, grid = grid, box = box, rug = rug, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ returnPlot <- function(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays returns given the cumulated series # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) x = returns(x, ...) seriesPlot(x, labels = labels, type = type, col = col, title = title, grid = grid, box = box, rug = rug, ...) # Return Value: invisible() } # ------------------------------------------------------------------------------ drawdownPlot <- function(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays drawdowns given the return series # FUNCTION: # timeSeries: stopifnot(is.timeSeries(x)) x = drawdowns(x, ...) seriesPlot(x, labels = labels, type = type, col = col, title = title, grid = grid, box = box, rug = rug, ...) # Return Value: invisible() } ################################################################################ fBasics/R/dist-hypRobMoments.R0000644000176200001440000001015414263246022015721 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # hypMED Returns true hyp median # hypIQR Returns true hyp inter quartal range # hypSKEW Returns true hyp robust skewness # hypKURT Returns true hyp robust kurtosis ################################################################################ hypMED <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh median # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Median Q = qgh(p=0.5, alpha, beta, delta, mu, lambda=1) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ hypIQR <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh inter quartal range # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Inter Quartile Range Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda=1) Q[2] = qgh(p=0.75, alpha, beta, delta, mu, lambda=1) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ hypSKEW <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh skewness # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Skewness: Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda=1) Q[2] = qgh(p=0.50, alpha, beta, delta, mu, lambda=1) Q[3] = qgh(p=0.75, alpha, beta, delta, mu, lambda=1) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ hypKURT <- function(alpha = 1, beta = 0, delta = 1, mu = 0) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh kurtosis # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qgh(p, alpha, beta, delta, mu, lambda=1)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/plot-qqPlot.R0000644000176200001440000002375714263246022014423 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # qqnormPlot Returns a tailored normal quantile-quantile plot # qqnigPlot Returns a tailored NIG quantile-quantile plot # qqghtPlot Returns a tailored GHT quantile-quantile plot # qqgldPlot Returns a tailored LD quantile-quantile plot ################################################################################ qqnormPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Example of a Normal quantile plot of data x to provide a visual # assessment of its conformity with a normal (data is standardised # first). # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Details: # The ordered data values are posterior point estimates of the # underlying quantile function. So, if you plot the ordered data # values (y-axis) against the exact theoretical quantiles (x-axis), # you get a scatter that should be close to a straight line if the # data look like a random sample from the theoretical distribution. # This function chooses the normal as the theory, to provide a # graphical/visual assessment of how normal the data appear. # To help with assessing the relevance of sampling variability on # just "how close" to the normal the data appears, we add (very) # approximate posterior 95% intervals for the uncertain quantile # function at each point (Based on approximate theory) . # Author: # Based on code written by Mike West, mw@stat.duke.edu # Note: # Source from # http://www.stat.duke.edu/courses/Fall99/sta290/Notes/ # Example: # x = rnorm(100); qqnormPlot(x); qqnormPlot(x, labels = FALSE) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = x@units x = as.vector(x) n = length(x) # Fit: p = (1:n)/(n+1) if (scale) x = (x-mean(x))/sqrt(var(x)) par = c(mean = mean(x), var = var(x)) # Quantiles: x = sort(x) p = ppoints(x) if (scale) z = qnorm(p) else z = qnorm(p, mean(x), sd(x)) # Plot: if (labels) { xlab = "Normal Quantiles" ylab = paste(Units, "Ordered Data") plot(z, x, xlab = xlab, ylab = ylab, col = col, pch = 19, ...) } else { plot(z, x, ...) } # Title: if(title) { title(main = "NORM QQ PLOT") } # Margin Text: if (mtext) { Text = "Confidence Intervals: 95%" mtext(Text, side = 4, adj = 0, col = "darkgrey", cex = 0.7) } # Grid: if (grid) { grid() } # Add Diagonal Line: abline(0, 1, col = "grey") # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 1, quiet = TRUE) rug(x, ticksize = 0.01, side = 2, quiet = TRUE) } # 95% Confidence Intervals: s = 1.96*sqrt(p*(1-p)/n) pl = p-s i = pl<1 & pl>0 lower = quantile(x, probs = pl[i]) lines(z[i], lower, col = "brown") pl = p+s i = pl < 1 & pl > 0 upper = quantile(x, probs = pl[i]) lines(z[i], upper, col = "brown") abline(h = mean(x), col = "grey") abline(v = mean(x), col = "grey") # Result: ans = list(x = z, y = x) attr(ans, "control")<-par # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ qqnigPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a NIG quantile-quantile Plot # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Example: # qqnigPlot(rnig(100)) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) stopifnot(isUnivariate(x)) Units = x@units x = as.vector(x) n = length(x) ## YC: no scaling ## FIXME: should take care of too small time series # Fit: fit = nigFit(x, doplot = FALSE, trace = FALSE) par = fit@fit$estimate names(par) = c("alpha", "beta", "delta", "mu") # Quantiles: x = sort(x) p = ppoints(x) z = qnig(p, par[1], par[2], par[3], par[4]) # Plot: if (labels) { xlab = "Theoretical Quantiles" ylab = "Sample Quantiles" plot(z, x, xlab = xlab, ylab = ylab, col = col, pch = pch, ...) } else { plot(z, x, ...) } # Title: if (title) { title(main = "NIG QQ Plot") } # Margin Text: rpar = signif(par, 3) text = paste( "alpha =", rpar[1], "| beta =", rpar[2], "| delta =", rpar[3], "| mu =", rpar[4]) mtext(text, side = 4, adj = 0, col = "grey", cex = 0.7) # Grid: if (grid) { grid() } # Add Fit: abline(lsfit(z, x)) # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 3, quiet = TRUE) rug(x, ticksize = 0.01, side = 4, quiet = TRUE) } # Result: ans = list(x = z, y = x) attr(ans, "control") <- par # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ qqghtPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a GHT quantile-quantile Plot # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Example: # qqnigPlot(rgh(100)) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) stopifnot(isUnivariate(x)) Units = x@units x = as.vector(x) n = length(x) # Fit: fit = ghtFit(x, doplot = FALSE, trace = FALSE) par = fit@fit$estimate names(par) = c("beta", "delta", "mu", "nu") # Plot: x <- sort(x) p <- ppoints(x) z <- qght(p, par[1], par[2], par[3], par[4]) if (labels) { plot(z, x, col = col, ann = FALSE, ...) } else { plot(z, x, ...) } # Add Grid: if (grid) { grid() } # Add title: if (title) { title( main = "GHT QQ Plot", xlab = "Theoretical Quantiles", ylab = "Sample Quantiles") } # Add Fit: abline(lsfit(z, x)) # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 3, quiet = TRUE) rug(x, ticksize = 0.01, side = 4, quiet = TRUE) } # Result: ans = list(x = z, y = x) attr(ans, "control")<-par # Return Value: invisible(ans) } ################################################################################ qqgldPlot <- function(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Displays a Generalized lambda Distribution quantile-quantile Plot # Arguments: # x - an univariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # Example: # qqgldPlot(rgld(100)) # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) stopifnot(isUnivariate(x)) Units = x@units x = as.vector(x) n = length(x) ## YC: no scaling ## FIXME: should take care of too small time series # Fit: fit = gldFit(x, doplot = FALSE, trace = FALSE) par = fit@fit$estimate names(par) = c("lambda1", "lambda2", "lambda3", "lambda4") # Quantiles: x = sort(x) p = ppoints(x) z = qgld(p, par[1], par[2], par[3], par[4]) # Plot: if (labels) { xlab = "Theoretical Quantiles" ylab = "Sample Quantiles" plot(z, x, xlab = xlab, ylab = ylab, col = col, pch = pch, ...) } else { plot(z, x, ...) } # Title: if (title) { title(main = "NIG QQ Plot") } # Margin Text: rpar = signif(par, 3) text = paste( "lambda1 =", rpar[1], "| lambda2 =", rpar[2], "| lambda3 =", rpar[3], "| lambda4 =", rpar[4]) mtext(text, side = 4, adj = 0, col = "grey", cex = 0.7) # Grid: if (grid) { grid() } # Add Fit: abline(lsfit(z, x)) # Add Rugs: if(rug) { rug(z, ticksize = 0.01, side = 3, quiet = TRUE) rug(x, ticksize = 0.01, side = 4, quiet = TRUE) } # Result: ans = list(x = z, y = x) attr(ans, "control") <- par # Return Value: invisible(ans) } ################################################################################fBasics/R/builtin-glGld.R0000644000176200001440000005663214264011647014666 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .pgld # .qgld # .qgl.fmkl # .qgl.fm5 # .qgl.rs # .qdgld # .qdgl.rs # .qdgl.fmkl # .qdgl.fm5 # .rgld # .gl.parameter.tidy # .gl.check.lambda ################################################################################ # Code borrowed from # R's contributed package "gld" written by Robert King # Rmetrics: # Note that gmm is not available on Debian as of 2009-11-11. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. # Package: gld # Version: 1.8.4 # Date: 2008/10/01 # Title: Estimation and use of the generalised (Tukey) lambda distribution # Author: Robert King # Maintainer: Robert King # Description: The generalised lambda distribution, or Tukey lambda # distribution, provides a wide variety of shapes with one # functional form. This package provides random numbers, # quantiles, probabilities, densities and plots. It also # includes an implementation of the starship estimation method # for the distribution. # License: GPL (>= 2) # URL: http://tolstoy.newcastle.edu.au/~rking/publ/rprogs/information.html # Packaged: 2009-10-14 ################################################################################ .dgld <- function(x, lambda1=0, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL, inverse.eps=1e-8, max.iterations=500) { # Tidy the parameters so gl.check.lambda will work lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the parameters if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", lambdas, "\ndo not produce a proper distribution with the", param, "parameterisation - see \ndocumentation for .gl.check.lambda")) } # calculate u=F(x) numerically, then use qdgl # Unless x is outside the range, then density should be zero extreme <- .qgld(c(0, 1), lambda1=lambdas, param=param) # It may be better to change this to simply # (x <= extreme[2])*(x >= extreme[1]) outside.range <- !as.logical((x<=extreme[2])*(x>=extreme[1])) u <- .pgld(x, lambdas, param=param, inverse.eps=inverse.eps, max.iterations=max.iterations) dens <- .qdgld(u, lambda1=lambdas, param=param) dens[outside.range] <- 0 dens } ################################################################################ .pgld <- function(q, lambda1=0, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL, inverse.eps=1e-8, max.iterations=500) { # Thanks to Steve Su, , for improvements to this code # If lambda1 is a vector, the default value for lambda2 will cause a # problem. # I did have a warning about this, but it will occur too often to make # up for the benefit, so I've deleted it. # Tidy the parameters so gl.check.lambda will work lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the parameters if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", lambda1, lambda2, lambda3, lambda4, "\ndo not produce a proper distribution with the", param, "parameterisation - see \ndocumentation for gl.check.lambda")) } jr <- q; jr[sort.list(q)] <- seq(along=q) order.x<-order(q) xx<-sort(q) # xx has the sorted data, and jr & order.x the information to get back # to theoriginal order. extreme <- .qgld(c(inverse.eps, 1-inverse.eps), lambda1=lambdas, param=param) max.e<-extreme[2] min.e<-extreme[1] ind.min<-xx<=min.e ind.max<-xx>=max.e # This simpler comparison works here because we are using inverse.eps as our # tolerance q<-xx[as.logical((xxmin.e))] # We only want to calculate the probabilities for q values inside # the support length.of.vector <- length(q) # Need a blank u to send to C u <- 0*q result <- switch(param, freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, # Notes on .C call - the "numerics", lambdas and inverse.eps # don't need the as.??? call as they are implicitly double fmkl=.C( C_gl_fmkl_distfunc, lambdas[1], lambdas[2], lambdas[3], lambdas[4], as.double(0), as.double(1), inverse.eps, as.integer(max.iterations), as.double(q), as.double(u), as.integer(length.of.vector)), ramberg=, # Ramberg & Schmeiser ram=, RS=, rs=.C( C_gl_rs_distfunc, lambdas[1], lambdas[2], lambdas[3], lambdas[4], as.double(0), as.double(1), inverse.eps, as.integer(max.iterations), as.double(q), as.double(u), as.integer(length.of.vector)), fm5=.C(C_gl_fm5_distfunc, lambdas[1], lambdas[2], lambdas[3], lambdas[4], lambdas[5], as.double(0), as.double(1), inverse.eps, as.integer(max.iterations), as.double(q), as.double(u), as.integer(length.of.vector)), stop("Error: Parameterisation must be one of fmkl, fm5 or rs") ) # closes "switch" if (!(is.numeric(result[[1]]))){ stop("Values for quantiles outside range. This shouldn't happen") } if (param=="fm5") { u <- result[[11]] } else { u <- result[[10]] } xx[as.logical((xxmin.e))]<-u xx[ind.min]<-0 xx[ind.max]<-1 # Revert to the original order of the dataset: xx[jr] } ################################################################################ .qgld <- function(p, lambda1, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the values are OK if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution for the", param, "parameterisation \n - see documentation for gl.check.lambda")) } result <- switch(param, freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, fmkl=.qgl.fmkl(p, lambdas), ramberg=, # Ramberg & Schmeiser ram=, RS=, rs=.qgl.rs(p, lambdas), fm5 = .qgl.fm5(p, lambdas), stop("Error: Parameterisation must be fmkl, fm5 or rs") ) # closes "switch" result } # ------------------------------------------------------------------------------ .qgl.fmkl <- function(p, lambdas) { # No checking - use qgl if you want that lambda4 = lambdas[4] lambda3 = lambdas[3] lambda2 = lambdas[2] lambda1 = lambdas[1] p <- as.double(p) # abandoned this for the simpler one below # outside.range <- !as.logical(((p<1)*(p>0))| # (sapply(p, all.equal, 1)=="TRUE")| (sapply(p, all.equal, 0)=="TRUE")) outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # If OK, determine special cases if (lambda3 == 0) { if (lambda4 == 0) { # both log quants <- lambda1 + (log(u) - log(1 - u))/lambda2 } else { # l3 zero, l4 non-zero quants <- lambda1 + (log(u) - ((1 - u)^lambda4 - 1)/lambda4)/lambda2 } } else { # lambda3 non-zero if (lambda4 == 0) { # non-zero, l4 zero quants <- lambda1 + ((u^lambda3 - 1)/lambda3 - log(1 - u))/lambda2 } else { # both non-zero - use usual formula quants <- lambda1 + ( ( u ^ lambda3 - 1) / lambda3 - ( (1-u)^lambda4 - 1) /lambda4 ) / lambda2 } } # Now we have the quantiles for p values inside [0, 1], put them in # the right place in the result vector result <- p*NaN result[!outside.range] <- quants # The remaining "quantiles" are NaN result } # ------------------------------------------------------------------------------ .qgl.fm5 <- function(p, lambdas) { # No parameter value checking. If you want that, use qgl! lambda5 = as.double(lambdas[5]) lambda4 = as.double(lambdas[4]) lambda3 = as.double(lambdas[3]) lambda2 = as.double(lambdas[2]) lambda1 = as.double(lambdas[1]) p <- as.double(p) # abandoned this for the simpler # outside.range <- !as.logical(((p<1)*(p>0))| # (sapply(p, all.equal, 1)=="TRUE")| (sapply(p, all.equal, 0)=="TRUE")) outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # If OK, determine special cases if (lambda3 == 0) { if (lambda4 == 0) { # both log quants <- lambda1 + ((1-lambda5)*log(u) - (1+lambda5)*log(1 - u))/lambda2 } else { # l3 zero, l4 non-zero quants <- lambda1 + ((1-lambda5)*log(u) - (1+lambda5)*((1 - u)^lambda4 - 1)/lambda4)/lambda2 } } else { # lambda3 non-zero if (lambda4 == 0) { # non-zero, l4 zero quants <- lambda1 + ((1-lambda5)*(u^lambda3 - 1)/lambda3 - (1+lambda5)*log(1 - u))/lambda2 } else { # both non-zero - use usual formula quants <- lambda1 + ((1-lambda5)* ( u ^ lambda3 - 1) / lambda3 - (1+lambda5)*( (1-u)^lambda4 - 1) /lambda4 ) / lambda2 } } # Now we have the quantiles for p values inside [0, 1], put them in # the right place in the result vector result <- p*NaN result[!outside.range] <- quants # The remaining "quantiles" are NaN result } # ------------------------------------------------------------------------------ .qgl.rs <- function(p, lambdas) { u <- p # No parameter value checking - use qgl! lambda4 = lambdas[4] lambda3 = lambdas[3] lambda2 = lambdas[2] lambda1 = lambdas[1] # At present, I'm rejecting zero values for l3 and l4, though I think # there are limit results, so one functional form. quants <- lambda1 + ( u ^ lambda3 - (1-u)^lambda4 ) / lambda2 quants } # ------------------------------------------------------------------------------ .qdgld <- function(p, lambda1, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { # Don't allow characters in lambda5 - # common error with parameterisation stuff if(is.character(lambda5)) { stop(paste("lambda5=", lambda5, "It should be a number between -1 and 1")) } lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the values are OK if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution for the", param, "parameterisation \n - see documentation for gl.check.lambda")) } result <- switch(param, # Different tests apply for each parameterisation freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, fmkl=.qdgl.fmkl(p, lambdas), ramberg=, # Ramberg & Schmeiser ram=, RS=, rs=.qdgl.rs(p, lambdas), fm5 = .qdgl.fm5(p, lambdas), stop("Error: Parameterisation must be fmkl, fm5 or rs") ) # closes "switch" result } # ------------------------------------------------------------------------------ .qdgl.rs <- function(p, lambdas) { # Check the values are OK) if(!.gl.check.lambda(lambdas, param="rs", vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution with the RS parameterisation", " - see \ndocumentation for gl.check.lambda")) } outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] dens <- lambdas[2]/(lambdas[3] * (p^(lambdas[3] -1)) + lambdas[4] * ((1 - p)^(lambdas[4] -1))) dens } # ------------------------------------------------------------------------------ .qdgl.fmkl <- function(p, lambdas) { # Check the values are OK) if(!.gl.check.lambda(lambdas, param="fmkl", vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution with the FMKL", "parameterisation - see \ndocumentation for gl.check.lambda")) } outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # The density is given by 1/Q'(u) dens <- lambdas[2]/(p^(lambdas[3] - 1) + (1 - p)^(lambdas[4] - 1)) dens } # ------------------------------------------------------------------------------ .qdgl.fm5 <- function(p, lambdas) { # Check the values are OK) if(!.gl.check.lambda(lambdas, param="fm5", vect=TRUE)) { stop(paste("The parameter values", paste(lambdas, collapse=" "), "\ndo not produce a proper distribution with the FM5", "parameterisation - see \ndocumentation for gl.check.lambda")) } outside.range <- !as.logical((p<=1)*(p>=0)) # u gets only the probabilities in [0, 1] u <- p[!outside.range] # The density is given by 1/Q'(u) dens <- lambdas[2]/((1-lambdas[5])*(u^(lambdas[3] - 1)) + (1+lambdas[5])*((1 - u)^(lambdas[4] - 1)) ) dens } ################################################################################ .rgld <- function(n, lambda1=0, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { # Check the parameters lambdas <- .gl.parameter.tidy(lambda1, lambda2, lambda3, lambda4, param, lambda5) # Check the values are OK if(!.gl.check.lambda(lambdas, param=param, vect=TRUE)) { stop(paste("The parameter values", lambdas, "\ndo not produce a proper distribution for the", param, "parameterisation \n - see documentation for gl.check.lambda")) } # Produce the uniform data p <- runif(n) # convert to gl res <- .qgld(p, lambda1=lambdas, param=param) res } ################################################################################ .gl.parameter.tidy <- function(lambda1, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL) { # Don't allow characters in lambda5 - common error with parameterisation stuff if(is.character(lambda5)) {stop(paste("lambda5=", lambda5, "It should be a number between -1 and 1"))} # Don't allow numbers in parameterisation - # included as a warning here, so the main one is a stop. if(!is.character(param)) {warning(paste("param=", param, "It shouldn't be a number, ", "it should be a string describing the parameterisation")) } if(length(lambda1) > 1) { # using a vector for the parameters. # Check that there aren't values in the individual lambda arguments if (!(is.null(lambda2) & is.null(lambda3)& is.null(lambda4) & is.null(lambda5)) ) { stop("Call includes vector version of the lambda parameters", "as well as the \nscalar version") } if ((length(lambda1) < 4) | (length(lambda1) > 5 ) ) { stop(paste("argument lambda1 has length", length(lambda1), "\nThis should be 1 (lambda parameters given as seperate ", "arguments), 4 (vector argument \n for RS or FMKL ", "parameterisation) or 5 (vector argument for fm5", "parameterisation")) } if (length(lambda1)== 5) { if (param != "fm5") { stop(paste("argument lambda1 has length", length(lambda1), "which is not valid for the", param, "\nparameterisation")) } # else --- fm5, in vector form, ready for gl.check.lambda } if (length(lambda1)== 4) { if (param == "fm5" ) { stop(paste("argument lambda1 has length 4, which is not", " valid for the fm5 \nparameterisation")) } # else --- 4 parameter versions in vector form, ready for gl.check.lambda } } else { # single parameter arguments - check they are there, # then collect them together if (is.null(lambda2)) { stop("No value for lambda2") } if (is.null(lambda3)) { stop("No value for lambda3") } if (is.null(lambda4)) { stop("No value for lambda4") } if ((is.null(lambda5)) & param=="fm5" ) { stop("No value for lambda5") } if (!(is.null(lambda5)) & param!="fm5") { stop(paste("lambda5=", lambda5, " but there is no lambda 5 for the\n", param, "parameterisation")) } if (param != "fm5") { # A 4 parameter version lambda1 <- c(lambda1, lambda2, lambda3, lambda4) } else { # fm5 lambda1 <- c(lambda1, lambda2, lambda3, lambda4, lambda5) } } # There is now an error if there is the wrong number of parameters, and # lambda1 returned as a vector with 4 or 5 elements # as.double is needed to remove data.frame attributes if lambda1 was # extracted from a data.frame as.double(lambda1) } # ------------------------------------------------------------------------------ .gl.check.lambda <- function(lambdas, lambda2=NULL, lambda3=NULL, lambda4=NULL, param="fmkl", lambda5=NULL, vect=FALSE) { # Checks to see that the lambda values given are allowed. # There is a function called .gl.parameter.tidy that does the tidying # around of parameters. It return a single vector, which contains the # parameters. # If you call this after .gl.parameter.tidy, let it know with the vect=TRUE # argument # If vect=TRUE, we don't need to tidy if (vect) { if (!is.null(lambda3)) { warning("lambda3 should be null because you claim the", " parameters are in a vector") } } else { lambdas <- .gl.parameter.tidy(lambdas, lambda2, lambda3, lambda4, param, lambda5) } if(param=="fm5"){lambda5 = lambdas[5]} lambda4 = lambdas[4] lambda3 = lambdas[3] lambda2 = lambdas[2] lambda1 = lambdas[1] # I did have a check for finite lambdas, but that caused a # problem with data frames, # so I removed it - still need to include the limit results param <- switch(param, # Different tests apply for each parameterisation freimer=, # allows for alternate expressions frm=, # allows for alternate expressions FMKL=, fmkl={ if (lambda2<=0) {return(FALSE)} else {return(TRUE)} }, ramberg=, # Ramberg & Schmeiser ram=, RS=, rs={ if (lambda3*lambda4>0) { # regions 3 and 4 # all values of lambda 3 and lambda 4 OK # check lambda 2 if ((lambda3>0)&(lambda4>0)) { # region 3 - l2 >0 if (lambda2<=0) { ret <- FALSE } else { ret <- TRUE } } if ((lambda3<0)&(lambda4<0)) { # region 4 - l2 <0 if (lambda2>=0) { ret <- FALSE } else { ret <- TRUE } } } else { # other quadrants - lambda 2 must be negative, and lambda3 # lambda 4 values need checking. if (lambda2>=0) {return(FALSE)} # Rectangular regions where RS is not defined if ((lambda3>0)&(lambda3<1)&(lambda4<0)) {return(FALSE)} if ((lambda4>0)&(lambda4<1)&(lambda3<0)) {return(FALSE)} # Different here because there are a # number of ways in which the parameters can fail. # # Curved regions where RS is not defined # change to shorter var names lc <- lambda3 ld <- lambda4 if ((lambda3>-1)&(lambda3<0)&(lambda4>1)) { # region 5 or not? if ( ((1-lc)^(1-lc)*(ld-1)^(ld-1))/((ld-lc)^(ld-lc)) > -lc/ld ) {return(FALSE)} else {return(TRUE)} } # Second curved region if ((lambda4>-1)&(lambda4<0)&(lambda3>1)) { # region 6 or not? if ( ((1-ld)^(1-ld)*(lc-1)^(lc-1))/((lc-ld)^(lc-ld)) > -ld/lc ) {return(FALSE)} else {return(TRUE)} } # There may be some limit results that mean these are not correct, but # I'll check that later # This is not the place where the possible l3, l4 zero values should appear if (lambda3 == 0) { warning('lambda 3 = 0 with RS parameterisation - possible problem') if (lambda4 == 0) {return(FALSE)} else {return(TRUE)} } if (lambda4 == 0) { warning('lambda 5 = 0 with RS parameterisation - possible problem') if (lambda4 == 0) {return(FALSE)} else {return(TRUE)} } # If we get here, then the parameters are OK. ret <- TRUE } }, fm5={ # make lambda5 - in here so it doesn't stuff up the # other parameterisations lambda5 <- lambdas[5] if (lambda2<=0) {ret <- FALSE} else { #Check lambda5 - should be between -1 and 1, but # I haven't checked this against a piece of paper if ((lambda5 >= -1) & (lambda5 <= 1)) { ret <- TRUE } else { ret <- FALSE } } }, stop("Error when checking validity of parameters.\n", " Parameterisation must be fmkl, rs or fm5") ) # closes "switch" ret } ################################################################################ fBasics/R/dist-sght.R0000644000176200001440000001033614263246022014062 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # dsght Standardized generalized hyperbolic Student-t # psght Probability of the Standardized GHT # qsght Quantiles of the Standardized GHT # rsght Random variates of the Standardized GHT ################################################################################ dsght <- function(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Returns density of the generalized hyperbolic Student-t # Arguments: # Example: # x = (-5):5; dght(x) # FUNCTION: # Parameters: if (length(beta) == 4) { nu = beta[4] mu = beta[3] delta = beta[2] beta = beta[1] } # Density: ans = dgh(x, alpha = abs(beta) + 1e-6, beta, delta, mu, lambda = -nu/2, log = log) # Return Value: ans } # ------------------------------------------------------------------------------ psght <- function(q, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities of the generalized hyperbolic Student-t # Arguments: # Example: # q = (-5):5; pght(q) # FUNCTION: # Cumulative Probability: ans = NULL for (Q in q) { Integral = integrate(dght, -Inf, Q, stop.on.error = FALSE, beta = beta, delta = delta, mu = mu, nu = nu) ans = c(ans, as.numeric(unlist(Integral)[1]) ) } # Return Value: ans } # ------------------------------------------------------------------------------ qsght <- function(p, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles of the generalized hyperbolic Student-t # Arguments: # Example: # p = (1:9)/10; qght(p); round(pght(qght(p)), digits = 4) # FUNCTION: # Internal Functions: .froot <- function(x, beta = beta, delta = delta, mu = mu, nu = nu, p) { pght(q = x, beta = beta, delta = delta, mu = mu, nu = nu) - p } # Loop over all p's: result = NULL for (pp in p) { lower = -1 upper = +1 counter = 0 iteration = NA while (is.na(iteration)) { iteration = .unirootNA(f = .froot, interval = c(lower, upper), beta = beta, delta = delta, mu = mu, nu = nu, p = pp) counter = counter + 1 lower = lower - 2^counter upper = upper + 2^counter } result = c(result, iteration) } # Return Value: ans = result + mu ans } # ------------------------------------------------------------------------------ rsght <- function(n, beta = 0.1, delta = 1, mu = 0, nu = 10) { # A function implemented by Diethelm Wuertz # Description: # Returns random Variates of generalized hyperbolic Student-t # Arguments: # Example: # r = rght(10) # FUNCTION: # Random Variates: x = rgh(n, alpha = abs(beta) + 1e-6, beta = beta, delta = delta, mu = mu, lambda = -nu/2) # Return Value: x } ################################################################################ fBasics/R/builtin-varpieA2R.R0000644000176200001440000000165314263246022015415 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # varpie ################################################################################ fBasics/R/dist-distPlot.R0000644000176200001440000000334114263246022014715 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ .distFitPlot <- function(fit, x, FUN = "dnig", main = "Parameter Estimation", span = "auto", add = FALSE, ...) { x = as.vector(x) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] # The Density function must accept multiple parameters # from the first parameter dFun = match.fun(FUN) y.points = dnig(span, fit$par) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title(main) lines(x = span, y = log(y.points), col = "steelblue") } } ################################################################################ fBasics/R/utils-colorPalettes.R0000644000176200001440000005673714263246022016151 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: COLOR PALETTES: # rainbowPalette Creates a rainbow color palette # heatPalette Creates a heat color palette # terrainPalette Creates a terrain color palette # topoPalette Creates a topo color palette # cmPalette Creates a cm color palette # greyPalette Creates a grey palette # timPalette Creates a cyan, yellow, to orange palette # FUNCTION: COLOR RAMPS: # rampPalette Creates a color ramp palette # seqPalette Creates a sequential color palette # divPalette Creates a diverging color palette # qualiPalette Creates a qualitative color palette # focusPalette Creates a focus color palette # monoPalette Creates a mono color palette ################################################################################ ################################################################################ # FUNCTION: DESCRIPTION: # rainbowPalette Creates a rainbow color palette # heatPalette Creates a heat color palette # terrainPalette Creates a terrain color palette # topoPalette Creates a topo color palette # cmPalette Creates a cm color palette rainbowPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: rainbow(n = n, ...) } # ------------------------------------------------------------------------------ heatPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: heat.colors(n, ...) } # ------------------------------------------------------------------------------ terrainPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: terrain.colors(n, ...) } # ------------------------------------------------------------------------------ topoPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: topo.colors(n, ...) } # ------------------------------------------------------------------------------ cmPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: cm.colors(n, ...) } ################################################################################ # FUNCTION: COLOR/GREY PALETTES: # greyPalette Creates a grey palette # timPalette Creates a cyan, yellow, to orange palette greyPalette <- function(n = 64, ...) { # A function implemented by Diethelm Wuertz # Description: # Create a vector of n gamma-corrected gray colors. # Arguments: # n - the number of greys to be constructed # start, end - the range of the color palette # gamma a gamma-correction # Value: # returns a grey palette like rainbow does # for color palettes # FUNCTION: # Compose: ans = gray.colors(n, ...) # Return Value: ans } # ------------------------------------------------------------------------------ timPalette <- function(n = 64) { # A function implemented by Diethelm Wuertz # Description: # Creates a cyan, yellow, to orange palette # Notes: # 'Tim.colors' in 'fields' package goes from blue to red, and passes # through the colors cyan, yellow, and orange. Also known as Jet # color-map in Matlab. You can also easily design your own color map # using 'rgb' function from 'gdDevices'. # From: # FUNCTION: orig = c( "#00008F", "#00009F", "#0000AF", "#0000BF", "#0000CF", "#0000DF", "#0000EF", "#0000FF", "#0010FF", "#0020FF", "#0030FF", "#0040FF", "#0050FF", "#0060FF", "#0070FF", "#0080FF", "#008FFF", "#009FFF", "#00AFFF", "#00BFFF", "#00CFFF", "#00DFFF", "#00EFFF", "#00FFFF", "#10FFEF", "#20FFDF", "#30FFCF", "#40FFBF", "#50FFAF", "#60FF9F", "#70FF8F", "#80FF80", "#8FFF70", "#9FFF60", "#AFFF50", "#BFFF40", "#CFFF30", "#DFFF20", "#EFFF10", "#FFFF00", "#FFEF00", "#FFDF00", "#FFCF00", "#FFBF00", "#FFAF00", "#FF9F00", "#FF8F00", "#FF8000", "#FF7000", "#FF6000", "#FF5000", "#FF4000", "#FF3000", "#FF2000", "#FF1000", "#FF0000", "#EF0000", "#DF0000", "#CF0000", "#BF0000", "#AF0000", "#9F0000", "#8F0000", "#800000") if (n == 64) return(orig) rgb.tim = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , 64) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb.tim[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } ans = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: ans } ################################################################################ # Package: colorRamps # Type: Package # Title: Builds pleasing color tables # Version: 1.0 # Date: 2007-04-05 # Author: Tim Keitt # Maintainer: Tim Keitt # Description: Builds single and double gradient color maps # License: GPL # Packaged: Thu Apr 5 16:34:42 2007; tkeitt .blue2redPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: n2 = ceiling(n / 2) red = rep(c(0, 1), each = n2)[1:n] green = 1 - abs(seq(-1, 1, length.out = n)) blue = rev(red) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .green2redPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: n2 = ceiling(n / 2) red = rep(c(0, 1), each = n2)[1:n] blue = 1 - abs(seq(-1, 1, length.out = n)) green = rev(red) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .blue2greenPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: n2 = ceiling(n / 2) green = rep(c(0, 1), each = n2)[1:n] red = 1 - abs(seq(-1, 1, length.out = n)) blue = rev(green) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .purple2greenPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: red = rep(0.5, length.out = n) green = seq(0, 1, length.out = n) blue = rev(green) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .blue2yellowPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: red = seq(0, 1, length.out = n) green = red blue = rev(red) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ .cyan2magentaPalette <- function(n) { # A copy from contributed R-package colorRamps # FUNCTION: # Color Ramp: red = seq(0, 1, length.out = n) green = rev(red) blue = rep(1, n) # Return Value: rgb(red, green, blue) } # ------------------------------------------------------------------------------ rampPalette <- function(n, name = c("blue2red", "green2red", "blue2green", "purple2green", "blue2yellow", "cyan2magenta")) { # Description: # Creates a color ramp palette # FUNCTION: # Color Ramp: name = match.arg(name) funPalette = match.fun(paste(".", name, "Palette", sep = "")) ans = funPalette(n) # Return Value: ans } ################################################################################ # Package: RColorBrewer # Version: 1.0-2 # Date: 2007-10-21 # Title: ColorBrewer palettes # Author: Erich Neuwirth # Maintainer: Erich Neuwirth # Depends: R (>= 2.0.0) # Description: The packages provides palettes for drawing nice maps # shaded according to a variable. # License: Apache License 2.0 seqPalette <- function(n, name = c("Blues", "BuGn", "BuPu", "GnBu", "Greens", "Greys", "Oranges", "OrRd", "PuBu", "PuBuGn", "PuRd", "Purples", "RdPu", "Reds", "YlGn", "YlGnBu", "YlOrBr", "YlOrRd")) { # A function implemented by Diethelm Wuertz # Description: # Creates a sequential color palette # FUNCTION: # Color Sets: # Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, # PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd. Blues = rgb(c(247,222,198,158,107,66,33,8,8), c(251,235,219,202,174,146,113,81,48), c(255,247,239,225,214,198,181,156,107),maxColorValue=255) BuGn = rgb(c(247,229,204,153,102,65,35,0,0), c(252,245,236,216,194,174,139,109,68), c(253,249,230,201,164,118,69,44,27),maxColorValue=255) BuPu = rgb(c(247,224,191,158,140,140,136,129,77), c(252,236,211,188,150,107,65,15,0), c(253,244,230,218,198,177,157,124,75),maxColorValue=255) GnBu = rgb(c(247,224,204,168,123,78,43,8,8), c(252,243,235,221,204,179,140,104,64), c(240,219,197,181,196,211,190,172,129),maxColorValue=255) Greens = rgb(c(247,229,199,161,116,65,35,0,0), c(252,245,233,217,196,171,139,109,68), c(245,224,192,155,118,93,69,44,27),maxColorValue=255) Greys = rgb(c(255,240,217,189,150,115,82,37,0), c(255,240,217,189,150,115,82,37,0), c(255,240,217,189,150,115,82,37,0),maxColorValue=255) Oranges = rgb(c(255,254,253,253,253,241,217,166,127), c(245,230,208,174,141,105,72,54,39), c(235,206,162,107,60,19,1,3,4),maxColorValue=255) OrRd = rgb(c(255,254,253,253,252,239,215,179,127), c(247,232,212,187,141,101,48,0,0), c(236,200,158,132,89,72,31,0,0),maxColorValue=255) PuBu = rgb(c(255,236,208,166,116,54,5,4,2), c(247,231,209,189,169,144,112,90,56), c(251,242,230,219,207,192,176,141,88),maxColorValue=255) PuBuGn = rgb(c(255,236,208,166,103,54,2,1,1), c(247,226,209,189,169,144,129,108,70), c(251,240,230,219,207,192,138,89,54),maxColorValue=255) PuOr = rgb(c(127,179,224,253,254,247,216,178,128,84,45), c(59,88,130,184,224,247,218,171,115,39,0), c(8,6,20,99,182,247,235,210,172,136,75),maxColorValue=255) PuRd = rgb(c(247,231,212,201,223,231,206,152,103), c(244,225,185,148,101,41,18,0,0), c(249,239,218,199,176,138,86,67,31),maxColorValue=255) Purples = rgb(c(252,239,218,188,158,128,106,84,63), c(251,237,218,189,154,125,81,39,0), c(253,245,235,220,200,186,163,143,125),maxColorValue=255) RdPu = rgb(c(255,253,252,250,247,221,174,122,73), c(247,224,197,159,104,52,1,1,0), c(243,221,192,181,161,151,126,119,106),maxColorValue=255) Reds = rgb(c(255,254,252,252,251,239,203,165,103), c(245,224,187,146,106,59,24,15,0), c(240,210,161,114,74,44,29,21,13),maxColorValue=255) YlGn = rgb(c(255,247,217,173,120,65,35,0,0), c(255,252,240,221,198,171,132,104,69), c(229,185,163,142,121,93,67,55,41),maxColorValue=255) YlGnBu = rgb(c(255,237,199,127,65,29,34,37,8), c(255,248,233,205,182,145,94,52,29), c(217,177,180,187,196,192,168,148,88),maxColorValue=255) YlOrBr = rgb(c(255,255,254,254,254,236,204,153,102), c(255,247,227,196,153,112,76,52,37), c(229,188,145,79,41,20,2,4,6),maxColorValue=255) YlOrRd = rgb(c(255,255,254,254,253,252,227,189,128), c(255,237,217,178,141,78,26,0,0), c(204,160,118,76,60,42,28,38,38),maxColorValue=255) # Compose: name = match.arg(name) orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } # ------------------------------------------------------------------------------ divPalette <- function(n, name = c("BrBG", "PiYG", "PRGn", "PuOr", "RdBu", "RdGy", "RdYlBu", "RdYlGn", "Spectral")) { # A function implemented by Diethelm Wuertz # Description: # Creates a diverging color palette # FUNCTION: # Color Sets: # BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral. BrBG = rgb(c(84,140,191,223,246,245,199,128,53,1,0), c(48,81,129,194,232,245,234,205,151,102,60), c(5,10,45,125,195,245,229,193,143,94,48),maxColorValue=255) PiYG = rgb(c(142,197,222,241,253,247,230,184,127,77,39), c(1,27,119,182,224,247,245,225,188,146,100), c(82,125,174,218,239,247,208,134,65,33,25),maxColorValue=255) PRGn = rgb(c(64,118,153,194,231,247,217,166,90,27,0), c(0,42,112,165,212,247,240,219,174,120,68), c(75,131,171,207,232,247,211,160,97,55,27),maxColorValue=255) PuOr = rgb(c(127,179,224,253,254,247,216,178,128,84,45), c(59,88,130,184,224,247,218,171,115,39,0), c(8,6,20,99,182,247,235,210,172,136,75),maxColorValue=255) RdBu = rgb(c(103,178,214,244,253,247,209,146,67,33,5), c(0,24,96,165,219,247,229,197,147,102,48), c(31,43,77,130,199,247,240,222,195,172,97),maxColorValue=255) RdGy = rgb(c(103,178,214,244,253,255,224,186,135,77,26), c(0,24,96,165,219,255,224,186,135,77,26), c(31,43,77,130,199,255,224,186,135,77,26),maxColorValue=255) RdYlBu = rgb(c(165,215,244,253,254,255,224,171,116,69,49), c(0,48,109,174,224,255,243,217,173,117,54), c(38,39,67,97,144,191,248,233,209,180,149),maxColorValue=255) RdYlGn = rgb(c(165,215,244,253,254,255,217,166,102,26,0), c(0,48,109,174,224,255,239,217,189,152,104), c(38,39,67,97,139,191,139,106,99,80,55),maxColorValue=255) Spectral = rgb(c(158,213,244,253,254,255,230,171,102,50,94), c(1,62,109,174,224,255,245,221,194,136,79), c(66,79,67,97,139,191,152,164,165,189,162),maxColorValue=255) # Compose: name = match.arg(name) orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } # ------------------------------------------------------------------------------ qualiPalette <- function(n, name = c("Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1", "Set2", "Set3")) { # A function implemented by Diethelm Wuertz # Description: # Creates a qualitative color palette # FUNCTION: # Color Sets: Accent = rgb(c(127,190,253,255,56,240,191,102), c(201,174,192,255,108,2,91,102), c(127,212,134,153,176,127,23,102),maxColorValue=255) Dark2 = rgb(c(27,217,117,231,102,230,166,102), c(158,95,112,41,166,171,118,102), c(119,2,179,138,30,2,29,102),maxColorValue=255) Paired = rgb(c(166,31,178,51,251,227,253,255,202,106,255,177), c(206,120,223,160,154,26,191,127,178,61,255,89), c(227,180,138,44,153,28,111,0,214,154,153,40),maxColorValue=255) Pastel1 = rgb(c(251,179,204,222,254,255,229,253,242), c(180,205,235,203,217,255,216,218,242), c(174,227,197,228,166,204,189,236,242),maxColorValue=255) Pastel2 = rgb(c(179,253,203,244,230,255,241,204), c(226,205,213,202,245,242,226,204), c(205,172,232,228,201,174,204,204),maxColorValue=255) Set1 = rgb(c(228,55,77,152,255,255,166,247,153), c(26,126,175,78,127,255,86,129,153), c(28,184,74,163,0,51,40,191,153),maxColorValue=255) Set2 = rgb(c(102,252,141,231,166,255,229,179), c(194,141,160,138,216,217,196,179), c(165,98,203,195,84,47,148,179),maxColorValue=255) Set3 = rgb(c(141,255,190,251,128,253,179,252,217,188,204,255), c(211,255,186,128,177,180,222,205,217,128,235,237), c(199,179,218,114,211,98,105,229,217,189,197,111),maxColorValue=255) # Compose: name = match.arg(name) orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } ################################################################################ # Package: PerformanceAnalytics # Type: Package # Title: Econometric tools for performance and risk analysis. # Version: 0.9.5 # Date: 2007-06-29 # Author: Peter Carl, Brian G. Peterson # Maintainer: Brian G. Peterson # Description: Library of econometric functions for performance and risk # analysis. This library aims to aid practitioners and researchers in # utilizing the latest research in analysis of non-normal return streams. # In general, this library is most tested on return (rather than price) # data on a monthly scale, but most functions will work with daily or # irregular return data as well. # Depends: R (>= 2.4.0), fExtremes, fPortfolio, quadprog, tseries, Hmisc # License: GPL # URL: http://braverock.com/R/ # Packaged: Tue Jul 10 04:30:47 2007 focusPalette <- function(n, name = c("redfocus", "greenfocus", "bluefocus")) { # A function implemented by Diethelm Wuertz # Description: # Creates a color palette for graphs # Source: # Contributed R package PerformanceAnalytics # Details: # This is not a function, per se, but a way to set up specific color # pallets for use in the charts we use. These pallets have been # designed to create readable, comparable line and bar graphs with # specific objectives outlined before each category below. # We use this approach rather than generating them on the fly for two # reasons: 1) fewer dependencies on libraries that don't need to be # called dynamically; and 2) to guarantee the color used for the n-th # column of data. # # FOCUS PALETTE # Colorsets designed to provide focus to the data graphed as the first # element. This palette is best used when there is clearly an important # data set for the viewer to focus on, with the remaining data being # secondary, tertiary, etc. Later elements graphed in diminishing # values of gray. These were generated with RColorBrewer, using the 8 # level "grays" palette and replacing the darkest with the focus color. # For best results, replace the highlight color with the first color # of the equal weighted palette from below. This will coordinate # charts with different purposes. # # MONOCHROME PALETTES: # Colorsets for monochrome color displays. # FUNCTION: # Match Arguments: name = match.arg(name) # Focus Palettes: redfocus = c( "#CB181D", "#252525", "#525252", "#737373", "#969696", "#BDBDBD", "#D9D9D9", "#F0F0F0") greenfocus = c( "#41AB5D", "#252525", "#525252", "#737373", "#969696", "#BDBDBD", "#D9D9D9", "#F0F0F0") bluefocus = c( "#0033FF", "#252525", "#525252", "#737373", "#969696", "#BDBDBD", "#D9D9D9", "#F0F0F0") # Compose: orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } # ------------------------------------------------------------------------------ monoPalette <- function(n, name = c("redmono", "greenmono", "bluemono")) { # A function implemented by Diethelm Wuertz # Description: # Creates a mono color palette # Source: # Contributed R package PerformanceAnalytics # FUNCTION: # Match Arguments: name = match.arg(name) # Monochrome Palettes: redmono = c( "#99000D", "#CB181D", "#EF3B2C", "#FB6A4A", "#FC9272", "#FCBBA1", "#FEE0D2", "#FFF5F0") greenmono = c( "#005A32", "#238B45", "#41AB5D", "#74C476", "#A1D99B", "#C7E9C0", "#E5F5E0", "#F7FCF5") bluemono = c( "#084594", "#2171B5", "#4292C6", "#6BAED6", "#9ECAE1", "#C6DBEF", "#DEEBF7", "#F7FBFF") # Compose: orig = eval(parse(text = name)) rgb = t(col2rgb(orig)) temp = matrix(NA, ncol = 3, nrow = n) x = seq(0, 1, , length(orig)) xg = seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } palette = rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: palette } ################################################################################ fBasics/R/utils-recessionLines.R0000644000176200001440000000371214263246022016277 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .recessionLines Adds vertical recession lines to a plot ################################################################################ .recessionLines <- function() { # A function implemented by Diethelm Wuertz # Description: # Adds vertical recession lines to a plot # Arguments: # none # References: # http://www.nber.org/cycles/cyclesmain.html # FUNCTION: # Add Recession Liones: abline(v = as.POSIXct(c( "1920-01-01", "1921-08-01", "1923-05-01", "1924-07-01", "1926-10-01", "1927-11-01", "1929-08-01", "1933-03-01", "1945-01-01", "1945-10-01", "1948-11-01", "1949-10-01", "1953-07-01", "1954-05-01", "1957-08-01", "1958-04-01", "1960-04-01", "1961-02-01", "1969-12-01", "1970-11-01", "1973-11-01", "1975-03-01", "1990-07-01", "1991-03-01", "2001-03-01", "2001-11-01", "2007-12-07")), col = "grey") } ################################################################################ fBasics/R/utils-baseMethods.R0000644000176200001440000002742314263246022015555 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: BASIC EXTENSIONS: # align aligns time series objects by approximation # align.default align default method # atoms Extracts atoms from 'timeSeries' object # atoms.default atoms default method # attach attach a database to the R path # attach.default attach default method # colnames<- colnames<- has become a generic function # colnames<-.default colnames<- default method # cor cor has become a generic function # cor.default cor default method # cov var has become a generic function # cov.default var default method # log log has become a generic function # log.default log default method # outlier outlier added generic function # outlier.default outlier default method # rownames<- rownames<- has become a generic function # rownames<-.default rownames<- default method # rank rank has become a generic function # rank.default rank default method # sample sample has become a generic function # sample.default sample default method # sort sort has become a generic function # sort.default sort default method # stdev stdev added generic function # stdev.default stdev default method # termPlot termPlot has become a generic function # termPlot.default termPlot default method # var var has become a generic function # var.default var default method # volatility volatility has become a generic function # volatility.default volatility default method ################################################################################ ## It is bad practice to create S3 generic from function especially ## when one wants to use S4 methods -> this can lead to tricky dispatch ## problems. ## .conflicts.OK = TRUE # ------------------------------------------------------------------------------ ## align <- ## function(x, y, xout, ...) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("align") ## } ## # ------------------------------------------------------------------------------ ## align.default <- ## function(x, y, xout, method = "linear", n = 50, rule = 1, f = 0, ## ties = mean, ...) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Align by Approximation: ## ans = approx(x = x, y = y, xout = xout, method = method, n = n, ## rule = rule, f = f, ties = ties, ...) ## # Return Value: ## ans ## } # ------------------------------------------------------------------------------ ## atoms <- ## function(x, ...) ## { ## # A function implemented by Diethelm WUertz ## # FUNCTION: ## # Return Value: ## UseMethod("atoms") ## } ## # ------------------------------------------------------------------------------ ## atoms.default <- ## function(x, ...) ## { ## # A function implemented by Diethelm WUertz ## # FUNCTION: ## # Return Value: ## invisible(x) ## } # ------------------------------------------------------------------------------ ## attach <- ## function(what, pos = 2, name = deparse(substitute(what)), ## warn.conflicts = TRUE) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("attach") ## } ## # ------------------------------------------------------------------------------ ## attach.default <- base::attach # ------------------------------------------------------------------------------ ## "colnames<-" = ## function(x, value) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("colnames<-") ## } ## # ------------------------------------------------------------------------------ ## `colnames<-.default` <- base::`colnames<-` # ------------------------------------------------------------------------------ ## cor <- ## function(x, y = NULL, use = "all.obs", ## method = c("pearson", "kendall", "spearman")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("cor") ## } # ------------------------------------------------------------------------------ ## cor.default <- stats::cor ## # ------------------------------------------------------------------------------ ## cov <- ## function(x, y = NULL, use = "all.obs", ## method = c("pearson", "kendall", "spearman")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("cov") ## } ## # ------------------------------------------------------------------------------ ## cov.default <- stats::cov ## # ------------------------------------------------------------------------------ ## log <- ## function(x, base = exp(1)) ## { # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("log") ## } ## # ------------------------------------------------------------------------------ ## log.default <- ## function(x, base = exp(1)) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## base::log(x, base) ## } # ------------------------------------------------------------------------------ ## rank <- ## function(x, na.last = TRUE, ## ties.method = c("average", "first", "random", "max", "min")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("rank") ## } # ------------------------------------------------------------------------------ ## rank.default <- ## function(x, na.last = TRUE, ## ties.method = c("average", "first", "random", "max", "min")) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## base::rank(x, na.last = na.last, ties.method = ties.method) ## } # ------------------------------------------------------------------------------ ## sample <- ## function(x, ...) ## { # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("sample") ## } ## # ------------------------------------------------------------------------------ ## sample.default <- ## function(x, size, replace = FALSE, prob = NULL, ...) ## { ## # FUNCTION: ## base::sample(x, size, replace = replace, prob = prob) ## } # ------------------------------------------------------------------------------ ## if(getRversion() < "2.4.0") { ## # Note: ## # sort() has been S3 generic in 'base' since 2.4.0 ## # Otherwise use something that works here ## sort <- function(x, decreasing = FALSE, ...) ## { ## if (!is.logical(decreasing) || length(decreasing) != 1) ## stop("'decreasing' must be a length-1 logical vector.\nDid you intend to set 'partial'?") ## UseMethod("sort") ## } ## sort.default <- function(x, decreasing = FALSE, ...) { ## if (is.object(x)) ## x[order(x, decreasing = decreasing)] ## else base::sort(x, decreasing = decreasing, ...) ## } ## }# endif {only for outdated R} # ------------------------------------------------------------------------------ ## outlier <- ## function(x, sd = 5, complement = TRUE, ...) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## UseMethod("outlier") ## } # ------------------------------------------------------------------------------ ## outlier.default <- ## function(x, sd = 5, complement = TRUE, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Returns outliers ## # Arguments: ## # x - a numeric vector ## # sd - a numeric value of standard deviations, e.g. 5 ## # means that values larger or smaller tahn five ## # times the standard deviation of the series will ## # be detected. ## # complement - a logical flag, should the outlier series ## # or its complements be returned. ## # Note: ## # This function is thought to find splits in financial ## # price or index data. If a price or index is splitted we ## # observe in the returns a big jump of several standard ## # deviations. ## # FUNCTION: ## # Find Outliers: ## SD = sd * sd(x) ## if (complement) { ## ans = x[x <= SD] ## } else { ## ans = x[x > SD] ## names(ans) = as.character(which(x > SD)) ## } ## # Return Value: ## ans ## } # ------------------------------------------------------------------------------ ## "rownames<-" = ## function(x, value) ## { # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("rownames<-") ## } ## # ------------------------------------------------------------------------------ ## `rownames<-.default` <- base::`rownames<-` # ------------------------------------------------------------------------------ stdev.default <- function(x, na.rm = FALSE) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: stats::sd(x = x, na.rm = na.rm) } # ------------------------------------------------------------------------------ stdev <- function(x, na.rm = FALSE) UseMethod("stdev") # ------------------------------------------------------------------------------ termPlot <- function(model, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("termPlot") } # ------------------------------------------------------------------------------ termPlot.default <- function(model, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: stats::termplot(model, ...) } # ------------------------------------------------------------------------------ ## var <- ## function(x, y = NULL, na.rm = FALSE, use) ## { ## # A function implemented by Diethelm Wuertz ## # FUNCTION: ## # Return Value: ## UseMethod("var") ## } ## # ------------------------------------------------------------------------------ ## var.default <- stats::var # ------------------------------------------------------------------------------ volatility <- function(object, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("volatility") } # ------------------------------------------------------------------------------ volatility.default <- function(object, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Compute volatility: x = object ans = (x - mean(x))^2 # Return Value: ans } ################################################################################ fBasics/R/plot-subStars.R0000644000176200001440000001054414263246022014737 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: SLIDER MENU: # .subStars Creates a sub stars plot ################################################################################ .subStars <- function(x, keyOffset = c(0, 0), loc = NULL, palette = NULL) { # Description: # Creates a sub stars plot # Details: # this is the stars::graphics() function modified in such a # way, that it works together with the subplot builtin. # FUNCTION: # Palette: if (is.null(palette)) palette = palette(rainbow(12, s = 0.6, v = 0.75)) # Graph Frame: xCol = ncol(x) yCol = nrow(x) NY = NX = ceiling(sqrt(xCol)) if (NX * NY == xCol) NY = NY + 1 if (is.null(loc)) { for (nx in 1:NY) for (ny in 1:NX) loc = rbind(loc, c(nx, ny)) loc = loc[1:xCol, ] loc[, 2] = NY + 1 - loc[, 2] } # Stars - Settings: x = t(x) locations = loc len = 0.4 key.loc = c(NX + 1, 1) + keyOffset xlim = c(1, NX + 0.5) ylim = c(0, NY + 1) op <- par("xpd") on.exit(par(op)) par(xpd = TRUE) labels = dimnames(x)[[1]] key.labels = dimnames(x)[[2]] n.loc <- nrow(x) n.seg <- ncol(x) xloc <- locations[, 1] yloc <- locations[, 2] angles <- seq.int(0, 2 * pi, length.out = n.seg + 1)[-(n.seg + 1)] x <- apply(x, 2, function(x) (x - min(x, na.rm = TRUE))/diff(range(x, na.rm = TRUE))) mx <- max(x <- x * len) if (is.null(xlim)) xlim <- range(xloc) + c(-mx, mx) if (is.null(ylim)) ylim <- range(yloc) + c(-mx, mx) deg <- pi/180 plot(0, type = "n", xlim = xlim, ylim = ylim, main = "", sub = "", xlab = "", ylab = "", asp = 1, axes = FALSE) s.x <- xloc + x * rep.int(cos(angles), rep.int(n.loc, n.seg)) s.y <- yloc + x * rep.int(sin(angles), rep.int(n.loc, n.seg)) aangl <- c(angles, if (TRUE) 2 * pi else pi) for (i in 1:n.loc) { px <- py <- numeric() for (j in 1:n.seg) { k <- seq.int(from = aangl[j], to = aangl[j + 1], by = 1 * deg) px <- c(px, xloc[i], s.x[i, j], x[i, j] * cos(k) + xloc[i], NA) py <- c(py, yloc[i], s.y[i, j], x[i, j] * sin(k) + yloc[i], NA) } polygon(px, py, col = 1:n.seg, lwd = 0.25, lty = 1) } text(xloc, yloc - mx + 0.04, labels, cex = 0.7, font = 2, adj = c(0.5, 1)) key.x <- len * cos(angles) + key.loc[1] key.y <- len * sin(angles) + key.loc[2] px <- py <- numeric() for (j in 1:n.seg) { k <- seq.int(from = aangl[j], to = aangl[j + 1], by = 1 * deg) px <- c(px, key.loc[1], key.x[j], len * cos(k) + key.loc[1], NA) py <- c(py, key.loc[2], key.y[j], len * sin(k) + key.loc[2], NA) } polygon(px, py, col = 1:n.seg, lwd = 0.25, lty = 1) lab.angl <- angles + (angles[2] - angles[1])/2 label.x <- 1.15 * len * cos(lab.angl) + key.loc[1] label.y <- 1.15 * len * sin(lab.angl) + key.loc[2] for (k in 1:n.seg) { text.adj <- c(if (lab.angl[k] < 90 * deg || lab.angl[k] > 270 * deg) 0 else if (lab.angl[k] > 90 * deg && lab.angl[k] < 270 * deg) 1 else 0.5, if (lab.angl[k] <= 90 * deg) (1 - lab.angl[k]/(90 * deg))/2 else if (lab.angl[k] <= 270 * deg) (lab.angl[k] - 90 * deg)/(180 * deg) else 1 - (lab.angl[k] - 270 * deg)/(180 * deg)) text(label.x[k], label.y[k], labels = key.labels[k], cex = 0.7, adj = text.adj) } # Return Value: invisible(locations) } ################################################################################ fBasics/R/dist-ghRobMoments.R0000644000176200001440000001041114263246022015513 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghMED Returns true gh median # ghIQR Returns true gh inter quartal range # ghSKEW Returns true gh robust skewness # ghKURT Returns true gh robust kurtosis ################################################################################ ghMED <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh median # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Median Q = qgh(p=0.5, alpha, beta, delta, mu, lambda) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ ghIQR <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh inter quartal range # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Inter Quartile Range Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda) Q[2] = qgh(p=0.75, alpha, beta, delta, mu, lambda) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ ghSKEW <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh skewness # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Skewness: Q = numeric() Q[1] = qgh(p=0.25, alpha, beta, delta, mu, lambda) Q[2] = qgh(p=0.50, alpha, beta, delta, mu, lambda) Q[3] = qgh(p=0.75, alpha, beta, delta, mu, lambda) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ ghKURT <- function(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) { # A function implemented by Diethelm Wuertz # Description: # Returns true gh robust gh kurtosis # Arguments: # beta - a numeric value, the location parameter # delta - a numeric value, the scale parameter # mu - a numeric value, the first shape parameter # nu - a numeric value, the second parameter # FUNCTION: # gh Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qgh(p, alpha, beta, delta, mu, lambda)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-stableFit.R0000644000176200001440000006110114324527232015031 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: STABLE DISTRIBUTION: # stableFit Fits parameters of a stable density # .phiStable Creates contour table for McCulloch estimators # .PhiStable Contour table created by function .phiStable() # .qStableFit Estimates parameters by McCulloch's approach # .mleStableFit Estimates stable parameters by MLE approach # .stablePlot Plots results of stable parameter estimates # LIBRARY: DESCRPTION: # stabledist Stable Duistribution ################################################################################ stableFit <- function(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, type = c("q", "mle"), doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description # Stable Parameter Estimation # FUNCTION: # Start Values: Use Quantile Method: ans = .qStableFit(x, doplot, title, description) # Continue with MLE Approach: if (type[1] == "mle") { Alpha = ans@fit$estimate[1] Beta = ans@fit$estimate[2] Gamma = ans@fit$estimate[3] Delta = ans@fit$estimate[4] if (is.na(Alpha)) Alpha = alpha if (is.na(Beta)) Beta = beta if (is.na(Gamma)) Gamma = gamma if (is.na(Delta)) Delta = delta ans = .mleStableFit(x, Alpha, Beta, Gamma, Delta, doplot, control, trace, title, description) } # Return Value: ans } # ------------------------------------------------------------------------------ .phiStable <- function() { # A function implemented by Diethelm Wuertz # Description: # Creates contour table for McCulloch estimators # Note: # Stable Distribution - delta=1 and gamma=0 fixed! # FUNCTION: # Settings: alpha = c(seq(0.50, 1.95, by = 0.1), 1.95, 1.99) beta = c(-0.95, seq(-0.90, 0.90, by = 0.10), 0.95) m = length(alpha) n = length(beta) # phi1: phi1 = function(alpha, beta) { ( stabledist::qstable(0.95, alpha, beta) - stabledist::qstable(0.05, alpha, beta) ) / ( stabledist::qstable(0.75, alpha, beta) - stabledist::qstable(0.25, alpha, beta) ) } # phi2: phi2 = function(alpha, beta) { ( ( stabledist::qstable(0.95, alpha, beta) - stabledist::qstable(0.50, alpha, beta) ) - ( stabledist::qstable(0.50, alpha, beta) - stabledist::qstable(0.05, alpha, beta) ) ) / ( stabledist::qstable(0.95, alpha, beta) - stabledist::qstable(0.05, alpha, beta) ) } # Phi: Phi1 = Phi2 = matrix(rep(0, n*m), ncol = n) for ( i in 1:m ) { for ( j in 1:n ) { Phi1[i,j] = phi1(alpha[i], beta[j]) Phi2[i,j] = phi2(alpha[i], beta[j]) print( c(alpha[i], beta[j], Phi1[i,j], Phi2[i,j]) ) } } #Plot: contour(alpha, beta, Phi1, levels = c(2.5, 3, 5, 10, 20, 40), xlab = "alpha", ylab = "beta", labcex = 1.5, xlim = c(0.5, 2.0)) contour(alpha, beta, Phi2, levels = c(-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8), col = "red", labcex = 1.5, add = TRUE) # Result: .PhiStable <- list(Phi1 = Phi1, Phi2 = Phi2, alpha = alpha, beta = beta) # Dump: if (FALSE) dump(".PhiStable", "PhiStable.R") # Return Value: .PhiStable } # ------------------------------------------------------------------------------ .PhiStable <- structure( # Contour table created by .phiStable() list( Phi1 = structure(c(28.1355600962322, 15.7196640771722, 10.4340722145276, 7.72099712337154, 6.14340919629241, 5.14081963876442, 4.45927409495436, 3.97057677836415, 3.60450193720703, 3.31957820409758, 3.09091185492284, 2.9029090263133, 2.74659551412658, 2.61782756430233, 2.51560631141019, 2.47408801877228, 2.44525363752631, 28.3483300097440, 15.8035063527296, 10.4727953966709, 7.74141823634036, 6.15576314177463, 5.14915506532249, 4.46531988542058, 3.97505189771183, 3.60736868847651, 3.32104702700423, 3.09111342044363, 2.90219825204304, 2.74552084086904, 2.61707392900167, 2.51531961206501, 2.47401179006119, 2.44525268403340, 28.9048147202382, 16.0601313263480, 10.6117049719320, 7.8237753182648, 6.20955682299195, 5.18679961805621, 4.49216000186678, 3.99373973242068, 3.61933010719953, 3.3275594162867, 3.09331226057454, 2.90155994459038, 2.74368539137288, 2.61555847928520, 2.51479444686423, 2.47387109904253, 2.44525093764076, 29.8026200973137, 16.5596243974224, 10.9360864131028, 8.04563520952583, 6.35889612112486, 5.28397230078563, 4.55431822343012, 4.03266384946799, 3.64246979110989, 3.33973615437707, 3.09810405845187, 2.90191073816964, 2.7422687239301, 2.61421528380001, 2.51433181630644, 2.47374639426837, 2.44524854839106, 31.1711919144382, 17.3256645589998, 11.4257682914949, 8.37916374365985, 6.59111369941092, 5.4435297531592, 4.65827423282770, 4.09593909348039, 3.67827370972420, 3.35788028768518, 3.10538249512478, 2.90322430476313, 2.7411383952518, 2.61307358265729, 2.51393064512464, 2.47363793686619, 2.44524473760244, 33.1948934984822, 18.4071500759968, 12.0794579487667, 8.80181848446003, 6.8744516193679, 5.6351929385469, 4.78568154439060, 4.17585254205654, 3.72404890551037, 3.38077811930023, 3.11457663716798, 2.90522469731234, 2.74043557937936, 2.61209710262770, 2.51359601849030, 2.47354590407541, 2.44523688699559, 35.9578028398622, 19.7936882741385, 12.8664734285429, 9.28349135864713, 7.18278427917665, 5.83729171895546, 4.91779143967590, 4.25960094973301, 3.77307337255403, 3.4057709031586, 3.12469898537975, 2.90767817230573, 2.7399388028832, 2.61130578115398, 2.51330169831474, 2.47347041290067, 2.4452243360266, 38.9647018181828, 21.2269841758554, 13.6587928142908, 9.75737930074977, 7.47910500668215, 6.02679068365276, 5.03975173429821, 4.33641171958119, 3.81844774991854, 3.42948489228359, 3.13453596844362, 2.91006724722144, 2.73965159366498, 2.61068126597691, 2.51309087992836, 2.47342375837911, 2.44524639012562, 41.7818369588849, 22.4602363370435, 14.3128567516975, 10.1435099877839, 7.72005687380391, 6.18048140792379, 5.13779081982581, 4.39769218775219, 3.85488105109873, 3.44880791704519, 3.14262443869137, 2.91215029990006, 2.73947335432536, 2.61026149741526, 2.51293721037603, 2.47333463795113, 2.44523871005695, 43.8597008728084, 23.308181530155, 14.7432127411421, 10.3930748979735, 7.87518470289541, 6.2796283727218, 5.20095216574708, 4.43724315622136, 3.87838777226609, 3.46128790559551, 3.14792806986726, 2.91355836113947, 2.7394048824397, 2.60999397178590, 2.51287974154683, 2.4733261698261, 2.44523291863013, 44.6351177921226, 23.612190997173, 14.8937659530932, 10.4791077126402, 7.9285095195091, 6.31375867971341, 5.2228691158165, 4.45085115209764, 3.88645952709398, 3.46562279514434, 3.14978921717977, 2.91402779433739, 2.73939000980242, 2.60993524495366, 2.51289337121820, 2.47330017392183, 2.44524525291192, 43.8597008728085, 23.3081815301550, 14.7432127411420, 10.3930748979735, 7.87518470289542, 6.2796283727218, 5.20095216574709, 4.43724315622136, 3.87838777226609, 3.46128790559551, 3.14792806986726, 2.91355836113947, 2.7394048824397, 2.60999397178590, 2.51287974154683, 2.4733261698261, 2.44523291863013, 41.7818369588849, 22.4602363370436, 14.3128567516975, 10.1435099877840, 7.72005687380392, 6.18048140792379, 5.13779081982581, 4.39769218775219, 3.85488105109874, 3.44880791704519, 3.14262443869137, 2.91215029990006, 2.73947335432536, 2.61026149741526, 2.51293721037603, 2.47333463795113, 2.44523871005695, 38.9647018181828, 21.2269841758554, 13.6587928142909, 9.75737930074976, 7.47910500668216, 6.02679068365277, 5.0397517342982, 4.33641171958119, 3.81844774991854, 3.42948489228359, 3.13453596844362, 2.91006724722144, 2.73965159366498, 2.61068126597691, 2.51309087992836, 2.47342375837911, 2.44524639012562, 35.9578028398623, 19.7936882741384, 12.8664734285429, 9.28349135864712, 7.18278427917667, 5.83729171895546, 4.91779143967590, 4.25960094973301, 3.77307337255403, 3.4057709031586, 3.12469898537975, 2.90767817230572, 2.7399388028832, 2.61130578115398, 2.51330169831474, 2.47347041290067, 2.4452243360266, 33.1948934984822, 18.4071500759967, 12.0794579487667, 8.80181848446, 6.8744516193679, 5.6351929385469, 4.78568154439059, 4.17585254205653, 3.72404890551037, 3.38077811930023, 3.11457663716797, 2.90522469731234, 2.74043557937936, 2.61209710262770, 2.51359601849030, 2.47354590407541, 2.44523688699559, 31.1711919144381, 17.3256645589999, 11.4257682914948, 8.37916374365983, 6.59111369941092, 5.44352975315921, 4.65827423282769, 4.09593909348039, 3.67827370972420, 3.35788028768518, 3.10538249512478, 2.90322430476312, 2.7411383952518, 2.61307358265729, 2.51393064512464, 2.47363793686619, 2.44524473760245, 29.8026200973136, 16.5596243974222, 10.9360864131028, 8.04563520952581, 6.35889612112487, 5.28397230078562, 4.55431822343012, 4.03266384946799, 3.64246979110989, 3.33973615437707, 3.09810405845187, 2.90191073816964, 2.7422687239301, 2.61421528380001, 2.51433181630644, 2.47374639426837, 2.44524854839106, 28.904814720238, 16.0601313263481, 10.6117049719320, 7.82377531826481, 6.20955682299195, 5.18679961805620, 4.49216000186678, 3.99373973242068, 3.61933010719952, 3.3275594162867, 3.09331226057455, 2.90155994459037, 2.74368539137288, 2.61555847928520, 2.51479444686423, 2.47387109904253, 2.44525093764076, 28.3483300097438, 15.8035063527296, 10.4727953966709, 7.74141823634038, 6.15576314177465, 5.1491550653225, 4.46531988542057, 3.97505189771183, 3.60736868847651, 3.32104702700424, 3.09111342044363, 2.90219825204304, 2.74552084086904, 2.61707392900167, 2.51531961206501, 2.47401179006119, 2.44525268403340, 28.1355600962322, 15.7196640771723, 10.4340722145275, 7.72099712337151, 6.1434091962924, 5.14081963876442, 4.45927409495435, 3.97057677836415, 3.60450193720703, 3.31957820409757, 3.09091185492284, 2.9029090263133, 2.74659551412658, 2.61782756430233, 2.51560631141019, 2.47408801877228, 2.44525363752631), .Dim = as.integer(c(17, 21))), Phi2 = structure(c(-0.984831521754346, -0.96178750287388, -0.925815277018118, -0.877909786944587, -0.820184465666658, 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0.0499759998221642, 0.0242509183116936, 0.00473892214395428, 0.979153629182453, 0.95106168789293, 0.909673941239617, 0.85602448791855, 0.791828629762055, 0.720369142925193, 0.644748722301942, 0.567214164881931, 0.489279988980024, 0.411880943403833, 0.335605401995838, 0.260987626602552, 0.188786833912991, 0.120269390643364, 0.0570145517746277, 0.0277025095388299, 0.00541534338063891, 0.984833720280198, 0.96124832221758, 0.92402252050786, 0.87419335313835, 0.81415246030398, 0.746626535051091, 0.674063099101783, 0.598388680797427, 0.520979416823626, 0.442739494396008, 0.364272193075271, 0.286088382456793, 0.208957354222478, 0.1342789733294, 0.0640144100029874, 0.0311490271069919, 0.00609172639474771, 0.984831521754346, 0.96178750287388, 0.925815277018118, 0.877909786944586, 0.820184465666658, 0.75503191854508, 0.684592921700776, 0.610570901457792, 0.534175805219629, 0.456236240234657, 0.377306837254648, 0.297867095171864, 0.218666685919607, 0.141143940005886, 0.0674987199592513, 0.0328701597273591, 0.00642990194045459), .Dim = as.integer(c(17, 21))), alpha = c(0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.95, 1.99), beta = c(-0.95, -0.9, -0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95)), .Names = c("Phi1", "Phi2", "alpha", "beta") ) # ------------------------------------------------------------------------------ .qStableFit <- function(x, doplot = TRUE, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Estimates stable parameters by McCulloch's approach # Note: # This implementation assumes delta=1 and gamma=0 # FUNCTION: # Settings: CALL = match.call() # Load Contour Table: # data(PhiStable) Phi1 = .PhiStable$Phi1 Phi2 = .PhiStable$Phi2 alpha = .PhiStable$alpha beta = .PhiStable$beta # Estimate phi: r = sort(x) N = length(r) q95 = r[round(0.95*N)] q75 = r[round(0.75*N)] q50 = r[round(0.50*N)] q25 = r[round(0.25*N)] q05 = r[round(0.05*N)] phi1 = max( (q95-q05) / (q75-q25), 2.4389 ) phi2 = ((q95-q50)-(q50-q05)) / (q95-q05) # print(c(min(Phi1), phi1, max(Phi1))) # print(c(min(Phi2), phi2, max(Phi2))) # Plot: if (doplot) { contour(alpha, beta, Phi1, levels = c(2.5, 3, 5, 10, 20, 40), xlab = "alpha", ylab = "beta", xlim = c(0.5, 2.0)) contour(alpha, beta, Phi2, levels = c(-0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8), col = "red", add = TRUE) lines(c(0.5, 2), c(0, 0), col = "red") contour(alpha, beta, Phi1, levels = phi1, add = TRUE, lty = 3, col = "blue") contour(alpha, beta, Phi2, levels = phi2, add = TRUE, lty = 3, col = "blue") title(main = "Stable Quantiles") } # Extract Estimate from Contours, if possible: u = contourLines(alpha, beta, Phi1, levels = phi1) Len = length(u) if( Len > 0) { u = u[[1]][-1] v = contourLines(alpha, beta, Phi2, levels = phi2) # print("v") # print(v) v = v[[1]][-1] xout = seq(min(v$y), max(v$y), length = 200) z = approx(v$y, v$x, xout = xout)$y - approx(u$y, u$x, xout = xout)$y index = which.min(abs(z)) V = round(xout[index], 3) U = round(approx(v$y, v$x, xout = xout[index])$y, 3) if (doplot) points(U, V, pch = 19, cex = 1) } else { U = V = NA } # Add Title and Description: if (is.null(title)) title = "Stable Parameter Estimation" if (is.null(description)) description = "" if (is.na(U) | is.na(V)) { GAM = NA } else { phi3 = stabledist::qstable(0.75, U, V) - stabledist::qstable(0.25, U, V) GAM = (q75-q25) / phi3 } if (is.na(U) | is.na(V)) { DELTA = NA } else { phi4 = -stabledist::qstable(0.50, U, V) + V*tan(pi*U/2) DELTA = phi4*GAM - V*GAM*tan(pi*U/2) + q50 } # Fit: fit = list(estimate = c(alpha = U, beta = V, gamma = GAM, delta = DELTA)) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Stable Distribution", data = as.data.frame(x), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .mleStableFit <- function(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Estimates stable parameters by MLE approach # Note: # This implementation assumes delta=1 and gamma=0 # FUNCTION: # Transform: x.orig = x x = as.vector(x) # Settings: CALL = match.call() # Log-likelihood Function: obj = function(x, y = x, trace = FALSE) { f = -mean(log(stabledist::dstable(y, alpha = x[1], beta = x[2], gamma = x[3], delta = x[4]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Stable Estimate: ", x) cat("\n") } f } # Minimization: eps = 1e-4 r <- nlminb( objective = obj, start = c(alpha, beta, gamma, delta), lower = c( eps, -1+eps, 0+eps, -Inf), upper = c(2-eps, 1-eps, Inf, Inf), y = x, control = control, trace = trace) alpha = r$par[1] beta = r$par[2] gamma = r$par[3] delta = r$par[4] # Optional Plot: if (doplot) .stablePlot(x, alpha, beta, gamma, delta) # Add Title and Description: if (is.null(title)) title = "Stable Parameter Estimation" if (is.null(description)) description = "" # Fit: fit = list(estimate = c(alpha, beta, gamma, delta), minimum = -r$objective, code = r$convergence, gradient = r$gradient) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Stable Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .stablePlot <- function(x, alpha, beta, gamma, delta) { # A function implemented by Diethelm Wuertz # Description: # Estimates stable parameters by MLE approach # FUNCTION: # Plot: span.min <- stabledist::qstable(0.01, alpha, beta, gamma, delta) span.max <- stabledist::qstable(0.99, alpha, beta, gamma, delta) span = seq(span.min, span.max, length = 100) par(err = -1) z = density(x, n = 100) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = stabledist::dstable(span, alpha, beta, gamma, delta) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)") title("Stable Distribution: Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") grid() # Return Value: invisible() } ################################################################################ fBasics/R/plot-acfPlot.R0000644000176200001440000002302514263246022014517 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # acfPlot Displays tailored autocorrelations function plot # pacfPlot Displays tailored partial autocorrelation function plot # teffectPlot Estimates and displays the Taylor effect # lacfPlot Displays lagged autocorrelations ################################################################################ acfPlot <- function(x, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Autocorrelations function plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "" xlab = "lag" ylab = "ACF" } else { main = xlab = ylab = "" } # ACF: for (i in 1:dim(x)[2]) { if (labels) main = Units[i] ans = acf(x = as.vector(x[, i]), main = main, xlab = xlab, ylab = ylab, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ pacfPlot <- function(x, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Partial autocorrelation function plot # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "" xlab = "lag" ylab = "PACF" } else { main = xlab = ylab = "" } # Partial ACF: for (i in 1:dim(x)[2]) { if (labels) main = Units[i] ans = pacf(x = as.vector(x[, i]), main = main, xlab = xlab, ylab = ylab, ...) } # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ teffectPlot <- function(x, deltas = seq(from = 0.2, to = 3.0, by = 0.2), lag.max = 10, ymax = NA, standardize = TRUE, labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Evaluate and Display Taylor Effect # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "" xlab = "Exponent Delta" ylab = "Autocorrelation" } else { main = xlab = ylab = "" } # Taylor Effect: ans = list() maxDelta = NULL for (i in 1:dim(x)[2]) { if (labels) main = Units[i] X = as.vector(x[, i]) # Standardize: if(standardize) X = (X-mean(X))/sqrt(var(X)) data = matrix(data = rep(0, times = lag.max*length(deltas)), nrow = lag.max, byrow = TRUE) for (id in 1:length(deltas)) data[,id] = as.double(acf(abs(X)^deltas[id], lag.max = lag.max, type = "corr", plot = FALSE)$acf)[2:(lag.max+1)] if (is.na(ymax)) ymax = max(data) # Plot: if (labels) { plot(deltas, data[1,], ylim = c(0, ymax), type = "n", main = main, xlab = xlab, ylab = ylab, ...) } else { plot(deltas, data[1,], type = "n", main = main, xlab = xlab, ylab = ylab, ...) } xl = 1:length(deltas) for (il in 1:(lag.max)) { yp = max(data[il, ]) yl = xl[data[il, ] == yp] lines(deltas, data[il, ], col = il) points(deltas[yl], yp, pch = 19) maxDelta = c(maxDelta, deltas[yl]) lines (c(1, 1), c(0, ymax)) } # Grid: if (labels) { mtext("Taylor Effect", side = 4, adj = 0, col = "darkgrey", cex = 0.7) grid() } ans[[i]] = data } names(ans) = Units # Deltas for max Peak: ans$maxDelta = maxDelta # Return Value: invisible(ans) } # ------------------------------------------------------------------------------ lacfPlot <- function(x, n = 12, lag.max = 20, type = c("returns", "values"), labels = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes the lagged autocorrelation function # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # type - a character string which specifies the type of the input # series, either "returns" or series "values". In the case of # a return series as input, the required value series is # computed by cumulating the financial returns: 'exp(colCumsums(x))' # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Cumulated Returns: type = match.arg(type) if (type == "values") { cumX = x } else if (type == "returns") { cumX = exp(colCumsums(x)) } # Labels: if (labels) { main = "" xlab = "tau" ylab = "Lagged Correlation" } else { main = xlab = ylab = "" } cumRho = cumLagged = NULL DIM = dim(cumX)[2] for (i in 1:DIM) { # Get Data: x = as.vector(as.matrix(cumX)[, i]) if (labels) main = Units[i] # Truncate to multiple of n: N = trunc(length(x)/n) M = length(x) - n*N if (M > 0) x = x[-c(1:M)] # One Step Volatilities: x.ret = c(0, diff(log(x))) x.mat = matrix(x.ret, byrow = TRUE, ncol = n) u = apply(abs(x.mat), 1, mean) # n-step Volatilities: index = n*(1:N) v = abs(c(0, diff(log(x[index])))) # Zero Tau: L = length(u) RhoZero = cor(u, v) # print(RhoZero) # Positive Tau: RhoPos = NULL for (tau in 1:lag.max) { X = u[-((L-tau+1):L)] X2 = X Y = v[-((L-tau+1):L)] Y2 = v[-(1:tau)] X.mean = mean(X) Y.mean = mean(Y) X1 = sum((X - X.mean)^2) Y1 = sum((Y - Y.mean)^2) XY1 = sum( (X2-X.mean)*(Y2-Y.mean) ) rho = XY1/sqrt(X1*Y1) RhoPos = c(RhoPos, rho) } # Negative Tau: RhoNeg = NULL for (tau in 1:lag.max) { X = v[-((L-tau+1):L)] X2 = X Y = u[-((L-tau+1):L)] Y2 = u[-(1:tau)] X.mean = mean(X) Y.mean = mean(Y) X1 = sum((X - X.mean)^2) Y1 = sum((Y - Y.mean)^2) XY1 = sum( (X2-X.mean)*(Y2-Y.mean) ) rho = XY1/sqrt(X1*Y1) RhoNeg = c(RhoNeg, rho) } # Correlations: Lagged = RhoPos - RhoNeg Rho = c(rev(RhoNeg), RhoZero, RhoPos) # Plot: plot(x = (-lag.max):(lag.max), y = Rho, type = "l", xlab = xlab, ylab = ylab, ylim = c(min(Lagged), max(Rho)), main = main, ...) Text = paste("n =", n, " | lag =", lag.max) mtext(Text, side = 4, adj = 0, col ="darkgrey", cex = 0.7) points(-lag.max:lag.max, Rho, pch = 19, cex = 0.7) lines(0:lag.max, c(0, Lagged), col = "red") points(0:lag.max, c(0, Lagged), pch = 19, cex = 0.7, col = "red") abline(h = 0, col = "grey", lty = 3) ci = 1/sqrt(length(u)) abline(h = +ci, col = "blue") abline(h = -ci, col = "blue") if (labels) grid() # Grid: if (labels) grid() cumRho = rbind(cumRho, Rho) cumLagged = c(cumLagged, Lagged) } # Return Value: invisible(list(Rho = cumRho, Lagged = cumLagged)) } ################################################################################ fBasics/R/utils-hessian.R0000644000176200001440000000516614263246022014751 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # tsHessian Computes Two Sided Hessian matrix ################################################################################ tsHessian <- function(x, fun, ...) { # Description: # Computes two sided (TS) approximated Hessian # Source: # A function borrowed from Kevin Sheppard's Matlab garch toolbox # as implemented by Alexios Ghalanos in his rgarch package # Notes: # optionally requires package Matrix (added as suggestion) # FUNCTION: # Settings: n = length(x) fx <- fun(x, ...) eps = .Machine$double.eps # Compute the stepsize: h = eps^(1/3) * apply( as.data.frame(x), 1, FUN = function(z) max(abs(z), 1.0e-2)) xh = x + h h = xh - x ee = diag(h) # Matrix(diag(h), sparse = TRUE) # Compute forward and backward steps: gp = vector(mode = "numeric", length = n) for(i in 1:n) gp[i] <- fun(x + ee[, i], ...) gm = vector(mode = "numeric", length = n) for(i in 1:n) gm[i] <- fun(x - ee[, i], ...) H = h %*% t(h) Hm = H Hp = H # Compute double forward and backward steps: for(i in 1:n){ for(j in i:n){ Hp[i, j] <- fun(x + ee[, i] + ee[, j], ...) Hp[j, i] <- Hp[i, j] Hm[i, j] <- fun(x - ee[, i] - ee[, j], ...) Hm[j, i] <- Hm[i, j] } } # Compute the Hessian: for(i in 1:n){ for(j in i:n){ H[i, j] = ((Hp[i, j] - gp[i] - gp[j] + fx + fx - gm[i] - gm[j] + Hm[i, j]) / H[i, j]) / 2 H[j, i] = H[i, j] } } # Return Value: return(H) } ################################################################################ fBasics/R/dist-nigFit.R0000644000176200001440000003327714324532632014351 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: GENERALIZED DISTRIBUTION: # nigFit Fits parameters of a normal inverse Gaussian density # .nigFit.mle max Log-likelihood Estimation # .nigFit.gmm gemeralized method of moments estimation # .nigFit.mps maximum product spacings estimation # .nigFit.vmps minimum variance product spacings estimation ################################################################################ nigFit <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, method = c("mle", "gmm", "mps", "vmps"), scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Settings: method = match.arg(method) # Select: if (method == "mle") { # MLE: fit = .nigFit.mle(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } else if (method == "gmm") { # GMM: fit = .nigFit.gmm(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } else if (method == "mps") { # MPS: fit = .nigFit.mps(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } else if (method == "vmps") { # MPS: fit = .nigFit.vmps(x = x, alpha = alpha, beta = beta, delta = delta, mu = mu , scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) } # Return Value: fit } # ------------------------------------------------------------------------------ .nigFit.mle <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a NIG using maximum log-likelihood # Example: # set.seed(4711); x = rnig(500); mle = .nigFit.mle(x); mle@fit$estimate # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Objective Function: obj = function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e9) f = -sum(dnig(y, x[1], x[2], x[3], x[4], log = TRUE)) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x, "\n") } f } # Parameter Estimation: eps = 1e-10 BIG = 1000 fit = nlminb( start = c(alpha, beta, delta, mu), objective = obj, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(fit$par) <- c("alpha", "beta", "delta", "mu") # Rescale Result: if (scale) { fit$scaleParams = c(SD, SD, 1/SD, 1/SD) fit$par = fit$par / fit$scaleParams fit$objective = obj(fit$par, y = as.vector(x.orig), trace = trace) } else { fit$scaleParams = rep(1, time = length(fit$par)) } fit$scale = scale fit$estimate = fit$par fit$minimum = -fit$objective fit$code = fit$convergence # Standard Errors and t-Values: fit = .distStandardErrors(fit, obj, x) # Add Title and Description: if (is.null(title)) title = "Normal Inverse Gaussian Parameter Estimation" if (is.null(description)) description = "" # Optional Plot: if (doplot) .distFitPlot( fit, x = x.orig, FUN = "dnig", main = "NIG Parameter Estimation", span = span, add = add, ...) # Return Value: new("fDISTFIT", call = match.call(), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .nigFit.gmm <- function(x, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a NIG using GMM estimator # Example: # set.seed(4711); x = rnig(500); gmm = .nigFit.gmm(x)@fit$estimate; gmm # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Parameter Estimation: obj <- function(Theta, x) { # Parameters: alpha = Theta[1] beta = Theta[2] delta = Theta[3] mu = Theta[4] names(Theta) <- c("alpha", "beta", "delta", "mu") # Trace: if (TRUE) print(Theta) # Moments: m1 <- x - .ghMuMoments(1, alpha, beta, delta, mu, lambda = -0.5) m2 <- x^2 - .ghMuMoments(2, alpha, beta, delta, mu, lambda = -0.5) m3 <- x^3 - .ghMuMoments(3, alpha, beta, delta, mu, lambda = -0.5) m4 <- x^4 - .ghMuMoments(4, alpha, beta, delta, mu, lambda = -0.5) # Result: f <- cbind(m1, m2, m3, m4) return(f) } r <- .gmm(g = obj, x = x, t0 = c(1, 0, 1, 0)) names(r$par) <- c("alpha", "beta", "delta", "mu") # Add Title and Description: if (is.null(title)) title = "Normal Inverse Gaussian Parameter Estimation" if (is.null(description)) description = "" # Rescale Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = NA } fit = list(estimate = r$par) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NIG GMM Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .nigFit.mps <- function(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a NIG using maximum product spacings # Example: # set.seed(4711); x = rnig(500); mps = .nigFit.mps(x)@fit$estimate; mps # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Parameter Estimation: obj <- function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e9) DH = diff(c(0, na.omit(.pnigC(sort(y), x[1], x[2], x[3], x[4])), 1)) f = -mean(log(DH[DH > 0]))*length(y) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } eps = 1e-10 BIG = 1000 r = nlminb(start = c(alpha, beta, delta, mu), objective = obj, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu") # Standard Errors: hessian = tsHessian(x = r$par, fun = obj, y = x, trace = FALSE) colnames(hessian) = rownames(hessian) = names(r$par) varcov = solve(hessian) par.ses = sqrt(diag(varcov)) if (scale) par.ses = par.ses / c(SD, SD, 1/SD, 1/SD) # Add Title and Description: if (is.null(title)) title = "NIG mps Parameter Estimation" if (is.null(description)) description = "" # Result: if (scale) { r$par = r$par / c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list( estimate = r$par, minimum = -r$objective, error = par.ses, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NIG MPS Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } # ------------------------------------------------------------------------------ .nigFit.vmps <- function (x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL,description = NULL, ...) { # A function implemented by Yohan Chalabi # Description: # Fits parameters of a NIG using maximum product spacings # Example: # set.seed(4711); x = rnig(500); vmps = .nigFit.vmps(x)@fit$estimate; vmps # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x/SD } # Settings: CALL = match.call() # Parameter Estimation: obj <- function(x, y = x, trace) { if (abs(x[2]) >= x[1]) return(1e+9) DH = diff(c(0, na.omit(.pnigC(sort(y), x[1], x[2], x[3], x[4])), 1)) f = log(var(DH[DH > 0])) if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x[1], x[2], x[3], x[4], "\n") } f } eps = 1e-10 BIG = 1000 r = nlminb( start = c(alpha, beta, delta, mu), objective = obj, lower = c(eps, -BIG, eps, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("alpha", "beta", "delta", "mu") # Standard Errors: hessian = tsHessian(x = r$par, fun = obj, y = x, trace = FALSE) colnames(hessian) = rownames(hessian) = names(r$par) varcov = solve(hessian) par.ses = sqrt(diag(varcov)) if (scale) par.ses = par.ses / c(SD, SD, 1/SD, 1/SD) # Add Title and Description: if (is.null(title)) title = "NIG varMPS Parameter Estimation" if (is.null(description)) description = "" # Result: if (scale) { r$par = r$par/c(SD, SD, 1/SD, 1/SD) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } fit = list( estimate = r$par, minimum = -r$objective, error = par.ses, code = r$convergence) # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 501) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dnig(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) if (add) { lines(x = span, y = log(y.points), col = "steelblue") } else { plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("NIG varMPS Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } } # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Normal Inverse Gaussian Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } ################################################################################ fBasics/R/gui-nigSlider.R0000644000176200001440000000612614263246022014660 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # nigSlider Displays normal inverse Gausssian distribution function ################################################################################ nigSlider <- function() { # A function implemented by Diethelm Wuertz # Normal Inverse Gaussian Distribution: # dnig(x, alpha = 1, beta = 0, delta = 1, mu = 0) # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: N = .sliderMenu(no = 1) alpha = .sliderMenu(no = 2) beta = .sliderMenu(no = 3) delta = .sliderMenu(no = 4) mu = .sliderMenu(no = 5) # Plot Data: xmin = round(qnig(0.01, alpha, beta, delta, mu), digits = 2) xmax = round(qnig(0.99, alpha, beta, delta, mu), digits = 2) s = seq(xmin, xmax, length = N) y1 = dnig(s, alpha, beta, delta, mu) y2 = pnig(s, alpha, beta, delta, mu) main1 = paste("NIG Density\n", "alpha = ", as.character(alpha), " | ", "beta = ", as.character(beta), " | ", "delta = ", as.character(delta), " | ", "mu = ", as.character(mu)) main2 = paste("NIG Probability\n", "xmin 0.01% = ", as.character(xmin), " | ", "xmax 0.99% = ", as.character(xmax), " | ") # Frame: par(mfrow = c(2, 1), cex = 0.7) # Density: plot(s, y1, type = "l", xlim = c(xmin, xmax), col = "steelblue") abline (h = 0, lty = 3) title(main = main1) # Probability: plot(s, y2, type = "l", xlim = c(xmin, xmax), ylim = c(0, 1), col = "steelblue" ) abline(h = 0.0, lty = 3) abline(h = 1.0, lty = 3) abline(h = 0.5, lty = 3) abline(v = mu, lty = 3, col = "red") title(main = main2) # Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "N", "alpha", "beta", "delta", "mu"), minima = c( 50, 0.00, -2.00, 0.00, -5.0), maxima = c(1000, 2.00, +2.00, 10.00, +5.0), resolutions = c( 50, 0.20, 0.20, 1.00, 1.0), starts = c( 50, 1.00, 0.00, 1.00, 0.0)) } ################################################################################ fBasics/R/dist-snigFit.R0000644000176200001440000000322214263246022014514 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # snigFit Fits parameters of a standardized NIG density ################################################################################ snigFit = function(x, zeta = 1, rho = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # Update Slots: if (is.null(title)) title = "SNIG Parameter Estimation" # Quick and dirty ... ans = sghFit(x, zeta = zeta, rho = rho, lambda = -0.5, include.lambda = FALSE, scale = scale, doplot = doplot, span = span, trace = trace, title = title, description = description, ...) # Update Slots: ans@call = match.call() ans@model = "Standarized NIG Distribution" # Return Value: ans } ################################################################################ fBasics/R/dist-gldRobMoments.R0000644000176200001440000001055714263246022015676 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gldMED Returns true GLD median # gldIQR Returns true GLD inter quartal range # gldSKEW Returns true GLD robust skewness # gldKURT Returns true GLD robust kurtosis ################################################################################ gldMED <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld median # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Median Q = qgld(p=0.5, lambda1, lambda2, lambda3, lambda4) med = c(MED = Q) # Return Value: med } # ------------------------------------------------------------------------------ gldIQR <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld inter quartal range # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Inter Quartile Range Q = numeric() Q[1] = qgld(p=0.25, lambda1, lambda2, lambda3, lambda4) Q[2] = qgld(p=0.75, lambda1, lambda2, lambda3, lambda4) iqr = c(IQR = Q[[2]] - Q[[1]]) # Return Value: iqr } # ------------------------------------------------------------------------------ gldSKEW <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld robust gld skewness # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Robust Skewness: Q = numeric() Q[1] = qgld(p=0.25, lambda1, lambda2, lambda3, lambda4) Q[2] = qgld(p=0.50, lambda1, lambda2, lambda3, lambda4) Q[3] = qgld(p=0.75, lambda1, lambda2, lambda3, lambda4) skew = c(SKEW = ( Q[[3]] + Q[[1]] - 2* Q[[2]] ) / (Q[[3]] - Q[[1]] ) ) # Return Value: skew } # ------------------------------------------------------------------------------ gldKURT <- function(lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) { # A function implemented by Diethelm Wuertz # Description: # Returns true gld robust gld kurtosis # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # FUNCTION: # gld Robust Kurtosis: Q = numeric() for (p in (1:7)/8) Q = c(Q, qgld(p, lambda1, lambda2, lambda3, lambda4)) kurt = c(KURT = (Q[[7]] - Q[[5]] + Q[[3]] - Q[[1]]) / (Q[[6]] - Q[[2]])) # Return Value: kurt } ################################################################################ fBasics/R/dist-distCheck.R0000644000176200001440000001216514650724111015020 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # distCheck Checks consistency of distributions ################################################################################ distCheck <- function(fun = "norm", n = 1000, robust = TRUE, subdivisions = 100, ...) { # A function implemented by Diethelm Wuertz # Description: # Checks consistency of distributions # Arguments: # fun - a character string denoting the name of the distribution # n - an integer specifying the number of random variates to be # generated # robust - a logical flag, should robust estimates be used? By # default \code{TRUE} # subdivisions - an integer specifying the numbers of subdivisions # in integration __ NB: only used in one place, *not* in the other.. hmm # ... - the distributional parameters and passed to integrate() ### FIXME (MM): add args.integrate = list() to be passed to integrate(), ### ----- and pass the others to distrib.functions # Examples: # .distCheck("norm", mean = 1, sd = 1) # .distCheck("t", df = 4) # .distCheck("exp", rate = 2) # .distCheck("weibull", shape = 1) # FUNCTION: # Distribution Functions: cat("\nDistribution Check for:", fun, "\n ") CALL = match.call() cat("Call: ") cat(paste(deparse(CALL), sep = "\n", collapse = "\n"), "\n", sep = "") dfun = match.fun(paste("d", fun, sep = "")) pfun = match.fun(paste("p", fun, sep = "")) qfun = match.fun(paste("q", fun, sep = "")) rfun = match.fun(paste("r", fun, sep = "")) # Range: xmin = qfun(p = 0.01, ...) xmax = qfun(p = 0.99, ...) # Check 1 - Normalization: NORM = integrate(dfun, lower = -Inf, upper = Inf, subdivisions = subdivisions, stop.on.error = FALSE, ...) cat("\n1. Normalization Check:\n NORM ") print(NORM) normCheck = (abs(NORM[[1]]-1) < 0.01) # Check 2: cat("\n2. [p-pfun(qfun(p))]^2 Check:\n ") p = c(0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999) P = pfun(qfun(p, ...), ...) pP = round(rbind(p, P), 3) print(pP) RMSE = sd(p-P) print(c(RMSE = RMSE)) rmseCheck = (abs(RMSE) < 0.0001) # Check 3: cat("\n3. r(", n, ") Check:\n", sep = "") r = rfun(n = n, ...) if (!robust) { SAMPLE.MEAN = mean(r) SAMPLE.VAR = var(r) } else { robustSample = MASS::cov.mcd(r, quantile.used = floor(0.95*n)) SAMPLE.MEAN = robustSample$center SAMPLE.VAR = robustSample$cov[1,1] } SAMPLE = data.frame(t(c(MEAN = SAMPLE.MEAN, "VAR" = SAMPLE.VAR)), row.names = "SAMPLE") print(signif(SAMPLE, 3)) fun1 = function(x, ...) { x * dfun(x, ...) } fun2 = function(x, M, ...) { x^2 * dfun(x, ...) } MEAN = integrate(fun1, lower = -Inf, upper = Inf, subdivisions = 5000, stop.on.error = FALSE,...) cat(" X ") print(MEAN) VAR = integrate(fun2, lower = -Inf, upper = Inf, subdivisions = 5000, stop.on.error = FALSE, ...) cat(" X^2 ") print(VAR) EXACT = data.frame(t(c(MEAN = MEAN[[1]], "VAR" = VAR[[1]] - MEAN[[1]]^2)), row.names = "EXACT ") print(signif(EXACT, 3)) meanvarCheck = (abs(SAMPLE.VAR-EXACT$VAR)/EXACT$VAR < 0.1) cat("\n") # Done: ans = list( normCheck = normCheck, rmseCheck = rmseCheck, meanvarCheck = meanvarCheck) # Return Value: unlist(ans) } # ------------------------------------------------------------------------------ ## (2023-10-15) GNB: Can't remove .distCheck easily since in recent versions of ## fGarch it was imported explicitly in NAMESPACE. This means that if I remove ## it, I need to ask every maintainer importing fGarch to require fGarch (>= ## 4031.90) - not impossible, but not particularly reasonable in the short term. ## ## So, keep it for now. .distCheck <- function(...) { if(("fGarch" %in% loadedNamespaces() && packageVersion("fGarch") < "4031.90") || ("fExtremes" %in% loadedNamespaces() && packageVersion("fExtremes") <= "4021.83") || ("stabledist" %in% loadedNamespaces() && packageVersion("stabledist") <= "0.7-1") ) { ## let it work with old versions of fGarch, fExtremes, stabledist distCheck(...) } else stop("'.distCheck' is defunct. Use 'distCheck' instead.") } ################################################################################ fBasics/R/matrix-pdl.R0000644000176200001440000000442714263246022014241 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # pdl Regressor matrix for polynomial distributed lags ################################################################################ pdl <- function(x, d = 2, q = 3, trim = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Regressor matrix for polynomial distributed lags # Aruments: # x - a numeric vector. # d - an integer specifying the order of the polynomial. # q - an integer specifying the number of lags to use in # creating polynomial distributed lags. This must be # greater than d. # trim - a logical flag; if TRUE, the missing values at # the beginning of the returned matrix will be trimmed. # Value: # Returns a matrix representing the regressor matrix. # Example: # stack.loss = c( # 42, 37, 37, 28, 18, 18, 19, 20, 15, 14, 14, # 13, 11, 12, 8, 7, 8, 8, 9, 15, 15) # pdl(stack.loss) # FUNCTION: # Check: stopifnot(q > d) # Polynomial distributed lags: M = tslag(x, 1:q, FALSE) C = NULL for (i in 0:d) { C = rbind(C, (1:q)^i) } Z = NULL for (i in 1:(d+1)) { Z = cbind(Z, apply(t(C[i,]*t(M)), 1, sum)) } Z[, 1] = Z[, 1] + x # Trim: if (trim) { indexes = (1:length(Z[,1]))[!is.na(apply(Z, 1, sum))] Z = Z[indexes, ] } # Return Value: Z } ################################################################################ fBasics/R/dist-ghtMoments.R0000644000176200001440000000726414263246022015250 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtMean Returns true GH Student-t mean # ghtVar Returns true GH Student-t variance # ghtSkew Returns true GH Student-t skewness # ghtKurt Returns true GH Student-t kurtosis # FUNCTION: DESCRIPTION: # ghtMoments Returns true GH Student-t moments ################################################################################ ghtMean <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true Mean of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghMean(alpha, beta, delta, mu, lambda) } # ------------------------------------------------------------------------------ ghtVar <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true variance of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghVar(alpha, beta, delta, mu, lambda) } # ------------------------------------------------------------------------------ ghtSkew <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true Skewness of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghSkew(alpha, beta, delta, mu, lambda) } # ------------------------------------------------------------------------------ ghtKurt <- function(beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns true Kurtosis of the GH Student-t distribution # FUNCTION: # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghKurt(alpha, beta, delta, mu, lambda) } ################################################################################ ghtMoments <- function(order, type = c("raw", "central", "mu"), beta=0.1, delta=1, mu=0, nu=10) { # A function implemented by Diethelm Wuertz # Descriptions: # Returns moments of the GH Student-t distribution # FUNCTION: # Settings: type = match.arg(type) # GH Parameters: alpha = abs(beta) + 1e-6 lambda = -nu/2 # Return Value: ghMoments(order, type, alpha, beta, delta, mu, lambda) } ################################################################################ fBasics/R/plot-varpie.R0000644000176200001440000001310014263246022014406 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # Function: DESCRIPTION: # .varpiePlot Plots a pie with variable radii # .varpieDemo Creates some demo plots ################################################################################ .varpiePlot <- function(x, radius = 0.8, labels = names(x), edges = 200, clockwise = FALSE, init.angle = if (clockwise) 90 else 0, density = NULL, angle = 45, col = NULL, border = NULL, lty = NULL, main = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Plots a pie with variable radii. # Note: # The idea is given in: # http://addictedtor.free.fr/packages/A2R/lastVersion/R/spieChart.R # Source: # Package: A2R # Type: Package # Title: Romain Francois misc functions # Version: 0.0-4 # Date: 2005-08-05 # Author: Romain Francois # Maintainer: Romain Francois # Description: Some functions of Romain Francois collection # License: GPL version 2 or newer # URL: http://addictedtor.free.fr/packages # FUNCTION: # Pie: if (length(radius) == 1) { # Normal Pie Plot: pie(x =x, labels = labels, edges = edges, radius = radius, clockwise = clockwise, init.angle = init.angle, density = density, angle = angle, col = col, border = border, lty = lty, main = main, ...) } else { # Variable Pie Plot: radius = radius/max(radius) X = x if (!is.numeric(x) || any(is.na(x) | x < 0)) stop("'x' values must be positive.") if (is.null(labels)) { labels = as.character(1:length(x)) } else { labels = as.graphicsAnnot(labels) } x = c(0, cumsum(x)/sum(x)) dx = diff(x) nx = length(dx) plot.new() pin = par("pin") xlim = ylim = c(-1, 1) if (pin[1] > pin[2]) { xlim = (pin[1]/pin[2]) * xlim } else { ylim = (pin[2]/pin[1]) * ylim } plot.window(xlim, ylim, "", asp = 1) if (is.null(col)) col = rainbow(n) if (is.null(border)) border = "darkgrey" col = if (is.null(density)) col else par("fg") pie(x = X, col = "white", lty = NULL, radius = max(radius), border = border, labels = labels, ...) phi = seq(0, 2*pi, length = 361) for (i in 1:length(radius)) lines(radius[i]*cos(phi), radius[i]*sin(phi), col = border) col = rep(col, length.out = nx) border = rep(border, length.out = nx) lty = rep(lty, length.out = nx) angle = rep(angle, length.out = nx) density = rep(density, length.out = nx) twopi = if (clockwise) -2 * pi else 2 * pi t2xy = function(t, i) { t2p = twopi * t + init.angle * pi/180 list(x = radius[i] * cos(t2p), y = radius[i] * sin(t2p)) } for (i in 1:nx) { n = max(2, floor(edges * dx[i])) P = t2xy(seq(x[i], x[i + 1], length = n), i) polygon(c(P$x, 0), c(P$y, 0), density = density[i], angle = angle[i], border = border[i], col = col[i], lty = lty[i]) P = t2xy(mean(x[i + 0:1]), i) # lab = as.character(labels[i]) # if (!is.na(lab) && nchar(lab)) { # lines(c(1, 1.05) * P$x, c(1, 1.05) * P$y, col = "red") # text(1.1 * P$x, 1.1 * P$y, labels[i], xpd = TRUE, # col = "red", adj = ifelse(P$x < 0, 1, 0), ...) } } title(main = main, ...) } # Return Values: invisible(NULL) } # ------------------------------------------------------------------------------ .varpieDemo <- function() { # Description: # Creates some demo plots # FUNCTION: # Demos: par (ask = TRUE) n = 64; phi = seq(0, pi/4, length = n) X = Y = runif(32) .varpiePlot(X, radius = Y, labels = "", border = "white") points(0, 0, pch = 19, cex = 4) n = 32; phi = seq(0, pi/4, length = n) X = abs(cos(phi)); Y = abs(sin(phi)) .varpiePlot(X, radius = Y, labels = "", border = "white") points(0, 0, pch = 19, cex = 4) n = 64; phi = seq(0, pi, length = n) X = abs(cos(phi)); Y = abs(sin(phi)) .varpiePlot(X, radius = Y, labels = "", border = "white", init.angle = -90) points(0, 0, pch = 19, cex = 4) n = 64; phi = seq(0, pi, length = n) X = abs(cos(phi)); Y = abs(sin(phi)) .varpiePlot(X, radius = Y, labels = "", col = heat.colors(n), border = "white", init.angle = -90) points(0, 0, pch = 19, cex = 4) } ################################################################################ fBasics/R/builtin-gelGmm.R0000644000176200001440000004574414263246022015043 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: # .rho # .get_lamb # .gel # .smooth_g # .bwNeweyWest2 # .summary.gel # .get_dat ################################################################################ # Code borrowed from # R's contributed package "gmm" written by Pierre Chausse. # Rmetrics: # Note that gmm is not available on Debian as of 2009-04-28. # To run these functions under Debian/Rmetrics we have them # implemented here as a builtin. # We also made modifications for tailored usage with Rmetrics. # Note that the dependences in the original package requires zoo # which may create conflicts with Rmetrics timeDate/timeSeries. # Package: gmm # Version: 1.0-4 # Date: 2009-03-10 # Title: Generalized Method of Moments and Generalized Empirical Likelihood # Author: Pierre Chausse # Maintainer: Pierre Chausse # Description: It is a complete suite to estimate models based on moment # conditions. It includes the two step Generalized method of moments # (GMM) of Hansen(1982), the iterated GMM and continuous updated # estimator (CUE) of Hansen-Eaton-Yaron(1996) and several methods that # belong to the Generalized Empirical Likelihood (GEL) family of estimators, # as presented by Smith(1997), Kitamura(1997), Newey-Smith(2004) and # Anatolyev(2005). # Depends: R (>= 2.0.0), sandwich, tseries, mvtnorm # Imports: stats # License: GPL (>= 2) # ------------------------------------------------------------------------------ .rho <- function(x, lamb, derive = 0, type = c("EL", "ET", "CUE"), drop = TRUE) { type <- match.arg(type) lamb <- matrix(lamb,ncol=1) gml <- x%*%lamb ch <- 0 if (derive==0) { if (type == "EL") { ch <- sum(gml>=1) if (drop) { gml <- (gml<1)*gml rhomat <- log(1-gml) } else { if (ch>0) rhomat <- NaN else rhomat <- log(1-gml) } } if (type == "ET") rhomat <- -exp(gml) if (type == "CUE") rhomat <- -gml -0.5*gml^2 } if (derive==1) { if (type == "EL") rhomat <- -1/(1-gml) if (type == "ET") rhomat <- -exp(gml) if (type == "CUE") rhomat <- -1 -gml } if (derive==2) { if (type == "EL") rhomat <- -1/(1-gml)^2 if (type == "ET") rhomat <- -exp(gml) if (type == "CUE") rhomat <- -1 } rhom <-list(ch = ch, rhomat=rhomat) return(rhom) } # ------------------------------------------------------------------------------ .get_lamb <- function(g, tet, x,type = c('EL', 'ET', 'CUE'), tol_lam=1e-12, maxiterlam=1000, tol_obj = 1e-7) { type <- match.arg(type) gt <- g(tet,x) n <- nrow(gt) tol_cond=1e-12 gb <- colMeans(gt) khat <- crossprod(gt)/n lamb0 <- -solve(khat,gb) conv_mes <- "Normal convergence" singular <-0 crit <-1e30 crit0 <- crit dcrit <- 10 dgblam <- -10 gblam0 <- NULL j <- 1 while ((crit > tol_lam*( 1+sqrt( crossprod(lamb0) ) ) ) & (j<=maxiterlam)) { rho2 <- as.numeric(.rho(gt,lamb0,derive=2,type=type)$rhomat) rho1 <- as.numeric(.rho(gt,lamb0,derive=1,type=type)$rhomat) gblam <- colMeans(rho1*gt) klam <- crossprod(rho2*gt,gt)/n chklam <- sum(abs(klam)) if (!is.null(gblam0)) dgblam <- crossprod(gblam)-crossprod(gblam0) if (is.na(chklam) | chklam == 0 | chklam == Inf | dgblam>0 | dgblam == Inf | is.na(dgblam) | dcrit < 0) { lamb1 <- rep(sqrt(1/n),length(lamb0)) crit <- 0 singular=2 conv_mes <- "The algorithm produced singular system, NaN or Inf" } else { if (rcond(klam)>tol_cond) { lamb1 <- lamb0-solve(klam,gblam) crit <- sqrt(crossprod(lamb0-lamb1)) lamb0 <- lamb1 } else { lamb1 <- rep(sqrt(1/n),length(lamb0)) crit <- 0 singular=2 conv_mes <- "The algorithm produced singular system" } } gblam0 <- gblam j <- j+1 dcrit<- crit0-crit crit0 <- crit } z <- list("lambda"=lamb1,singular=singular,conv_mes=conv_mes) if (j>maxiterlam | max(abs(gblam))>tol_obj) { singular <- 1 conv_mes <- "No convergence after 'maxiterlam' iterations" z$singular <- singular } z$obj <- crossprod(gblam) return(z) } # ------------------------------------------------------------------------------ .gel <- function(g,x,tet0,gradv=NULL,smooth=FALSE,type=c("EL","ET","CUE","ETEL"), vcov=c("HAC","iid"), kernel = c("Bartlett", "Parzen", "Truncated", "Tukey-Hanning"), bw = .bwAndrews2, approx = c("AR(1)", "ARMA(1,1)"), prewhite = 1, ar.method = "ols", tol_weights = 1e-7, tol_lam=1e-9, tol_obj = 1e-9, tol_mom = 1e-9,maxiterlam=1000, constraint = FALSE, intercept = TRUE, optfct = c("optim", "optimize"), optlam = c("iter", "numeric"), ...) { vcov=match.arg(vcov) type <- match.arg(type) optfct <- match.arg(optfct) optlam <- match.arg(optlam) weights = .weightsAndrews2 if (type == "ETEL") { typel <- "ET" typet <- "EL" } else { typel <- type typet <- type } approx <- match.arg(approx) kernel <- match.arg(kernel) k <- length(tet0) typeg=0 if (is(g,"formula")) { typeg=1 dat <- .get_dat(g, x, intercept = intercept) x <- dat$x g <- function(tet,x,ny=dat$ny,nh=dat$nh,k=dat$k) { tet <- matrix(tet,ncol=k) if (intercept) { e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } if (!intercept) e <- x[,1:ny] - x[,(ny+1):(ny+k)]%*%t(tet) gt <- e*x[,ny+k+1] if (nh > 1) { for (i in 2:nh) { gt <- cbind(gt,e*x[,(ny+k+i)]) } } return(gt) } gradv <- function(tet,x,ny=dat$ny,nh=dat$nh,k=dat$k) { tet <- matrix(tet,ncol=k) dgb <- -(t(x[,(ny+k+1):(ny+k+nh)])%*%x[,(ny+1):(ny+k)]) %x% diag(rep(1,ny))/nrow(x) return(dgb) } } if (typeg) n <- nrow(x) else n = nrow(g(tet0,x)) if (smooth) { g1 <- g rgmm <- .gmm(g,x,tet0,wmatrix="ident") if (is.function(weights)) w <- weights(g(rgmm$par,x), kernel=kernel, bw=bw, prewhite = prewhite,ar.method=ar.method,approx=approx, tol=tol_weights) else w <- weights sg <- function(thet,x) { gf <- g1(thet,x) gt <- .smooth_g(gf, weights=w)$smoothx return(gt) } g <- sg } lll <- 1 thetf <- function(tet) { if (optlam == "iter") { lamblist <- .get_lamb(g,tet,x,type=typel,tol_lam=tol_lam, maxiterlam=maxiterlam,tol_obj=tol_obj) lamb <- lamblist$lambda gt <- g(tet,x) pt <- -.rho(gt,lamb,type=typet,derive=1)$rhomat/nrow(gt) checkmom <- sum(as.numeric(pt)*gt) if (lamblist$singular==0) p <- sum(.rho(gt,lamb,type=typet)$rhomat) + abs(checkmom)/tol_mom if (lamblist$singular==1) p <- sum(.rho(gt,lamb,type=typet)$rhomat) + abs(checkmom)/tol_mom + lamblist$obj/tol_mom if (lamblist$singular==2) p <- 1e50*lll lll <- lll+1 } else { gt <- g(tet,x) rhofct <- function(lamb) { rhof <- -sum(.rho(gt,lamb,type=typet)$rhomat) return(rhof) } if (ncol(gt)>1) rlamb <- optim(rep(0,ncol(gt)),rhofct,...) else { rlamb <- optimize(rhofct,c(-1,1)) rlamb$par <- rlamb$minimum rlamb$value <- rlamb$objective } lamb <- rlamb$par pt <- -.rho(gt,lamb,type=typet,derive=1)$rhomat/nrow(gt) checkmom <- sum(as.numeric(pt)*gt) p <- -rlamb$value + (checkmom)^2/tol_mom + (sum(as.numeric(pt))-1)^2/tol_mom } return(p) } if (!constraint) if (optfct == "optim") res <- optim(tet0,thetf,...) else { res <- optimize(thetf,tet0,...) res$par <- res$minimum res$convergence <- "Pas applicable" } else res <- constrOptim(tet0,thetf,...) rlamb <- .get_lamb(g,res$par,x,type=typel,tol_lam=tol_lam,maxiterlam=maxiterlam,tol_obj=tol_obj) z <- list(par=res$par,lambda=rlamb$lam,conv_lambda=rlamb$conv_mes,conv_par=res$convergence) z$foc_lambda <- rlamb$obj z$type <- type z$gt <- g(z$par,x) rhom <- .rho(z$gt,z$lambda,type=typet) z$pt <- -.rho(z$gt,z$lambda,type=typet,derive=1)$rhomat/n z$conv_moment <- colSums(as.numeric(z$pt)*z$gt) z$conv_pt <- sum(as.numeric(z$pt)) z$objective <- sum(as.numeric(rhom$rhomat)-.rho(1,0,type=typet)$rhomat)/n if (type == "EL") { z$badrho <- rhom$ch names(z$badrho) <- "Number_of_bad_rho" } Gf <- function(thet) { myenv <- new.env() assign('x',x,envir=myenv) assign('thet',thet,envir=myenv) barg <- function(x,thet) { gt <- g(thet,x) gbar <- as.vector(colMeans(gt)) return(gbar) } G <- attr(numericDeriv(quote(barg(x,thet)),"thet",myenv),"gradient") return(G) } if (!is.function(gradv)) G <- Gf(z$par) else G <- gradv(z$par,x) if (vcov == "iid") khat <- crossprod(z$gt) else khat <- .HAC(g(z$par,x), kernel=kernel, bw=bw,prewhite=prewhite, ar.method=ar.method,approx=approx,tol=tol_weights) kg <- solve(khat,G) z$vcov_par <- solve(crossprod(G,kg))/n z$vcov_lambda <- ((solve(khat)-kg%*%z$vcov_par%*%t(kg)))/n if (smooth) z$weights<-w if (typeg ==0) { names(z$par) <- paste("Theta[",1:k,"]",sep="") colnames(z$gt) <- paste("gt[",1:ncol(z$gt),"]",sep="") names(z$lambda) <- paste("Lambda[",1:ncol(z$gt),"]",sep="") } if (typeg == 1) { namex <- colnames(dat$x[,(dat$ny+1):(dat$ny+dat$k)]) nameh <- colnames(dat$x[,(dat$ny+dat$k+1):(dat$ny+dat$k+dat$nh)]) if (dat$ny > 1) { namey <- colnames(dat$x[,1:dat$ny]) names(z$par) <- paste(rep(namey,dat$k),"_",rep(namex,rep(dat$ny,dat$k)),sep="") colnames(z$gt) <- paste(rep(namey,dat$nh),"_",rep(nameh,rep(dat$ny,dat$nh)),sep="") names(z$lambda) <- paste("Lam(",rep(namey,dat$nh),"_",rep(nameh,rep(dat$ny,dat$nh)),")",sep="") } if (dat$ny == 1) { names(z$par) <- namex colnames(z$gt) <- nameh names(z$lambda) <- nameh } } dimnames(z$vcov_par) <- list(names(z$par),names(z$par)) dimnames(z$vcov_lambda) <- list(names(z$lambda),names(z$lambda)) class(z) <- "gel" return(z) } # ------------------------------------------------------------------------------ .smooth_g <- function(x, bw = .bwAndrews2, prewhite = 1, ar.method = "ols", weights=.weightsAndrews2, kernel=c("Bartlett","Parzen","Truncated","Tukey-Hanning"), approx = c("AR(1)","ARMA(1,1)"), tol = 1e-7) { kernel <- match.arg(kernel) approx <- match.arg(approx) n <- nrow(x) if (is.function(weights)) { w <- weights(x, bw = bw, kernel = kernel, prewhite = prewhite, ar.method = ar.method, tol = tol, verbose = FALSE, approx = approx) } else w <- weights rt <- length(w) if (rt >= 2) { w <- c(w[rt:2],w) w <- w / sum(w) rt <- rt-1 sgt <- function(t) crossprod(x[(t-rt):(t+rt),],w) x[(rt+1):(n-rt),] <- t(sapply((rt+1):(n-rt),sgt)) sx <- list("smoothx"=x,"kern_weights"=w) return(sx) } else sx <- list("smoothx"=x,"kern_weights"=1) return(sx) } # ------------------------------------------------------------------------------ .bwNeweyWest2 <- function(x, kernel = c("Bartlett", "Parzen", "Quadratic Spectral", "Truncated", "Tukey-Hanning"), prewhite = 1, ar.method = "ols",...) { kernel <- match.arg(kernel) if (kernel %in% c("Truncated", "Tukey-Hanning")) stop(paste("Automatic bandwidth selection only available for ", dQuote("Bartlett"), ", ", dQuote("Parzen"), " and ", dQuote("Quadratic Spectral"), " kernel. Use ", sQuote(".bwAndrews2"), " instead.", sep = "")) prewhite <- as.integer(prewhite) n <- nrow(x) k <- ncol(x) weights <- rep(1, k) if (length(weights) < 2) weights <- 1 mrate <- switch(kernel, Bartlett = 2/9, Parzen = 4/25, "Quadratic Spectral" = 2/25) m <- floor(ifelse(prewhite > 0, 3, 4) * (n/100)^mrate) if (prewhite > 0) { x <- as.matrix(na.omit(ar(x, order.max = prewhite, demean = FALSE, aic = FALSE, method = ar.method)$resid)) n <- n - prewhite } hw <- x %*% weights sigmaj <- function(j) sum(hw[1:(n - j)] * hw[(j + 1):n])/n sigma <- sapply(0:m, sigmaj) s0 <- sigma[1] + 2 * sum(sigma[-1]) s1 <- 2 * sum(1:m * sigma[-1]) s2 <- 2 * sum((1:m)^2 * sigma[-1]) qrate <- 1/(2 * ifelse(kernel == "Bartlett", 1, 2) + 1) rval <- switch(kernel, Bartlett = { 1.1447 * ((s1/s0)^2)^qrate }, Parzen = { 2.6614 * ((s2/s0)^2)^qrate }, "Quadratic Spectral" = { 1.3221 * ((s2/s0)^2)^qrate }) rval <- rval * (n + prewhite)^qrate return(rval) } # ------------------------------------------------------------------------------ .summary.gel <- function(object,interval=FALSE, ...) { z <- object n <- nrow(z$gt) khat <- crossprod(z$gt)/n gbar <- colMeans(z$gt) se_par <- sqrt(diag(z$vcov_par)) par <- z$par tval <- par/se_par se_parl <- sqrt(diag(z$vcov_lambda)) lamb <- z$lambda tvall <- lamb/se_parl LR_test <- 2*z$objective*n LM_test <- n*crossprod(z$lambda,crossprod(khat,z$lambda)) J_test <- n*crossprod(gbar,solve(khat,gbar)) test <- c(LR_test,LM_test,J_test) vptest <- pchisq(test,(ncol(z$gt)-length(z$par)),lower.tail=FALSE) ans <- list(type=z$type) names(ans$type) <-"Type of GEL" ans$par <- round(cbind(par,se_par, tval, 2 * pnorm(abs(tval), lower.tail = FALSE)),5) ans$lambda <- round(cbind(lamb,se_parl, tvall, 2 * pnorm(abs(tvall), lower.tail = FALSE)),5) dimnames(ans$par) <- list(names(z$par), c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) dimnames(ans$lambda) <- list(names(z$lambda), c("Estimate", "Std. Error", "t value", "Pr(>|t|)")) ans$test <- cbind(test,vptest) dimnames(ans$test) <- list(c("LR test","LM test","J test"),c("statistics","p-value")) if (interval != FALSE) { zs <- qnorm((1-interval)/2,lower.tail=FALSE) ch <- zs*se_par ans$interval_par <- cbind(par-ch,par+ch) dimnames(ans$interval_par) <- list(names(par),c("Theta_lower","Theta_upper")) chl <- zs*se_parl ans$interval_lam <- cbind(lamb-chl,lamb+chl) dimnames(ans$interval_lam) <- list(names(lamb),c("Lambda_lower","Lambda_upper")) } if (z$type == "EL") ans$badrho <- z$badrho if (!is.null(z$weights)) { ans$weights <- z$weights } ans$conv_par <- z$conv_par ans$conv_pt <- z$conv_pt ans$conv_moment <- cbind(z$conv_moment) ans$conv_lambda <- z$conv_lambda names(ans$conv_par) <- "Convergence_code_theta" names(ans$conv_pt) <- "Sum_of_pt" names(ans$conv_lambda) <- "Convergence_code_for_lambda" dimnames(ans$conv_moment) <- list(names(z$gt),"Sample_moment_with_pt") class(ans) <- "summary.gel" ans } # ------------------------------------------------------------------------------ .get_dat <- function(formula,h,intercept=TRUE) { cl <- match.call() mf <- match.call(expand.dots = FALSE) m <- match(c("formula", "data"), names(mf), 0L) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1L]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) mt <- attr(mf, "terms") h <- cbind(rep(1,nrow(h)),h) colnames(h) <- c("Intercept",paste("h",1:(ncol(h)-1),sep="")) y <- as.matrix(model.response(mf, "numeric")) x <- as.matrix(model.matrix(mt, mf, NULL)) if (!intercept) { x <- as.matrix(x[,2:ncol(x)]) h <- as.matrix(h[,2:ncol(h)]) } ny <- ncol(y) k <- ncol(x) nh <- ncol(h) if (nrow(y) != nrow(x) | nrow(x) != nrow(h) | nrow(y)!=nrow(h)) stop("The number of observations of X, Y and H must be the same") if (nh1) colnames(y) <- paste("y",1:ncol(y),sep="") if (ny == 1) colnames(y) <- "y" } x <- cbind(y,x,h) return(list(x=x,nh=nh,ny=ny,k=k)) } ################################################################################ fBasics/R/gui-HeavisideSlider.R0000644000176200001440000000506314263246022016003 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: HEAVISIDE SLIDER: # .heavisideSlider Displays Heaviside and related functions ################################################################################ .heavisideSlider <- function() { # A function implemented by Diethelm Wuertz # Description # Displays Heaviside and related functions # FUNCTION: # Internal Function: refresh.code = function(...) { # Sliders: a = .sliderMenu(no = 1) by = .sliderMenu(no = 2) # Frame: par(mfrow = c(2, 2), cex = 0.7) # FGN TimeSeries: x = seq(-10, 10, by = by) H = Heaviside(x, a) Sign = Sign(x, a) Delta = Delta(x, a) Boxcar = Boxcar(x, a) Ramp = Ramp(x, a) plot(x, H, type = "b", ylim = c(-1, 1), col = "steelblue", pch = 19) title(main = paste("Heaviside | a =", a)) grid() plot(x, Sign, type = "b", ylim = c(-1, 1), col = "steelblue", pch = 19) title(main = paste("Sign | a =", a)) grid() plot(x, Boxcar, type = "b", ylim = c(-1, 1), col = "steelblue", pch = 19) title(main = paste("Boxcar | a =", a)) grid() plot(x, Ramp, type = "b", col = "steelblue", pch = 19) title(main = paste("Ramp | a =", a)) grid() # Reset Frame: par(mfrow = c(1, 1), cex = 0.7) } # Open Slider Menu: .sliderMenu(refresh.code, names = c( "a", "by"), minima = c( -10, 0.1), maxima = c( 10, 1.0), resolutions = c( 0.1, 0.1), starts = c( 0, 0.2)) } ################################################################################ fBasics/R/stats-maxdd.R0000644000176200001440000002240514263246022014405 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR Description. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # maxddStats Expectation of Drawdowns for BM with drift # .maxddStats Utility function called by "maxddStats" # FUNCTION: DISTRIBUTION AND RANDOM VARIATES: # dmaxdd Density function of mean Max-Drawdowns # pmaxdd Probability function of mean Max-Drawdowns # rmaxdd Random Variates of mean Max-Drawdowns ################################################################################ maxddStats <- function(mean = 0, sd = 1, horizon = 1000) { # A function implemented by Diethelm Wuertz # Description: # Calculates Expectation Value E[D] of maximum Drawdowns of # Brownian Motion for a given drift "mu", variance "sigma", # and runtime "horizon" of the Brownian Motion process. # Arguments: # mu - Drift of Brownian Motion, a numeric vector. # sigma - Standard Deviation of Brownian Motion, a numeric value. # horizon - Runtime of the process, the time horizon of the # investor, a numeric value. # Details: # Interpolates the data given in the table of Appendix B # in Magdon-Ismail et al. [2003], "On the Maximum Drawdown # of Brownian Motion". The interpolation is done with R's # function spline(). # Notes. # This computes for a vector of horizon values. # FUNCTION: # Statistics: ans = NULL for (i in 1:length(horizon)) { ans = c(ans, .maxddStats(mu = mean, sigma = sd, horizon = horizon[i])) } # Return Value: ans } # ------------------------------------------------------------------------------ .maxddStats <- function(mu = 0, sigma = 1, horizon = 1000) { # A function implemented by Diethelm Wuertz # Description: # Utility function called by "maxddStats" # Arguments: # see function "maxddStats # FUNCTION: # Internal Function - POSITIVE CASE: mu > 0 # Left Table from Appendix B: QP = function(x) { gamma = sqrt(pi/8) vqn = t(matrix(c( 0.0005,0.019690, 0.0010,0.027694, 0.0015,0.033789, 0.0020,0.038896, 0.0025,0.043372, 0.0050,0.060721, 0.0075,0.073808, 0.0100,0.084693, 0.0125,0.094171, 0.0150,0.102651, 0.0175,0.110375, 0.0200,0.117503, 0.0225,0.124142, 0.0250,0.130374, 0.0275,0.136259, 0.0300,0.141842, 0.0325,0.147162, 0.0350,0.152249, 0.0375,0.157127, 0.0400,0.161817, 0.0425,0.166337, 0.0450,0.170702, 0.0500,0.179015, 0.0600,0.194248, 0.0700,0.207999, 0.0800,0.220581, 0.0900,0.232212, 0.1000,0.243050, 0.2000,0.325071, 0.3000,0.382016, 0.4000,0.426452, 0.5000,0.463159, 1.5000,0.668992, 2.5000,0.775976, 3.5000,0.849298, 4.5000,0.905305, 10.000,1.088998, 20.000,1.253794, 30.000,1.351794, 40.000,1.421860, 50.000,1.476457, 150.00,1.747485, 250.00,1.874323, 350.00,1.958037, 450.00,2.020630, 1000.0,2.219765, 2000.0,2.392826, 3000.0,2.494109, 4000.0,2.565985, 5000.0,2.621743), 2)) # Interpolation: if (x < 0.0005) { y = gamma*sqrt(2*x) } if (x >= 0.0005 & x <= 5000) { y = spline(log(vqn[,1]), vqn[,2], n = 1, xmin = log(x), xmax = log(x))$y } if (x > 5000) { y = 0.25*log(x) + 0.49088} # Return Value: y } # Internal Function - NEGATIVE CASE: mu < 0 # Right Table from Appendix B: QN = function(x) { gamma = sqrt(pi/8) vqn = t(matrix(c( 0.0005,0.019965, 0.0010,0.028394, 0.0015,0.034874, 0.0020,0.040369, 0.0025,0.045256, 0.0050,0.064633, 0.0075,0.079746, 0.0100,0.092708, 0.0125,0.104259, 0.0150,0.114814, 0.0175,0.124608, 0.0200,0.133772, 0.0225,0.142429, 0.0250,0.150739, 0.0275,0.158565, 0.0300,0.166229, 0.0325,0.173756, 0.0350,0.180793, 0.0375,0.187739, 0.0400,0.194489, 0.0425,0.201094, 0.0450,0.207572, 0.0475,0.213877, 0.0500,0.220056, 0.0550,0.231797, 0.0600,0.243374, 0.0650,0.254585, 0.0700,0.265472, 0.0750,0.276070, 0.0800,0.286406, 0.0850,0.296507, 0.0900,0.306393, 0.0950,0.316066, 0.1000,0.325586, 0.1500,0.413136, 0.2000,0.491599, 0.2500,0.564333, 0.3000,0.633007, 0.3500,0.698849, 0.4000,0.762455, 0.5000,0.884593, 1.0000,1.445520, 1.5000,1.970740, 2.0000,2.483960, 2.5000,2.990940, 3.0000,3.492520, 3.5000,3.995190, 4.0000,4.492380, 4.5000,4.990430, 5.0000,5.498820), 2)) # Interpolation: if (x < 0.0005) { y = gamma*sqrt(2*x) } if (x >= 0.0005 & x <= 5) { y = spline(vqn[,1], vqn[,2], n = 1, xmin = x, xmax = x)$y } if (x > 5) { y = x + 1/2 } # Return Value: y } # Result: ED = NULL for (i in 1:length(mu)) { if (mu[i] == 0) { gamma = sqrt(pi/8) ED[i] = 2 * gamma * sigma * sqrt(horizon) } else { x = mu[i]^2 * horizon / ( 2 * sigma^2 ) if (mu[i] > 0) { ED[i] = ( 2* sigma^2 / mu[i] ) * QP(x) } if (mu[i] < 0) { ED[i] = - ( 2* sigma^2 / mu[i] ) * QN(x) } } } # Return Value: ED } ################################################################################ dmaxdd <- function(x, sd = 1, horizon = 100, N = 1000) { # Description: # Calculates for a trendless Brownian process (mean=0) and # standard deviation "sd" the density from the probability # that the maximum drawdown "D" is larger or equal to "h" # in the interval [0,T], where "T" denotes the time horizon # of the investor. # Arguments: # x - Vector of Drawdowns # sd - Standard Deviation # horizon - Time horizon of the investor # N - number of summands # Notes: # The drift dependent case is not yet implemented! # FUNCTION: # Use "h", "sigma" to be conform with Magdon-Ismael et al. [2003] h = x sigma = sd # Settings: n = 1:N pn = 2 * sin((n-0.5)*pi) * sigma^2 * (n-0.5) * pi * horizon en = sigma^2 * (n-0.5)^2 * pi^2 * horizon / 2 # Loop over all Drawdowns: result = rep(NA, times = length(h)) for (i in 1:length(h)) { if (h[i] == 0) { result[i] = 0 } else { g = pn * exp(-en/(h[i]^2)) / h[i]^3 result[i] = sum(g) } } # Return Value: result } # ------------------------------------------------------------------------------ pmaxdd <- function(q, sd = 1, horizon = 100, N = 1000) { # Description: # Calculates for a trendless Brownian process (mean=0) # with standard deviation "sd" the probability that the # maximum drawdown "D" is larger or equal to "h" in the # interval [0,T], where "T" denotes the time horizon of # the investor. # Arguments: # q - Vector of Drawdowns # sd - Standard Deviation # horizon - Time horizon of the investor # N - number of summands # Value: # GD(h) - eqn(14) In Magdon-Ismail et al. # Notes: # The drift dependent case is not yet implemented! # FUNCTION: # Use "h", "sigma" to be conform with Magdon-Ismael et al. [2003] h = q sigma = sd # Settings: n = 1:N pn = 2 * ( sin((n-0.5)*pi) / ((n-0.5)*pi) ) en = sigma^2 * (n-0.5)^2 * pi^2 * horizon / 2 # Loop over all Drawdowns: result = rep(NA, times = length(h)) for (i in 1:length(h)) { g = pn * ( 1 - exp(-en/(h[i]^2)) ) result[i] = sum(g) } # Return Value: result } # ------------------------------------------------------------------------------ rmaxdd <- function(n, mean = 0, sd = 1, horizon = 100) { # Description: # Generates for a Brownian process with mean "mean" and # standard deviation "sd" random variates of maximum # Drawdowns. # Arguments: # n - Number of Drawdown rvs # mean - Drift # sd - Standard Deviation # horizon - Time horizon of the investor # FUNCTION: # Simulation of "n" max Drawdowns "h": result = NULL for (i in 1:n) { D = cumsum(rnorm(horizon, mean = mean, sd = sd)) result[i] = max(cummax(D)-D) } # Return Value: result } ################################################################################ fBasics/R/builtin-ssdenGss.R0000644000176200001440000000173014263246022015407 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ############################################################################### ##.ssden <- gss::ssden ##.pssden <- gss::pssden ##.qssden <- gss::qssden ##.dssden <- gss::dssden ############################################################################### fBasics/R/dist-gldMode.R0000644000176200001440000000364214263246022014472 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # gldMode Computes the Generalized Lambda Distribution mode ################################################################################ gldMode <- function(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) { # A function implemented by Diethelm Wuertz # Arguments: # lambda1 - a numeric value, the location parameter # lambda2 - a numeric value, the scale parameter # lambda3 - a numeric value, the first shape parameter # lambda4 - a numeric value, the second parameter # Description: # Computes the Generalized Lambda Distribution mode # FUNCTION: # Find Maximum: min = qgld(0.01, lambda1, lambda2, lambda3, lambda4) max = qgld(0.99, lambda1, lambda2, lambda3, lambda4) ans = optimize(f = dgld, interval = c(min, max), lambda1 = lambda1, lambda2 = lambda2, lambda3 = lambda3, lambda4 = lambda4, maximum = TRUE, tol = .Machine$double.eps)$maximum # Return Value: ans } ################################################################################ fBasics/R/dist-ghtFit.R0000644000176200001440000000710214324531715014343 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # ghtFit Fits parameters of a generalized hyperbolic density ################################################################################ ghtFit <- function(x, beta = 0.1, delta = 1, mu = 0, nu = 10, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Fits parameters of a generalized hyperbolic density # Example: # x = rght(10000); ghtFit(x, 0.2, 0.8, mean(x), 8) # FUNCTION: # Transform: x.orig = x x = as.vector(x) if (scale) { SD = sd(x) x = x / SD } # Settings: CALL = match.call() # Log-likelihood Function: obj <- function(x, y = x, trace){ if (NA %in% x) return(1e99) ## if (abs(x[2]) >= x[1]) return(1e99) f = -sum(log(dght(y, x[1], x[2], x[3], x[4]))) # Print Iteration Path: if (trace) { cat("\n Objective Function Value: ", -f) cat("\n Parameter Estimates: ", x, "\n") } f } # Variable Transformation and Minimization: eps = 1e-10 BIG = 1000 f = obj(x = c(beta, delta, mu, nu), y = x, trace = FALSE) r = nlminb(start = c(beta, delta, mu, nu), objective = obj, lower = c(-BIG, eps, -BIG, -BIG), upper = BIG, y = x, trace = trace) names(r$par) <- c("beta", "delta", "mu", "nu") # Result: if (scale) { r$par = r$par / c(SD, 1/SD, 1/SD, 1) r$objective = obj(r$par, y = as.vector(x.orig), trace = trace) } # Optional Plot: if (doplot) { x = as.vector(x.orig) if (span == "auto") span = seq(min(x), max(x), length = 51) z = density(x, n = 100, ...) x = z$x[z$y > 0] y = z$y[z$y > 0] y.points = dght(span, r$par[1], r$par[2], r$par[3], r$par[4]) ylim = log(c(min(y.points), max(y.points))) plot(x, log(y), xlim = c(span[1], span[length(span)]), ylim = ylim, type = "p", xlab = "x", ylab = "log f(x)", ...) title("GHT Parameter Estimation") lines(x = span, y = log(y.points), col = "steelblue") } # Add Title and Description: if (is.null(title)) title = "Generalized Hyperbolic Parameter Estimation" if (is.null(description)) description = "" # Fit: fit = list( estimate = r$par, minimum = -r$objective, code = r$convergence) # Return Value: new("fDISTFIT", call = as.call(CALL), model = "Generalized Hyperbolic Distribution", data = as.data.frame(x.orig), fit = fit, title = as.character(title), description = description ) } ################################################################################ fBasics/R/utils-listIndex.R0000644000176200001440000000305014263246022015250 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # listIndex Extracts R package index ################################################################################ listIndex <- function(package, character.only = FALSE) { ## A function implemented by Diethelm Wuertz & Yohan Chalabi ## Extracts R package index ## Example: ## listIndex("timeSeries") ### --> ../man/utils-listIndex.Rd ## ~~~~~~~~~~~~~~~~~~ if (!character.only) package <- as.character(substitute(package)) ans <- library(help = package, character.only=TRUE) index <- ans$info[[2]] cat("\n", package, "Index:\n\n") cat(paste(" ", index), sep = "\n") } ################################################################################ fBasics/R/stats-rowStats.R0000644000176200001440000000641314263246022015137 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # rowStats Computes sample statistics by row # rowSums Computes sums of values in each row # rowMeans Computes means of values in each row # rowSds Computes standard deviation of each row # rowVars Computes sample variance by row # rowSkewness Computes sample skewness by row # rowKurtosis Computes sample kurtosis by row # rowMaxs Computes maximum values in each row # rowMins Computes minimum values in each row # rowProds Computes product of values in each row # rowQuantiles Computes product of values in each row # FUNCTION: NO LONGER USED: # rowAvgs Computes sample mean by row # rowStdevs Computes sample variance by row ################################################################################ rowStats <- function(x, FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes sample statistics by row # FUNCTION: # Statistics: apply(na.omit(as.matrix(x), ...), 1, FUN, ...) } # ------------------------------------------------------------------------------ # rowSums() # is part of R's base package # ------------------------------------------------------------------------------ # rowMeans <- # is part of R's base package # ------------------------------------------------------------------------------ rowSds <- function(x, ...) { rowStats(x, "sd", ...) } rowVars <- function(x, ...) { rowStats(x, "var", ...) } rowSkewness <- function(x, ...) { rowStats(x, "skewness", ...) } rowKurtosis <- function(x, ...) { rowStats(x, "kurtosis", ...) } rowMaxs <- function(x, ...) { rowStats(x, "max", ...) } rowMins <- function(x, ...) { rowStats(x, "min", ...) } rowProds <- function(x, ...) { rowStats(x, "prod", ...) } # ------------------------------------------------------------------------------ rowQuantiles <- function(x, prob = 0.05, ...) { # A function implemented by Diethelm Wuertz # FUNCTION: # Row Quantiles: stopifnot(length(prob) == 1) rowStats(x, "quantile", probs = prob, ...) } ################################################################################ rowAvgs <- function(x, ...) rowMeans(x, ...) rowStdevs <- rowSds ################################################################################ fBasics/R/test-jbTable.R0000644000176200001440000001457314263246022014503 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # .jbTable Finite sample p values for the Jarque Bera test # .jbPlot Plots probabilities # .pjb Returns probabilities for JB given quantiles # .qjb Returns quantiles for JB given probabilities ################################################################################ .jbTable <- function(type = c("LM", "ALM"), size = c("mini", "small", "all")) { # A function implemented by Diethelm Wuertz # Description: # Finite sample p values for the Jarque Bera test # Details: # The function jbTable() returns a data.frame with rows denoting # size and columns denoting probabilities 0 < p < 1. # Note: # x=-3:0; y=0:3; z=outer(x,y,"*"); rownames(z)=x; colnames(z)=y; z # Example: # .jbTable() # FUNCTION: # Check Arguments: type = match.arg(type) size = match.arg(size) # Create Table: if (type == "LM") { Table = t(.jbLM()) } else if (type == "ALM") { Table = t(.jbALM()) } # Select Size: if (size == "small") { Table = t(Table) n = dim(Table)[1] Table = Table[c(matrix(1:(n-2), byrow = TRUE, ncol = 22)[, 1], n), ] Table = Table[-(1:17),] Table = t(Table) } else if (size == "mini") { p = c( "0.01", "0.0251189", "0.05", "0.1", "0.9", "0.95", "0.9751687", "0.99") Table = Table[, p] Table = rbind(Table, qchisq(as.numeric(p), df = 2)) Table = round(Table, 3) colnames(Table) = substr(colnames(Table), 1, 5) nRows = dim(Table)[1] rownames(Table)[nRows] = "Inf" } ans = list( x = as.numeric(rownames(Table)), y = as.numeric(colnames(Table)), z = Table) # Add Control: attr(ans, "control") <- c(table = "jb", type = type, size = size) # Return Value: class(ans) = "gridData" ans } # ------------------------------------------------------------------------------ .jbPlot <- function(type = c("LM", "ALM")) { # A function implemented by Diethelm Wuertz # Match Arguments: type = match.arg(type) # Load Table: Y = .jbTable(size = "small") X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = X[, 1] # N y = X[, 3] # q-Stat z = X[, 2] # p-Value # Interpolate: ans = akimaInterp(log(x), log(y), z, gridPoints = 101, extrap = FALSE) persp(ans, theta = 40, xlab = "log(N)", ylab = "log(q)", zlab = "p", main = paste(type, "Jarque Bera")) # Return Value: invisible(NULL) } # ------------------------------------------------------------------------------ .pjb <- function(q, N = Inf, type = c("LM", "ALM")) { # A function implemented by Diethelm Wuertz # Description: # Returns probabilities for the JB Test given quantiles # Arguments: # q = quantiles (0.020 < q < 12) # N = sample sizes # Details: # Uses "small" table for interpolation. # Example: # q=c(0.03,1,5,9); for (N in c(4,1000,50000,Inf)) print(.pjb(q, N)) # FUNCTION: # Match Arguments: type = match.arg(type) # Check N: stopifnot(length(N) == 1) # Grid Points: Y = .jbTable(type = type, size = "small") if (N < 10000) { # Interpolate on log-Scale: X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = log(X[, 1]) # N y = log(X[, 3]) # q-Stat z = X[, 2] # p-Value # Out Points: xo = rep(log(N), times = length(q)) yo = log(q) # Interpolate: p = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3] } else if (N > 10000) { p = pchisq(as.numeric(q), df = 2) } # Result: if (N < 5) controlN = "< 5" if (N >= 5 & N <= 10000) controlN = as.character(N) if (N > 10000) controlN = "> 10000" if (!is.finite(N)) controlN = "Inf" names(p) = as.character(q) attr(p, "control") <- c(N = controlN) # Return Value: p } # ------------------------------------------------------------------------------ .qjb <- function(p, N = Inf, type = c("LM", "ALM")) { # A function implemented by Diethelm Wuertz # Description: # Returns quantiles for the ADF Test given probabilities # Arguments: # p = probabilities (0.01 < p < 0.99) # N = sample sizes # Details: # Uses "small" table for interpolation. # Example: # p=(1:9)/10; for (N in c(4,1000,50000,Inf)) print(.qjb(p, N)) # FUNCTION: # Match Arguments: type = match.arg(type) statistic = match.arg(statistic) # Check N: stopifnot(length(N) == 1) # Grid Points: Y = .jbTable(type = type, size = "small") if (N < 10000) { # Interpolate on log-Scale: X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z)) x = log(X[, 1]) # N y = X[, 2] # p-Value z = X[, 3] # q-Stat # Out Points: xo = rep(log(N), times = length(p)) yo = p # Interpolate - Scaling Required: xStd = sqrt(var(x)) q = linearInterpp(x/xStd, y, z, xo = xo/xStd, yo = yo)[, 3] } else { # Asymptotic Limit: q = qchisq(as.numeric(p), df = 2) } # Result: if (N < 5) controlN = "< 5" if (N >= 5 & N <= 10000) controlN = as.character(N) if (N > 10000) controlN = "> 10000" if (!is.finite(N)) controlN = "Inf" names(q) = as.character(p) attr(q, "control") <- c(N = controlN) # Return Value: q } ################################################################################ fBasics/R/test-fHTEST.R0000644000176200001440000001055214324451102014161 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # 'fHTEST' S4 Class Representation # show.fHTEST S4 Print Method ################################################################################ setClass("fHTEST", representation( call = "call", data = "list", test = "list", title = "character", description = "character") ) # ------------------------------------------------------------------------------ setMethod("show", "fHTEST", function(object) { # A function implemented by Diethelm Wuertz # Source: # This function copies code from base:print.htest # FUNCTION: # Unlike print the argument for show is 'object'. x = object # Title: cat("\nTitle:\n ", x@title, "\n", sep = "") # Call: # cat("\nCall:\n", deparse(x@call), "\n", sep = "") # Data Name: # cat("\nData Name:\n", ans@data.name, "\n", sep = "") # Test Results: test = x@test cat("\nTest Results:\n", sep = "") # Tests from tseries package: # Parameter: if (!is.null(test$parameter)) { parameter = test$parameter Names = names(parameter) cat(" PARAMETER:\n") for ( i in 1: length(Names) ) cat(paste(" ", names(parameter[i]), ": ", format(round(parameter[i], 3)), "\n", sep = "") ) } # Sample Estimates: if (!is.null(test$estimate)) { estimate = test$estimate Names = names(estimate) cat(" SAMPLE ESTIMATES:\n") for (i in 1:length(Names)) { cat(paste(" ", Names[i], ": ", round(estimate[i], digits = 4), "\n", sep = "" ) ) } } # Statistic: if (!is.null(test$statistic)) { statistic = test$statistic Names = names(statistic) cat(" STATISTIC:\n") for (i in 1:length(Names)) { if (!is.na(statistic[i])) { cat(paste(" ", Names[i], ": ", round(statistic[i], digits = 4), "\n", sep = "" ) ) } } } # P-Value: if (!is.null(test$p.value)) { pval = test$p.value Names = names(pval) if (Names[1] == "") space = "" else space = ": " cat(" P VALUE:\n") for (i in 1:length(Names)) { if (!is.na(pval[i])) { if (!inherits(version, "Sversion")) { cat(paste(" ", Names[i], space, format.pval(pval[i], digits = 4), " \n", sep = "" ) ) } else { cat(paste(" ", Names[i], space, round(pval[i], digits = 4), " \n", sep = "" ) ) } } } } # Confidence Interval: if (!is.null(test$conf.int)) { conf = test$conf.int # For SPlus compatibility use dimnames istead of colnames! colNames = dimnames(conf)[[2]] cat(" CONFIDENCE INTERVAL:\n") for (i in 1:length(colNames)) { cat(paste(" ", colNames[i], ": ", round(conf[1, i], digits = 4), ", ", round(conf[2, i], digits = 4), "\n", sep = "" ) ) } } # More Specific Output Results: if (!is.null(test$output)) { cat(test$output, fill = FALSE, sep = "\n") } ## Description: ## GNB: don't print description if it is "" if(!identical(x@description, "")) { cat("\nDescription:\n ", x@description, sep = "") } cat("\n") # Return Value: # invisible() # made visible by DW }) ################################################################################ fBasics/R/plot-scalinglawPlot.R0000644000176200001440000000734414263246022016120 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # scalinglawPlot Evaluates and displays scaling law behavior ################################################################################ scalinglawPlot <- function(x, span = ceiling(log(length(x)/252)/log(2)), doplot = TRUE, labels = TRUE, trace = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Investigates the scaling law. # The input "x" requires log-returns. # Arguments: # x - an uni- or multivariate return series of class 'timeSeries' # or any other object which can be transformed by the function # 'as.timeSeries()' into an object of class 'timeSeries'. # labels - a logical flag, by default true. Should a default # main title and labels addet to the plot? # FUNCTION: # Settings: if (!is.timeSeries(x)) x = as.timeSeries(x) Units = colnames(x) # Labels: if (labels) { main = "Scaling Law Plot" xlab = "log-time" ylab = "log-volatility" } else { main = xlab = ylab = "" } X = x DIM = dim(X)[2] Intercept = Exponent = InverseExponent = NULL for (i in 1:DIM) { # Get Data: x = as.vector(as.matrix(X)[, i]) if (labels) main = Units[i] # Settings: logtimesteps = span xmean = mean(x) # x have to be logarithmic returns y = (x-xmean) logprices = cumsum(y) # Scaling Power Low: scale = function(nx, logprices) { sum(abs(diff(logprices, lag = (2^nx))))} nx = 0:logtimesteps; x = nx*log(2) y = log(apply(matrix(nx), 1, scale, logprices)) # fit = lsfit(x, y)$coefficients # Runs in both environments, R and SPlus: fit = lsfit(x, y) # Robust Fit: # fit = l1fit(x, y) # Fit Result: Fit = unlist(fit)[1:2] alpha = 1.0/Fit[2] if (doplot) { plot(x, y, main = main, xlab = xlab, ylab = ylab, ...) abline(Fit[1], Fit[2]) abline(Fit[1], 0.5, col = "steelblue") } if (labels) grid() # Trace: if (trace) { cat ("\nScaling Law: ", Units[i]) cat ("\n Plot Intercept ", fit$coefficients[1]) cat ("\n Plot Slope ", fit$coefficients[2]) cat ("\n Plot Inverse Slope ", 1/fit$coefficients[2]) cat ("\n\n") } Intercept = c(Intercept, fit$coefficients[1]) Exponent = c(Exponent, fit$coefficients[2]) InverseExponent = c(InverseExponent, 1/fit$coefficients[2]) } names(Intercept) = Units names(Exponent) = Units names(InverseExponent) = Units result = list( Intercept = Intercept, Exponent = Exponent, InverseExponent = InverseExponent) # Return Value: invisible(result) } ################################################################################ fBasics/R/matrix-inv.R0000644000176200001440000000247014263246022014252 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # LINEAR ALGEBRA: DESCRIPTION: # inv Returns the inverse of a matrix ################################################################################ inv <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns the inverse of a matrix # FUNCTION: # Inverse: # ans = chol2inv(chol(x)) # or ... ans = base::solve(x) # Return Value: ans } ################################################################################ fBasics/R/dist-ssd.R0000644000176200001440000000526414263246022013712 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTIONS: DESCRIPTION: # dssd Returns spline smoothed density estimate # pssd Returns spline smoothed probability estimate # qssd Returns spline smoothed quantiles estimate # rssd Returns spline smoothed random variates # LIBRARY: DESCRPTION: # stabledist Stable Duistribution ################################################################################ dssd <- function(x, param, log = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Evaluate density using smoothing spline ANOVA model # FUNCTION: # Density: ans <- gss::dssden(object = param, x = x) # Log: if(log) ans <- log(ans) # Return Value: ans } # ------------------------------------------------------------------------------ pssd <- function(q, param) { # A function implemented by Diethelm Wuertz # Description: # Evaluate probability using smoothing spline ANOVA model # FUNCTION: # Return Value: gss::pssden(object = param, q = q) } # ------------------------------------------------------------------------------ qssd <- function(p, param) { # A function implemented by Diethelm Wuertz # Description: # Evaluate quantiles using smoothing spline ANOVA model # FUNCTION: # Return Value: gss::qssden(object = param, p = p) } # ------------------------------------------------------------------------------ rssd <- function(n, param) { # A function implemented by Diethelm Wuertz # Description: # Generate random deviates using smoothing spline ANOVA model # FUNCTION: # Return Value: gss::qssden(object = param, p = runif(n)) } ################################################################################ fBasics/R/00fBasics-Package.R0000644000176200001440000000144514263246022015220 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ############################################################################### fBasics/R/test-locationTest.R0000644000176200001440000001667114324452730015614 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: LOCATION TESTS: # locationTest Performs locations tests on two samples # .tTest Unpaired t test for differences in mean # .kw2Test Kruskal-Wallis test for differences in locations ################################################################################ locationTest <- function(x, y, method = c("t", "kw2"), title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Correlation Tests # FUNCTION: # Test: method = match.arg(method) if (method == "t") { ans = .tTest(x, y, title = title, description = description) } if (method == "kw2") { ans = .kw2Test(x, y, title = title, description = description) } # Return Value: ans } # ------------------------------------------------------------------------------ .tTest <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Tests if two population means are equal. # Arguments: # x, y - two numeric vector of data values or time series objects # description - a brief description of the porject of type character. # title - a character string which allows for a project title. # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Asymptotic Test: two.sided = t.test(x = x, y = y, alternative = "two.sided", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) less = t.test(x = x, y = y, alternative = "less", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) greater = t.test(x = x, y = y, alternative = "greater", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) # Assume Equal Variances: two.sided.equal = t.test(x = x, y = y, alternative = "two.sided", mu = 0, paired = FALSE, var.equal = TRUE, conf.level = 0.95) less.equal = t.test(x = x, y = y, alternative = "less", mu = 0, paired = FALSE, var.equal = TRUE, conf.level = 0.95) greater.equal = t.test(x = x, y = y, alternative = "greater", mu = 0, paired = FALSE, var.equal = TRUE, conf.level = 0.95) # Sample Estimates: PARAMETER = c(length(x), length(y), 0) names(PARAMETER) = c( "x Observations", "y Observations", "mu") test$parameter = PARAMETER # Sample Estimates: ESTIMATE = c(two.sided$estimate, var(x), var(y)) names(ESTIMATE) = c("Mean of x", "Mean of y", "Var of x", "Var of y") test$estimate = ESTIMATE # P Values: PVAL = c( two.sided$p.value, less$p.value, greater$p.value, two.sided.equal$p.value, less.equal$p.value, greater.equal$p.value) names(PVAL) = c( "Alternative Two-Sided", "Alternative Less", "Alternative Greater", "Alternative Two-Sided | Equal Var", "Alternative Less | Equal Var", "Alternative Greater | Equal Var") test$p.value = PVAL # Statistic: STATISTIC = c( two.sided$statistic, two.sided.equal$statistic) names(STATISTIC) = c( " T", "T | Equal Var") test$statistic = STATISTIC # Confidence Intervals: CONF.INT = cbind( a = two.sided$conf.int, b = less$conf.int, c = greater$conf.int, d = two.sided.equal$conf.int, e = less.equal$conf.int, f = greater.equal$conf.int) # For Splus compatibility use named a CONF.INT # and dimnames instead of colnames! dimnames(CONF.INT)[[2]] = c( "Two-Sided", " Less", " Greater", "Two-Sided | Equal Var", " Less | Equal Var", " Greater | Equal Var") test$conf.int = CONF.INT # Add: if (is.null(title)) title = "t Test" if (is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } # ------------------------------------------------------------------------------ .kw2Test <- function(x, y, title = NULL, description = NULL) { # A function implemented by Diethelm Wuertz # Description: # Performs a Kruskal-Wallis rank sum test of the null that # the location parameters of the distribution of x are the # same in each group (sample). The alternative is that they # differ in at least one. # Arguments: # x, y - two numeric vector of data values or time series objects # description - a brief description of the porject of type character. # title - a character string which allows for a project title. # Note: # A function linked to "stats" # FUNCTION: # Call: call = match.call() # Test: test = list() # Data Set Name: DNAME = paste(deparse(substitute(x)), "and", deparse(substitute(y))) test$data.name = DNAME # Convert Type: x = as.vector(x) y = as.vector(y) # Sample Estimates: ESTIMATE = c(mean(x), mean(y), var(x), var(y)) names(ESTIMATE) = c("Mean of x", "Mean of y", "Var of x", "Var of y") test$estimate = ESTIMATE # Parameter: PARAMETER = c(length(x), length(y)) names(PARAMETER) = c( "x Observations", "y Observations") test$parameter = PARAMETER # Operate on Lists: x = list(x = x, y = y) if (length(x) < 2) stop("x must be a list with at least 2 elements") k = length(x) l = sapply(x, "length") g = factor(rep(1 : k, l)) x = unlist(x) # Test: n = length(x) if (n < 2) stop("not enough observations") r = rank(x) TIES = table(x) # Statistic: STATISTIC = sum(tapply(r, g, "sum")^2 / tapply(r, g, "length")) STATISTIC = ((12 * STATISTIC / (n * (n + 1)) - 3 * (n + 1)) / (1 - sum(TIES^3 - TIES) / (n^3 - n))) names(STATISTIC) = "KW chi-squared" test$statistic = STATISTIC # P Value: PVAL = 1 - pchisq(STATISTIC, 1) names(PVAL) = "" test$p.value = PVAL # Add: if(is.null(title)) title = "Kruskal-Wallis Two Sample Test" if(is.null(description)) description = "" # Return Value: new("fHTEST", call = call, data = list(x = x, y = y), test = test, title = as.character(title), description = as.character(description) ) } ################################################################################ fBasics/R/utils-printControl.R0000644000176200001440000000344614650724111016013 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: CONTROL ATTRIBUTES: # print.control Prints unlisted control attributes # FUNCTION: DESCRIPTION: # .print Used in regression package # (2023-10-21) GNB: not ################################################################################ print.control <- function(x, ...) { # Return Value: print(unlist(x)) } ################################################################################ # ------------------------------------------------------------------------------ .plot <- function(x, ...) { UseMethod(".plot") } # ------------------------------------------------------------------------------ .summary <- function(object, ...) { UseMethod(".summary") } # ------------------------------------------------------------------------------ .predict <- function(object, ...) { UseMethod(".predict") } ################################################################################ fBasics/R/dist-ssdFit.R0000644000176200001440000000270614650724111014353 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTIONS: DESCRIPTION # ssdFit Estimate probability densities using smoothing spline ANOVA ################################################################################ ssdFit <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Estimate probability densities using smoothing spline ANOVA # models with cubic spline, linear spline, or thin-plate spline # marginals for numerical variables. # FUNCTION: # Parameter Fit: ans <- gss::ssden(~x) # Return Value: ans } ################################################################################ fBasics/R/test-jbLM.R0000644000176200001440000174264114263246022013771 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # DATA: DESCRIPTION: # .jbLM Jarque-Bera Lagrange Multiplier Data ################################################################################ .jbLM <- function() { structure(list("5" = c(0.052470165, 0.052479988, 0.052481227, 0.052482714, 0.052484752, 0.0524849, 0.052487004, 0.052487547, 0.052492897, 0.052493878, 0.052493879, 0.052494842, 0.052496092, 0.052497834, 0.052499018, 0.052505397, 0.052509442, 0.052511664, 0.052515321, 0.052517838, 0.052529162, 0.05253247, 0.052537375, 0.05254833, 0.052553431, 0.052558648, 0.052559815, 0.052561665, 0.052570098, 0.052574164, 0.05258048, 0.05258408, 0.052586498, 0.052588564, 0.052593144, 0.052598993, 0.052600907, 0.052607888, 0.05260833, 0.05261147, 0.052611702, 0.052613837, 0.052620487, 0.052628488, 0.052631379, 0.052633052, 0.05263496, 0.052635328, 0.052641592, 0.052645432, 0.052651572, 0.052656332, 0.052660683, 0.052665999, 0.052673242, 0.052676104, 0.052688929, 0.052694803, 0.052699646, 0.052701458, 0.052709881, 0.052712268, 0.05272215, 0.052727874, 0.052730256, 0.052735346, 0.052743604, 0.05274666, 0.052760922, 0.052767973, 0.052775119, 0.052778247, 0.052781513, 0.052786578, 0.052791093, 0.052803672, 0.052807291, 0.052821458, 0.052823871, 0.052828785, 0.052833725, 0.052840874, 0.052843109, 0.052847147, 0.052853554, 0.052854431, 0.052861028, 0.052868051, 0.052877535, 0.052886001, 0.052888722, 0.052898288, 0.052898694, 0.052908141, 0.052910534, 0.052914546, 0.052923779, 0.052927527, 0.052935115, 0.052937428, 0.052942354, 0.052949011, 0.052953643, 0.052955157, 0.052959405, 0.052966149, 0.052973704, 0.052979684, 0.052991848, 0.053004827, 0.053010343, 0.053013686, 0.053025845, 0.053033555, 0.053044444, 0.053058405, 0.053066997, 0.053073765, 0.053081806, 0.053088497, 0.053090763, 0.053108566, 0.053114103, 0.053122934, 0.053128147, 0.053146206, 0.053152252, 0.053164623, 0.05316833, 0.053177206, 0.053182248, 0.05319418, 0.053199323, 0.053207293, 0.053213266, 0.053217012, 0.053225175, 0.053229997, 0.053235579, 0.053242451, 0.05324788, 0.05326003, 0.053268582, 0.053276668, 0.053286, 0.053297456, 0.053302415, 0.053308875, 0.053318566, 0.053325458, 0.053328303, 0.053336053, 0.053344035, 0.053358689, 0.053366123, 0.053379, 0.053385515, 0.053401057, 0.053409904, 0.053419693, 0.05342981, 0.053439077, 0.053447854, 0.053455487, 0.053471333, 0.053484097, 0.053494267, 0.053504441, 0.053516544, 0.053536177, 0.053548321, 0.05356068, 0.053567346, 0.053579452, 0.053587055, 0.053599905, 0.053615242, 0.053631305, 0.053642128, 0.053656104, 0.053669132, 0.053682175, 0.053698071, 0.053707687, 0.053716235, 0.053726155, 0.053745809, 0.053755976, 0.05377043, 0.053780103, 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"0.999985", "0.9999852", "0.9999854", "0.9999855", "0.9999857", "0.9999859", "0.999986", "0.9999862", "0.9999864", "0.9999865", "0.9999867", "0.9999868", "0.999987", "0.9999871", "0.9999873", "0.9999874", "0.9999876", "0.9999877", "0.9999878", "0.999988", "0.9999881", "0.9999883", "0.9999884", "0.9999885", "0.9999886", "0.9999888", "0.9999889", "0.999989", "0.9999892", "0.9999893", "0.9999894", "0.9999895", "0.9999896", "0.9999898", "0.9999899", "0.99999"), class = "data.frame") } ################################################################################ fBasics/R/utils-colorLocator.R0000644000176200001440000000631414263246022015755 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: COLOR NAMES: # colorLocator Plots R's 657 named colors for selection # colorMatrix Returns matrix of R's color names ################################################################################ ################################################################################ # Package: epitools # Version: 0.4-9 # Date: 2007-06-27 # Title: Epidemiology Tools # Author: Tomas Aragon # Maintainer: Tomas Aragon # Depends: R (>= 2.1.0) # Description: EpiTools: R Package for Epidemiologic Data and Graphics # License: GPL version 2 or newer # URL: http://www.epitools.net # Packaged: Wed Jun 27 20:27:33 2007; Tomas colorLocator <- function(locator = FALSE, cex.axis = 0.7) { # A function implemented by Diethelm Wuertz # Description: # Plots R's 657 named colors for selection # Source: # Code borrowed from contributed R-package epitools # FUNCTION: # Plot: xx <- rep(1:30, 22) yy <- rep(1:22, rep(30, 22)) yyy <- matrix(yy, ncol = 22) cm <- colorMatrix() matplot(xx[1:30], yyy, pch = 15, type = "n", axes = FALSE, xlab = "colorMatrix[row, ]", ylab = "colorMatrix[ , col]", main = "Identify Color Names.") ## title(sub = "Source: www.epitools.net", cex.sub = 0.7) points(xx, yy, type = "p", pch = 15, cex = 2, col = c(colors(), NA, NA, NA)) axis(1, at = c(0:30 + 0.5), labels = FALSE, tick = TRUE) axis(1, at = 1:30, labels = 1:30, cex.axis = cex.axis, tick = FALSE) axis(2, at = c(0:22 + 0.5), labels = FALSE, tick = TRUE) axis(2, at = 1:22, labels = 1:22, cex.axis = cex.axis, tick = FALSE, las = 1) # Locator: if (locator) { lxy <- locator() xy <- round(data.frame(lxy)) xym <- as.matrix(xy) ## return located := data.frame(xy, color.names = cm[xym]) } else { invisible(cm) } } # ------------------------------------------------------------------------------ colorMatrix <- function() { # A function implemented by Diethelm Wuertz # Description: # Returns matrix of R's color names # Source: # Code borrowed from contributed R-package epitools # FUNCTION: # Color Names: x <- matrix(c(colors(), NA, NA, NA), 30, 22) # Return Value: invisible(x) } ################################################################################ fBasics/data/0000755000176200001440000000000014660437567012557 5ustar liggesusersfBasics/data/HedgeFund.rda0000644000176200001440000000172514660437567015105 0ustar liggesusers‹eUMLSA~”–ŸÊ¯¢QxÕ‹!Žd·ÐZ U£ —ÏòbmË£%¯röâE^ôâ…«zÚb4š ñß%€òSþ¬nß|ÛG`“v;;³3ß|3³ ûÏðžójšæÒÜù],º]ò«Hskår/o×bzk&1 µUòÀ?ëï÷Ìnä×M6=Õ6^7:¥ÎÙ$ôO½M©Ûo¼l⤽Øì-yØ”ºÃÆófõ÷ÅühÞÁ^k‹a‘«³¬ºùõÒª·,·í®‰­]}x­÷›'=wѹøLþX^{tõÛDüuºÏ~ôÚ™Âù ö.Ø‹k—×\³ ÅàT~rˆ³Œ|'ÞØ ±„|WóÞzæÙ$Åï(_1‡üo!ÞGÂ/žÀîðåȯUJþ,7ñb• ^¸þ‘?±Aù ä¥î‰¯¤·Š`·@þÅ3àY¤¼ÄxÖˆw^Aø,åeUSÞÖÊË? õawQÏEÍ^b õ¼¾f /ûdÓ6Ä>ŒÙÙ ì_C?Cx¬jðñxêÉž¦û&š·Æf—Š–{½ÃO<"bL…Gü‡E䯨]ÿI´u7ŠØeÍY"~°n4öFÄÏzœç‰+2ìДèør^® "~8‰‰úY‰$RËgd`\$ÈK~vÜ7‰Ô1iÂ#bË‘ ¹ˆÔŠg‰Ôz<·–¿³ 8éðL×9ÄEg<퇶ã ìqÄwB»üm·ç]oÛu“þÔ·½qÛûŸÙ >ÿëóçúD¬˜C\l>)…þÈZœ[_ÊèÞ«": ‰;xž8¹ÛY"ÑǺ¤Q—”‰¼± °JÄž#ÇYÔ#ñzSmÔ :,ö%9 ~©&ä³Úпô-ÔÃ:V/ëæÂÙ&?»8}èGºù­zð±öß:¸ôjçºOØÇ¡së-àlû Í츨£=}ÞaøUõ"nÉ!äóñ>†Îj?úá›B}jv£^!úéÄõ‘··|<Ä/¥Ÿ85ÄõÅ «ÊU裯uZB^òòÇO?cýÓɧzýVùý<—?A_Ê™gy¹˜§Œ¼ðöÙ˜ã æsQ§‹ü+gP‡Aˇ07+XWqæ×Áù„æÇ‘ù@ó=óWPg%óès qJÈ¿„þÿs<Ìψ3ç tT³nž½Äõ ùøï´3ä7Éû3ˆ7ßОø7«ýeåýC—ùŠuzŠ92ó¬îï¢ÿæmôÑ|Æó4í{ÚwÄŸ.¸þ˜/Y¿ôcœpã,ª”eß½´ê¬æ¯’u-Vï û`ßè³ð½rn‚ìkóäœê|®«¹æ½ÎyTs®sÞt΋Î|:ñó~ <òx| ê4¨ÃPóG=ç®–÷µ˜QKœZê QwˆùCÄ ‘Wqÿ\;»³Ò”ÏÿMд•òSRà²HºìËdwåäÞG¿9ùñÎûÂÇÞ«}“mWûæÖ_ömñŸûµO¶5µª}´=š,¤8s~š'.qñ:ªui¦æšãßBŠwìÍäräèV8Ù·EùÝÂlf ¿û€ŠËô÷gRßÜâ¹~6B fBasics/data/msft.dat.rda0000644000176200001440000001231314660437567014767 0ustar liggesusers‹u[ xUE²¾ÈpCADÄ=ހɹ»ÛÜ Iî~ϹnˆˆD ¢ˆ²!;!lÁ ‹ fD…ç Ã(*î(£# Ž¢‘q÷Œ¸à(#8j^Ý®¿ÏûÎ)ß÷ÓtŸîêîªêÚ²#G½Gõöx$ì#|Dø˜ð áSÂg„Ï _:ÿ"|IØOøŠð5á·„„ïÿ&|Oøpð„Cüƶ GEùŒòŠ©x½‡1ÚÛ(((VfÅHHŒ„#…4hˆ¯ñ‰¿ ˆ±Wa)#â<…â<…b¯Â  ‰qCœÇ{â»!ÎcˆóxÅ^^×Ý ‡ÈCŒxÅH@ŒÅHHŒ„Ý#né ÎÓˆOŒøÅˆ8O¡8!ö2ÄÝ qwCìnHÊ⦆¸©[†à¼!tÞ:o΂ó†à¼!tÞ:o΂ó†à¼!tÞ:o΂ó†à¼!4Üœ7ç 'çsêíÔgñŠŸñ‹‘{9ô™G ň8C{y$(FÄî…bwCP6ÄM qSCÜÔç‘\õŠ›z]7ui8bÄ/Fb$(FBb$ì){zňOŒˆóŠóbDÜÔ»bwCR75\7õ ®zW½‚«^ÁU¯àªWpÕ+¸ê\õ ®zW½‚«^ÁU¯Ðg¯à³WðÙ+øì|ö >{Ÿ½¿ÁgCœÇ­ó>ÁyŸ°->a[|B:>!Ÿà¼OØŸ°->!Ÿà³OØŸ°->! 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(a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) #define pi 3.141593 #define EPS 1e-12 #define XLEAST 2.23e-308 #define XSMALL 1.11e-16 #define XINF 1.79e308 #define XMAX 704.78 // log(XINF) - 5 #define MAX_ITER 5000 #define ITMAX 100 #if 0 //////////////////////////////////////////////////////////////////////////////// // // Print error text and return to R - UNUSED // //////////////////////////////////////////////////////////////////////////////// static void printError(char *text, char filename[200]) { error("%s: %s !!!\n", text, filename); } #endif //////////////////////////////////////////////////////////////////////////////// // // FUNCTIONNAME: heap_sort // AUTHOR : Kjersti Aas, NR // DATE : March 2000 // ARGUMENTS : n Number of elements in the vector to be sorted // x Vector to be sorted (n x 1) // order Order of sorted values (n x 1) // DESCRIPTION: Returns order of sorted values in index array. // //////////////////////////////////////////////////////////////////////////////// static void heapSort(int n, double *x, int *order) { int i,j,l; int ir, ordert; double q; for(j = 0; j < n; j++) order[j] = j; if(n <= 1) return; l = (n >> 1) + 1; /* shift n one bit to the right, i.e divide it by two.*/ ir = n; for(;;) /* Indefinite loop */ { if(l > 1) { /* Decrement l before evaluating the expression */ q = x[(ordert = order[--l-1])]; } else { q = x[(ordert = order[ir-1])]; order[ir-1] = order[0]; /* Decrement ir before its value is used */ if(--ir == 1) { order[0] = ordert; return; } } i = l; j = l << 1; while(j <= ir) { if((j < ir) && (x[order[j-1]] > x[order[j]])) j++; if(q > x[order[j-1]]) { order[i-1] = order[j-1]; j += (i=j); } else j = ir + 1; } order[i-1] = ordert; } } //////////////////////////////////////////////////////////////////////////////// // // Evaluates the Bessel function of the third order, index nu=1 // //////////////////////////////////////////////////////////////////////////////// static double bessk1(double x) { int i; double y, k1; double sump, sumq, sumf, sumg; static double p[5] = { 4.8127070456878442310e-1, 9.9991373567429309922e+1, 7.1885382604084798576e+3, 1.7733324035147015630e+5, 7.1938920065420586101e+5 }; static double q[3] = { -2.8143915754538725829e+2, 3.7264298672067697862e+4, -2.2149374878243304548e+6 }; static double f[5] = { -2.2795590826955002390e-1,-5.3103913335180275253e+1, -4.5051623763436087023e+3,-1.4758069205414222471e+5, -1.3531161492785421328e+6 }; static double g[3] = { -3.0507151578787595807e+2, 4.3117653211351080007e+4, -2.7062322985570842656e+6 }; static double pp[11] = { 6.4257745859173138767e-2, 7.5584584631176030810e+0, 1.3182609918569941308e+2, 8.1094256146537402173e+2, 2.3123742209168871550e+3, 3.4540675585544584407e+3, 2.8590657697910288226e+3, 1.3319486433183221990e+3, 3.4122953486801312910e+2, 4.4137176114230414036e+1, 2.2196792496874548962e+0 }; static double qq[9] = { 3.6001069306861518855e+1, 3.3031020088765390854e+2, 1.2082692316002348638e+3, 2.1181000487171943810e+3, 1.9448440788918006154e+3, 9.6929165726802648634e+2, 2.5951223655579051357e+2, 3.4552228452758912848e+1, 1.7710478032601086579e+0 }; if(x < XLEAST) // Case: x < XLEAST { k1 = XINF; } else if(x <= 1.0) // Case: XLEAST <= x < XSMALL { if(x < XSMALL) { k1 = 1.0/x; } else // Case: XSMALL <= x < 1.0 { y = x*x; sump = ((((p[0]*y + p[1])*y + p[2])*y + p[3])*y + p[4])*y + q[2]; sumq = ((y + q[0])*y + q[1])*y + q[2]; sumf = (((f[0]*y + f[1])*y + f[2])*y + f[3])*y + f[4]; sumg = ((y + g[0])*y + g[1])*y + g[2]; k1 = (y*log(x)*sumf/sumg + sump/sumq)/x; } } else if(x > XMAX) // Case: x > XMAX { k1 = 0.0; } else // Case: 1.0 < x <= XMAX { y = 1.0/x; sump = pp[0]; for(i = 1; i < 11; i++) { sump = sump*y + pp[i]; } sumq = y; for(i = 0; i < 8; i++) { sumq = (sumq + qq[i]) * y; } sumq = sumq + qq[8]; k1 = sump/sumq/sqrt(x)*exp(-x); } return(k1); } //////////////////////////////////////////////////////////////////////////////// // // Return density of NIG distribution evaluated at given points // //////////////////////////////////////////////////////////////////////////////// // "FIXME": is only used from fdNIG() below ... could simplify // *or* call from R's dnig void dNIG(double* x, double* mu, double* delta, double* alpha, double* beta, int* n, double* d) { for(int i = 0; i < *n; i++) { double sdx = sqrt(pow(*delta,2.0)+pow((x[i]-*mu),2.0)), xarg = *alpha * sdx, exparg = *delta*sqrt(pow(*alpha,2.0)-pow(*beta,2.0))+*beta*(x[i]-*mu); if(exparg < -XMAX) exparg = -XMAX; if(exparg > XMAX) exparg = XMAX; d[i] = (*alpha*(*delta)/pi)*exp(exparg)*bessk1(xarg)/ sdx; } } //////////////////////////////////////////////////////////////////////////////// // // Return density of NIG distribution evaluated at a given point // (made for integration) // //////////////////////////////////////////////////////////////////////////////// static double fdNIG(double x, double mu, double delta, double alpha, double beta) { int i = 1; double f; dNIG(&x, &mu, &delta, &alpha, &beta, &i, &f); return(f); } ///////////////////////////////////////////////////////////////////////// // // DE-Quadrature // Numerical Automatic Integrator for Improper Integral // method : Double Exponential (DE) Transformation // dimension : one // table : not use functions // intde : integrator of f(x) over (a,b). // intdei : integrator of f(x) over (a,infinity), f(x) is non oscillatory function. // intdeo : integrator of f(x) over (a,infinity), f(x) is oscillatory function. // /////////////////////////////////////////////////////////////////////////// static void intdei(double a, double mu, double delta, double alpha, double beta, double *i, double *err) { /* ---- adjustable parameters ---- */ int mmax = 512; double efs = 0.1, hoff = 11.0; /* ------------------------------ */ int m; double pi4, epsln, epsh, h0, ehp, ehm, epst, ir, h; double iback, irback, t, ep, em, xp, xm, fp, fm, errt, errh, errd; pi4 = atan(1.0); epsln = 1 - log(efs * EPS); epsh = sqrt(efs * EPS); h0 = hoff / epsln; ehp = exp(h0); ehm = 1 / ehp; epst = exp(-ehm * epsln); ir = fdNIG(a + 1, mu, delta, alpha, beta); *i = ir * (2 * pi4); *err = fabs(*i) * epst; h = 2 * h0; m = 1; errh = 0; do { iback = *i; irback = ir; t = h * 0.5; do { em = exp(t); ep = pi4 * em; em = pi4 / em; do { xp = exp(ep - em); xm = 1 / xp; fp = fdNIG(a + xp, mu, delta, alpha, beta) * xp; fm = fdNIG(a + xm, mu, delta, alpha, beta) * xm; ir += fp + fm; *i += (fp + fm) * (ep + em); errt = (fabs(fp) + fabs(fm)) * (ep + em); if (m == 1) *err += errt * epst; ep *= ehp; em *= ehm; } while (errt > *err || xm > epsh); t += h; } while (t < h0); if (m == 1) { errh = (*err / epst) * epsh * h0; errd = 1 + 2 * errh; } else { errd = h * (fabs(*i - 2 * iback) + 4 * fabs(ir - 2 * irback)); } h *= 0.5; m *= 2; } while (errd > errh && m < mmax); *i *= h; if (errd > errh) { *err = -errd * m; } else { *err = errh * epsh * m / (2 * efs); } } //////////////////////////////////////////////////////////////////////////////// // // Return cumulative NIG distribution function evaluated at given points // //////////////////////////////////////////////////////////////////////////////// void pNIG(double *x, double *mu, double *delta, double *alpha, double *beta, int *n, double *p) { int i; double err, v; for(i = 0; i < *n; i++) { // Special cases (-Inf, Inf) if(x[i] <= -XINF) { p[i] = 0.0; } else if(x[i] >= XINF) { p[i] = 1.0; } // Integrates density from x to Inf else { // printf("intdei\n"); intdei(x[i], *mu, *delta, *alpha, *beta, &v, &err); // Check that 0 <= v <= 1 if(v < 0.0) v = 0.0; if(v > 1.0) v = 1.0; // Computes integral from -Inf to x p[i] = 1.0-v; } } } //////////////////////////////////////////////////////////////////////////////// // // Return cdf of NIG distribution evaluated at a given point, with the // probability p substracted (made for root finding) // //////////////////////////////////////////////////////////////////////////////// static double fpNIG(double x, double mu, double delta, double alpha, double beta, double sp) { double f; int i = 1; pNIG(&x, &mu, &delta, &alpha, &beta, &i, &f); f -= sp; return(f); } //////////////////////////////////////////////////////////////////////////////// // // Computes the root of fpNIG // //////////////////////////////////////////////////////////////////////////////// static double zbrent(double x1, double x2, double mu, double delta, double alpha, double beta, double sp) { int iter; double a, b, c, d, e, min1, min2; double fa, fb, fc, p, q, r, s, tol1, xm; d = 0; e = 0; a = x1; b = x2; c = x2; fa = fpNIG(a,mu,delta,alpha,beta,sp); fb = fpNIG(b,mu,delta,alpha,beta,sp); // if((fa > 0.0 && fb > 0.0)||(fa < 0.0 && fb < 0.0)) // printError("Root must be bracketed in zbrent", ""); fc = fb; for(iter = 1; iter <= ITMAX; iter++) { if((fb > 0.0 && fc > 0.0)||(fb < 0.0 && fc < 0.0)) { c = a; fc = fa; e = d = b-a; } if(fabs(fc) < fabs(fb)) { a = b; b = c; c = a; fa = fb; fb = fc; fc = fa; } tol1 = 2.0*EPS*fabs(b)+0.5*EPS; xm = 0.5*(c-b); if(fabs(xm) <= tol1 || fb == 0.0) return b; if(fabs(e) >= tol1 && fabs(fa) > fabs(fb)) { s = fb/fa; if(a == c) { p = 2.0*xm*s; q = 1.0-s; } else { q = fa/fc; r = fb/fc; p = s*(2.0*xm*q*(q-r)-(b-a)*(r-1.0)); q = (q-1.0)*(r-1.0)*(s-1.0); } if(p > 0.0) q = -q; p = fabs(p); min1 = 3.0*xm*q-fabs(tol1*q); min2 = fabs(e*q); if(2.0*p < (min1 < min2 ? min1 : min2)) { e = d; d = p/q; } else { d = xm; e = d; } } else { d = xm; e = d; } a = b; fa = fb; if(fabs(d) > tol1) b += d; else b += SIGN(tol1, xm); fb = fpNIG(b, mu, delta, alpha, beta, sp); } // printError("Maximum number of iterations exceeded in zbrent", ""); return 0.0; } //////////////////////////////////////////////////////////////////////////////// // // Function that computes inverse cumulative NIG distribution function, // evaluated at given points. // INPUT: // p Probabilities at which to evaluate cdf^-1 (nx1) // i_mu First parameter of NIG distribution // i_delta Second parameter of NIG distribution // i_alpha Third parameter of NIG distribution // i_beta Fourth parameter of NIG distribution // i_n Number of quantiles // q Computed quantiles // //////////////////////////////////////////////////////////////////////////////// void qNIG(double* p, double* i_mu, double* i_delta, double* i_alpha, double* i_beta, int* i_n, double* q) { int i, n, counter, *pOrder; double mu, delta, alpha, beta, qTmp, sp, lpiv, rpiv, mpiv, div, lpivVal, rpivVal; n=*i_n; mu=*i_mu; delta=*i_delta; alpha=*i_alpha; beta=*i_beta; // Expectation and standard deviation of NIG distribution, // used for setting lpiv and rpiv mpiv = mu+delta*beta/sqrt(alpha*alpha-beta*beta); div = sqrt(delta*alpha*alpha/pow(alpha*alpha-beta*beta,1.5)); //Allocagte space: pOrder = malloc(n*sizeof(int)); //Find descending order of probabilities:0.5, heapSort(n, p, pOrder); for(i=0;i 0) { lpiv = MAX(lpiv, q[pOrder[n-i]]); // Make sure that upper boundary is > than lower if(rpiv <= lpiv) { while(rpiv <= lpiv) { rpiv += 2*div; } } } lpivVal = fpNIG(lpiv,mu,delta,alpha,beta,sp); rpivVal = fpNIG(rpiv,mu,delta,alpha,beta,sp); // Search for a quantile q that fulfills |fpNIG(q)-p| < eps if(lpivVal*rpivVal >= 0.0) { while(lpivVal*rpivVal >= 0.0) { counter++; lpiv = lpiv-pow(2.0, (double)counter)*div; rpiv = rpiv+pow(2.0, (double)counter)*div; lpivVal = fpNIG(lpiv,mu,delta,alpha,beta,sp); rpivVal = fpNIG(rpiv,mu,delta,alpha,beta,sp); } } qTmp = zbrent(lpiv, rpiv, mu, delta, alpha, beta, sp); q[pOrder[n-i-1]] = qTmp; } } // Free space: free(pOrder); } //////////////////////////////////////////////////////////////////////////////// fBasics/src/gld.c0000644000176200001440000003521314324176520013335 0ustar liggesusers/* gld.fm5.fx.c - Part of Robert King's gld package for the R statistical * language. * * Copyright (C) Robert King 1993,2000,2001,2005,2006 * robert.king@newcastle.edu.au * http://tolstoy.newcastle.edu.au/~rking/publ/software.html * * This program is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by the Free * Software Foundation; either version 2 of the License, or (at your option) * any later version. * * This program is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or * FITNESS FOR A PARTICULAR PURPOSE. * * See the GNU General Public License for more details. * You should have received a copy of the GNU General Public License along * with this program; if not, write to the * Free Software Foundation, Inc., 59 Temple Place, Suite 330, * Boston, MA 02111-1307 USA * * These functions calculate F(x) for the five parameter version of the * FMKL paramterisation of the gld. * This parameterisation is a variation of the 5 parameter version of the * RSparameterisation. * * This is not very good C. It is based on gld.rs.fx.c, which is based on * code written in 1993, as my first real program. I still haven't tidied * it up very much for release with the R package. * However, it has been used for some time and works fine. * * This is code is loaded into R. * The two include lines are handled by R, but need to be here to keep * the compile step happy * * $Id$ */ #include #include /* proto */ static void fm5_funcd( double , double , double *, double *, double *, double *, double *, double *, double *); /* This needs a comment for explaining the arguments */ void gl_fm5_distfunc( double *pa,double *pb,double *pc,double *pd, double *pe, double *pu1,double *pu2,double *pxacc, int *max_it, double *ecks, double *u, int *lengthofdata) { /* pa to pe: pointers to the values of the parameters of the gld (fm5 param) * pu1: minimum value of u, should be zero * pu2: maximum value of u, should be 1 * pxacc: desired accuracy of the calculation * max_it: maximum iterations for N-R root finder * ecks: the quantiles of the gld given * u: array to put the calculated depths * pl: length of the data */ double u1, u2, xacc; int i,j; double df,dx,dxold,f,fh,fl; double x; double temp,xh,xl,rts; /* trying initialising things */ i=0; j=0; df=0.0;dx=0.0;dxold=0.0;f=0.0;fh=0.0;fl=0.0; x=0.0; temp=0.0;xh=0.0;xl=0.0;rts=0.0; u1 = *pu1; u2 = *pu2; xacc = *pxacc; /* If we are trying to do things outside the range, lets set them at almost 0 or almost 1 */ if (*pc < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } if (*pd < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } for (i=0;i<*lengthofdata;i++) { x = ecks[i]; u[i] = 0.0; fm5_funcd(u1,x,&fl,&df,pa,pb,pc,pd,pe); fm5_funcd(u2,x,&fh,&df,pa,pb,pc,pd,pe); if (fl*fh >= 0.0) { REprintf("Program aborted at parameter values %f, %f, %f, %f %f\n", *pa, *pb, *pc, *pd, *pe); REprintf("The data value being investigated was index %d",i); REprintf(" value: %f\n",x); error("C code failure - see error message printed above"); } if (fl < 0.0) { xl = u1; xh = u2; } else { xh = u1; xl = u2; } rts = 0.5*(u1+u2); dxold = fabs(u2-u1); dx = dxold; fm5_funcd(rts,x,&f,&df,pa,pb,pc,pd,pe); for (j=1;j<=*max_it;j++) { if ((((rts - xh)*df - f)* ( (rts-xl)*df - f) >= 0.0 ) || ( fabs(2.0*f) > fabs (dxold*df))) { dxold = dx; dx = .5* (xh - xl); rts = xl +dx; if (xl == rts ) { u[i] = rts; break; } } else { dxold = dx; dx = f/df; temp = rts; rts -= dx; if (temp == rts) { u[i] = rts; break; } } if (fabs(dx) < xacc) { u[i] = rts; break; } fm5_funcd(rts,x,&f,&df,pa,pb,pc,pd,pe); if (f < 0.0) xl =rts; else xh =rts; } } } static void fm5_funcd( double u, double x, double *F, double *dFdu, double *pa, double *pb, double *pc, double *pd, double *pe) { /* *F is the gld F-1(u) */ /* *dFdu is dF-1(u)/du */ /* NOTE: *dFdu is 1/f(x) a.k.a. 1/f(F-1(u)) - the opposite of what's required for the pdf */ if ( *pc == 0 ) { if ( *pd == 0 ) { /* Both l3 and l4 zero */ *F = *pa + ( (1 - *pe) * log(u) - (1 + *pe) * (log(1.0-u)) )/ *pb - x; /* need the derivatives for each case */ *dFdu = ( (1 - *pe)/u + (1 + *pe)/(1-u)) / *pb ; } else { /* l3 zero, l4 non-zero */ *F = *pa + ((1 - *pe) * log(u) - ((1 + *pe)*( pow((1-u),*pd)-1)/ *pd ) ) / *pb - x; *dFdu = ((1 - *pe)/u + (1 + *pe)*pow((1-u), *pd-1) )/ *pb ; } } else { if ( *pd == 0 ) { /* l4 zero, l3 non-zero */ *F = *pa + ( (1 - *pe)*(( pow(u,*pc)- 1)/ *pc) - (1 + *pe)* log(1-u) ) / *pb - x; *dFdu = ( (1 - *pe) * pow(u, *pc-1) + (1 + *pe)/(1-u) ) / *pb; } else { /* l3 non-zero, l4 non-zero */ *F = ((1 - *pe)* ( pow((u),*pc) -1 )/ *pc - (1 + *pe)* (pow((1.0-u),*pd) -1 )/ *pd )/ *pb + *pa - x; *dFdu = ((1 - *pe)* ( pow((u),(*pc-1.0)) ) + (1 + *pe)* ( pow( (1.0-u),(*pd-1.0)) ) )/ *pb ; } } } /* gld.fmkl.fx.c - Part of Robert King's gld package for the R statistical * language. * * Copyright (C) Robert King 1993,2000,2001,2006 * robert.king@newcastle.edu.au * http://tolstoy.newcastle.edu.au/~rking/publ/software.html * * This program is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by the Free * Software Foundation; either version 2 of the License, or (at your option) * any later version. * * This program is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or * FITNESS FOR A PARTICULAR PURPOSE. * * See the GNU General Public License for more details. * You should have received a copy of the GNU General Public License along * with this program; if not, write to the * Free Software Foundation, Inc., 59 Temple Place, Suite 330, * Boston, MA 02111-1307 USA * * These functions calculate F(x) for the Freimer, Mudholkar, Kollia and Lin parameterisation * of the gld. It is adapted from gld.rs.fx.c (also in this R distribution). * Because of that, this is not very good C. The original code was my first * real program in C and I haven't tidied it up very much for release with the R package. * However, it has been used for some time and works fine. * * This is code is loaded into R. * The two include lines are handled by R, but need to be here to keep the compile step happy * $Id$ */ #include #include #include /* proto */ static void fmkl_funcd( double , double , double *, double *, double *, double *, double *, double *); /* the function that finds the root */ void gl_fmkl_distfunc( double *pa,double *pb,double *pc,double *pd, double *pu1,double *pu2,double *pxacc, int *max_it, double *ecks, double *u, int *lengthofdata) { /* pa to pd: pointers to the values of the parameters of the gld (rs param) * pu1: minimum value of u, should be zero * pu2: maximum value of u, should be 1 * pxacc: desired accuracy of the calculation * max_it: maximum iterations for N-R root finder * ecks: the quantiles of the gld given * u: array to put the calculated depths * pl: length of the data */ double u1, u2, xacc; int i,j; double df,dx,dxold,f,fh,fl; double x; double temp,xh,xl,rts; /* trying initialising things */ i=0; j=0; df=0.0;dx=0.0;dxold=0.0;f=0.0;fh=0.0;fl=0.0; x=0.0; temp=0.0;xh=0.0;xl=0.0;rts=0.0; u1 = *pu1; u2 = *pu2; xacc = *pxacc; if (*pc < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } if (*pd < 0) { if (u1 == 0) { u1 = xacc; } if (u2 == 1) { u2 = 1-xacc; } } for (i=0;i<*lengthofdata;i++) { x = ecks[i]; u[i] = 0.0; fmkl_funcd(u1,x,&fl,&df,pa,pb,pc,pd); fmkl_funcd(u2,x,&fh,&df,pa,pb,pc,pd); if (fl*fh >= 0.0) { /* This is suggested in writing R extensions, but still gives the warning */ /* error("Program aborted at parameter values %f, %f, %f, %f\n The data value being investigated was index %d, value: %f\n", *pa, *pb, *pc, *pd, i, x); */ REprintf("C code aborted at parameter values %f, %f, %f, %f\n", *pa, *pb, *pc, *pd); REprintf("The data value being investigated was index %d",i); REprintf(" value: %f\n",x); error("C code numerical failure"); } if (fl < 0.0) { xl = u1; xh = u2; } else { xh = u1; xl = u2; } rts = 0.5*(u1+u2); dxold = fabs(u2-u1); dx = dxold; fmkl_funcd(rts,x,&f,&df,pa,pb,pc,pd); for (j=1;j<=*max_it;j++) { if ((((rts - xh)*df - f)* ( (rts-xl)*df - f) >= 0.0 ) || ( fabs(2.0*f) > fabs (dxold*df))) { dxold = dx; dx = .5* (xh - xl); rts = xl +dx; if (xl == rts ) { u[i] = rts; break; } } else { dxold = dx; dx = f/df; temp = rts; rts -= dx; if (temp == rts) { u[i] = rts; break; } } if (fabs(dx) < xacc) { u[i] = rts; break; } fmkl_funcd(rts,x,&f,&df,pa,pb,pc,pd); if (f < 0.0) xl =rts; else xh =rts; } } } static void fmkl_funcd( double u, double x, double *F, double *dFdu, double *pa, double *pb, double *pc, double *pd) { /* *F is the gld F-1(u) */ /* *dFdu is dF-1(u)/du */ /* NOTE: *dFdu is 1/f(x) a.k.a. 1/f(F-1(u)) - the opposite of what's required for the pdf */ if ( *pc == 0 ) { if ( *pd == 0 ) { /* Both l3 and l4 zero */ *F = *pa + (log(u) - log(1.0-u))/ *pb - x; *dFdu = ( 1 /(u * (1-u))) / *pb ; /* correct but confusing, should be 1/u + 1/(1-u) */ } else { /* l3 zero, l4 non-zero */ *F = *pa + ( log(u) - (( pow((1-u),*pd)-1)/ *pd ) ) / *pb - x; *dFdu = (1/u + pow((1-u), *pd-1) )/ *pb ; } } else { if ( *pd == 0 ) { /* l4 zero, l3 non-zero */ *F = *pa + ( (( pow(u,*pc)- 1)/ *pc) - log(1-u) ) / *pb - x; *dFdu = (pow(u, *pc-1) + 1/(1-u) ) / *pb; } else { /* l3 non-zero, l4 non-zero */ *F = ( ( pow((u),*pc) -1 )/ *pc - (pow((1.0-u),*pd) -1 )/ *pd )/ *pb + *pa - x; *dFdu = ( ( pow((u),(*pc-1.0)) ) + ( pow( (1.0-u),(*pd-1.0)) ) )/ *pb ; } } } /* gld.rs.fx.c - Part of Robert King's gld package for the R statistical * language. * * Copyright (C) Robert King 1993,2000,2001 * robert.king@newcastle.edu.au * http://maths.newcastle.edu.au/~rking/publ/software.html * * This program is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by the Free * Software Foundation; either version 2 of the License, or (at your option) * any later version. * * This program is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or * FITNESS FOR A PARTICULAR PURPOSE. * * See the GNU General Public License for more details. * You should have received a copy of the GNU General Public License along * with this program; if not, write to the * Free Software Foundation, Inc., 59 Temple Place, Suite 330, * Boston, MA 02111-1307 USA * * These functions calculate F(x) for the rs paramterisation for the gld * This is not very good C. I wrote this in 1993, as my first real program * and I haven't tidied it up very much for release with the R package. * However, it has been used for some time and works fine. * * This is code is loaded into R. It doesn't need stdio.h * It needs math.h, which is loaded automagically by R. */ #include #include #include static double la, lb, lc, ld, x; /* the function that finds the root */ static void funcd(double u, double *a, double *b); void gl_rs_distfunc( double *pa,double *pb,double *pc,double *pd, double *px1,double *px2,double *pxacc, int *max_it, double *ecks, double *u, int *pl) { /* pa to pd: pointers to the values of the parameters of the gld (rs param) * px1: minimum value of u, should be zero * px2: maximum value of u, should be 1 * pxacc: desired accuracy of the calculation * max_it: maximum iterations for N-R root finder * ecks: the quantiles of the gld given * u: array to put the calculated depths * pl: length of the data */ double x1, x2, xacc; double a, b, c, d; int l; int i,j; double df,dx,dxold,f,fh,fl; double temp,xh,xl,rts; // void funcd(); // 2022-10-20 GNB: warning from CRAN about absence of // prototype. Also it is declared before this point x1 = *px1; x2 = *px2; xacc = *pxacc; a = *pa; b = *pb; c = *pc; d = *pd; l = *pl; la = a; lb = b; lc = c; ld = d; /* The C version has something here to force the limits to be xacc and 1-xacc rather than 0 and 1 if lambda3 and lambda4 are negative. I can't see why, so I'm leaving it out.*/ for (i=0;i= 0.0) { REprintf("Program aborted during calculation of F(x)"); REprintf("at parameter values %f, %f, %f, %f\n", *pa, *pb, *pc, *pd); REprintf("The x value being investigated was index: %d",i); REprintf(" value: %f\n",x); error("C code numerical failure"); } if (fl < 0.0) { xl = x1; xh = x2; } else { xh = x1; xl = x2; } rts = 0.5*(x1+x2); dxold = fabs(x2-x1); dx = dxold; funcd(rts,&f,&df); for (j=1;j<= *max_it;j++) { if ((((rts - xh)*df - f)* ( (rts-xl)*df - f) >= 0.0 ) || ( fabs(2.0*f) > fabs (dxold*df))) { dxold = dx; dx = .5* (xh - xl); rts = xl +dx; if (xl == rts ) { u[i] = rts; break; } } else { dxold = dx; dx = f/df; temp = rts; rts -= dx; if (temp == rts) { u[i] = rts; break; } } if (fabs(dx) < xacc) { u[i] = rts; break; } funcd(rts,&f,&df); if (f < 0.0) xl =rts; else xh =rts; } } } // 2022-10-20 GNB: fixed old style declarations (CRAN warns about them now) static void funcd(double u, double *a, double *b) // double u,*a,*b; { /* function is the gld F-1(u) */ /* df_du is dF-1(u)/du */ double function, df_du; if ( lc == 0 ) { if ( ld == 0 ) { /* Both l3 and l4 zero */ function = la - x; df_du = 0; } else { /* l3 zero, l4 non-zero */ function = la + ( 1 - pow((1-u),ld) )/ lb - x; df_du = (ld) * ( pow((1.0-u),(ld - 1.0)) / lb); } } else { if ( ld == 0 ) { /* l4 zero, l3 non-zero */ function = la + ( pow(u,lc) - 1 ) / lb - x; df_du = lc * ( pow(u,(lc- 1.0)) ) / lb; } else { /* l3 non-zero, l4 non-zero */ function = ( pow((u),lc) - pow((1.0-u),ld) )/ lb + la - x; df_du = (lc * (pow((u),(lc-1.0))) + ld * ( pow((1.0-u),(ld-1.0)) ))/ lb; } } *a = function; *b = df_du; } fBasics/ChangeLog0000644000176200001440000006234614263246022013412 0ustar liggesusers2013-04-30 chalabi * DESCRIPTION, NAMESPACE, R/builtin-ssdenGss.R, R/zzz.R, src/gss_a.f, src/gss_b.c: gss is now an import package 2012-09-21 chalabi * ChangeLog, DESCRIPTION: Updated ChangeLog and DESC files 2012-09-20 mmaechler * DESCRIPTION, NAMESPACE, R/dist-stable-deprecated.R, inst/unitTests/runit.DistributionFits.R, man/fBasics-deprecated.Rd: no longer re-export the [dpqr]stable() functions from "stabledist"; and more reasonable unitTests for those 2012-09-14 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * DESCRIPTION: updated version number * R/utils-Sys.putenv.R: Removed deprecated putenv patch 2012-08-22 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * DESCRIPTION, NAMESPACE, R/stats-interpAkima.R, R/stats-interpLinear.R: Removed code that was copied from akiam (0.5-1) to avoid external .Fortran calls to akima package. * DESCRIPTION: updated version number * ChangeLog: updated Changelog * DESCRIPTION: added reference to code from akima (0.5-1) package 2012-08-12 chalabi * DESCRIPTION: updated version number * NAMESPACE, R/test-normalityTest.R: shapiroTest calls now base R function shapiro.test * DESCRIPTION: added stats pkg in Depends 2012-03-21 chalabi * R/utils-getS4.R, man/utils-getS4.Rd: updated manual page and getArgs() 2012-03-20 chalabi * NAMESPACE, R/builtin-ssdenGss.R, src/gss.f, src/gss_a.f, src/gss_b.c: removed fixed warnings generated by fortran routine from gss package * DESCRIPTION: updated DESC 2012-03-19 chalabi * R/builtin-ssdenGss.R, R/gui-sliderMenu.R, R/gui-stableSlider.R, R/utils-listFunctions.R: fixed partial argument names 2012-02-16 mmaechler * NAMESPACE, R/builtin-ssdenGss.R, R/dist-nig.R, R/gui-sliderMenu.R, R/utils-colorLocator.R, man/utils-colorLocator.Rd: more cosmetic; notably first round of "partial argument match" fixes * src/nig.c: add #include .. (thanks to Jan Deleeuw) 2012-01-14 mmaechler * DESCRIPTION, NAMESPACE, R/utils-getS4.R, man/00fBasics-package.Rd, man/utils-getS4.Rd: do not export getCall() -- which conflicts with R >= 2.14, in that case * src/gld.c, src/nig.c: fix C code {stderr, exit} 2011-09-23 mmaechler * DESCRIPTION: remove deprecated "LazyLoad" entry 2011-08-02 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * R/utils-getS4.R, man/fBasics-deprecated.Rd, man/stableSlider.Rd, man/utils-getS4.Rd: fixed warnings with R CMD check 2011-06-22 mmaechler * NAMESPACE, R/dist-distCheck.R, R/utils-getS4.R, man/utils-getS4.Rd: getCall() -- adapt to future R 2.14.x ff 2011-06-22 chalabi * NAMESPACE: updated NAMESPACE * man/dist-distCheck.Rd, man/dist-ghRobMoments.Rd, man/dist-ghtRobMoments.Rd, man/dist-gldMode.Rd, man/dist-hypRobMoments.Rd, man/dist-nigRobMoments.Rd, man/dist-normRobMoments.Rd, man/dist-sght.Rd, man/fBasics-deprecated.Rd, man/stableSlider.Rd, man/utils-hessian.Rd: updated man pages with new functions * R/dist-hypRobMoments.R, R/dist-stable-deprecated.R, R/utils-colorRGB.R, R/utils-getS4.R: getSlot() is now a wrapper of slot() function 2011-05-04 chalabi * man/test-normalityTests.Rd: added credits to Paul Johnson for earlier funcions based on his Fortran code. 2011-02-03 mmaechler * DESCRIPTION, NAMESPACE, R/dist-distCheck.R, man/dist-distCheck.Rd: rename package "stable" to "stabledist" - as "stable" has existed for many years in Jim Lindsey`s off-CRAN collection 2011-01-31 mmaechler * DESCRIPTION: update Version ('stable' needs it for unit tests) * man/dist-distCheck.Rd: update: forgot new arg.s in usage * man/fBasics-deprecated.Rd: man for keep deprecated [dpqr]stable() * R/dist-stable-deprecated.R: keep "back compatibility" version of stable * R/dist-distCheck.R, man/dist-distCheck.Rd: (changes from a week ago:) "control" attributes only in the fBasics "deprecated" versions; add utils; start re-enabling the unit tests 2011-01-24 mmaechler * DESCRIPTION, NAMESPACE, R/dist-stable.R, R/dist-stableMode.R, R/gui-stableSlider.R, inst/unitTests/runit.StableDistribution.R, man/dist-stable.Rd, man/dist-stableMode.Rd, man/stableSlider.Rd: from fBasics, factor out a new "stable" package; with distributions, but not (yet?) stableFit() etc 2010-10-26 chalabi * NAMESPACE: updated NAMESPACE 2010-07-23 chalabi * inst/DocCopying.pdf: removed DocCopying.pdf license is already specified in DESCRIPTION file 2010-05-29 wuertz * R/utils-getS4.R, man/utils-getS4.Rd: man page updated * R/utils-getArgs.R: getArgs moved to getS4 * NAMESPACE, R/builtin-glGld.R, R/builtin-posdefCorpcor.R, R/builtin-testNortest.R, R/builtin-varpieA2R.R, R/dist-distCheck.R, R/dist-distErrors.R, R/dist-distPlot.R, R/dist-fDISTFIT.R, R/gui-HeavisideSlider.R, R/gui-ghSlider.R, R/gui-nigSlider.R, R/gui-returnSeries.R, R/utils-Sys.putenv.R, R/utils-getArgs.R, R/utils-getS4.R, R/utils-listDescription.R, R/utils-listFunctions.R, R/utils-listIndex.R, R/utils-printControl.R, R/utils-randLCG.R, R/utils-unitTest.R, man/utils-getS4.Rd: getArgs added, minor mods in some functions, namespace adapted 2010-03-24 chalabi * NAMESPACE, R/builtin-xlsGdata.R, R/read-download.R, R/read-lines.R, R/read-lynx.R, R/read-split.R, R/read-xls.R, inst/perl, man/read-download.Rd, man/read-lines.Rd, man/read-lynx.Rd, man/read-split.Rd, man/read-xls.Rd: moved new read.<> functions from fBasics to fImport 2010-03-09 dscott * man/test-correlationTest.Rd, man/test-fHTEST.Rd, man/test-ks2Test.Rd, man/test-locationTest.Rd, man/test-normalityTests.Rd, man/test-scaleTest.Rd, man/test-varianceTest.Rd: Fixed spelling mistakes * src/nig.c: changed XMAX for 64 bit compatiblitiy and removed unused headers 2010-03-02 wuertz * R/builtin-xlsGdata.R: xls byuiltin added 2010-02-23 wuertz * NAMESPACE: namespace adapted * NAMESPACE: namespace adapted * R/builtin-xlsGdata.R, R/read-download.R, R/read-lines.R, R/read-lynx.R, R/read-split.R, R/read-xls.R, inst/perl, inst/perl/IO, inst/perl/IO/AtomicFile.pm, inst/perl/IO/InnerFile.pm, inst/perl/IO/Lines.pm, inst/perl/IO/Scalar.pm, inst/perl/IO/Scalar.pm.html, inst/perl/IO/ScalarArray.pm, inst/perl/IO/Stringy.pm, inst/perl/IO/Wrap.pm, inst/perl/IO/WrapTie.pm, inst/perl/OLE, inst/perl/OLE/Storage_Lite.pm, inst/perl/Spreadsheet, inst/perl/Spreadsheet/ParseExcel, inst/perl/Spreadsheet/ParseExcel.pm, inst/perl/Spreadsheet/ParseExcel/Dump.pm, inst/perl/Spreadsheet/ParseExcel/FmtDefault.pm, inst/perl/Spreadsheet/ParseExcel/FmtJapan.pm, inst/perl/Spreadsheet/ParseExcel/FmtJapan2.pm, inst/perl/Spreadsheet/ParseExcel/FmtUnicode.pm, inst/perl/Spreadsheet/ParseExcel/SaveParser.pm, inst/perl/Spreadsheet/ParseExcel/Utility.pm, inst/perl/html-parser, inst/perl/html-parser/html-ascii.pl, inst/perl/html-parser/html-parser.html, inst/perl/html-parser/html-to-ascii.pl, inst/perl/html-parser/html-to-rfc.pl, inst/perl/html-parser/parse-html.pl, inst/perl/html-parser/rfc.pl, inst/perl/html-parser/tformat.pl, inst/perl/xls2csv.pl, man/00fBasics-package.Rd, man/read-download.Rd, man/read-lines.Rd, man/read-lynx.Rd, man/read-split.Rd, man/read-xls.Rd: read tools added for reading files from internet 2010-02-21 dscott * ChangeLog, NAMESPACE, R/builtin-glGld.R, R/dist-gld.R, R/dist-gldFit.R: Some fixes to gld functions 2010-02-09 chalabi * DESCRIPTION: updated version number 2010-02-08 chalabi * DESCRIPTION: updated DESC files * man/utils-colorLocator.Rd: update Rd files 2009-12-08 wuertz * DESCRIPTION: description file updated 2009-12-07 wuertz * man/00fBasics-package.Rd: text updated 2009-12-06 wuertz * DistributionsStatus: no longer needed * DESCRIPTION: description updated * DESCRIPTION, man/00fBasics-package.Rd: description file and manual package info updated * man/plot-interactivePlot.Rd, man/print.Rd, man/rand-lcg.Rd, man/returnSeriesGUI.Rd: obsolete man files removed * src/Makevars, src/gld.c, src/gss.f: src files added * R/builtin-posdefCorpcor.R, R/builtin-testNortest.R, R/builtin-varpieA2R.R, R/lines-recessionLines.R, R/list-listDescription.R, R/list-listFunctions.R, R/list-listIndex.R, R/print.R, R/rand-lcg.R, R/utils-listDescription.R, R/utils-listFunctions.R, R/utils-listIndex.R, R/utils-printControl.R, R/utils-randLCG.R, R/utils-recessionLines.R, man/00fBasics-package.Rd: builtin and util scripts added * R/test-correlationTest.R, R/test-fHTEST.R, R/test-jbALM.R, R/test-jbLM.R, R/test-jbTable.R, R/test-ks2Test.R, R/test-locationTest.R, R/test-scaleTest.R, R/test-varianceTest.R: tests updated * R/test-NormalityTests.R, R/test-normalTest.R, R/test-normalityTest.R: normality tests merged * man/00fUtilities-package.Rd, man/colorLocator.Rd, man/colorPalette.Rd, man/colorTable.Rd, man/dist-ssd.Rd, man/interp-akimaInterp.Rd, man/interp-krigeInterp.Rd, man/interp-linearInterp.Rd, man/listDescription.Rd, man/listFunctions.Rd, man/listIndex.Rd, man/plot-returnSeriesGUI.Rd, man/stats-interpAkima.Rd, man/stats-interpKrige.Rd, man/stats-interpLinear.Rd, man/utils-colorLocator.Rd, man/utils-colorPalette.Rd, man/utils-colorTable.Rd, man/utils-getS4.Rd, man/utils-interactivePlot.Rd, man/utils-listDescription.Rd, man/utils-listFunctions.Rd, man/utils-listIndex.Rd, man/utils-printControl.Rd, man/utils-randLCG.Rd: man pages updated * man/00fBasics-package.Rd, man/baseMethods.Rd: man updated * R/builtin-HyperbolicDist.R, R/builtin-gelGMM.R, R/builtin-gelGmm.R, R/builtin-gelGmm2.R, R/builtin-gld.R, R/builtin-gmmGMM.R, R/builtin-gmmGmm.R, R/builtin-gmmGmm2.R, R/builtin-gss.R, R/builtin-sandwichGMM.R, R/color-Locator.R, R/color-Palettes.R, R/color-RGB.R, R/color-Table.R, R/dist-ghFit.R, R/dist-ghMode.R, R/dist-nig.R, R/dist-ssd.R, R/dist-stableFit.R, R/interp-akimaInterp.R, R/interp-krigeInterp.R, R/interp-linearInterp.R, R/matrix-colVec.R, R/matrix-gridVector.R, R/matrix-inv.R, R/matrix-kron.R, R/matrix-norm.R, R/matrix-pascal.R, R/matrix-pdl.R, R/matrix-posDefinite.R, R/matrix-rk.R, R/matrix-tr.R, R/matrix-triang.R, R/matrix-tslag.R, R/matrix-vech.R, R/plot-boxPlot.R, R/plot-interactivePlot.R, R/plot-qqPlot.R, R/plot-seriesPlot.R, R/stats-basicStats.R, R/stats-interpAkima.R, R/stats-interpKrige.R, R/stats-interpLinear.R, R/stats-maxdd.R, R/stats-rowStats.R, R/utils-Heaviside.R, R/utils-colorLocator.R, R/utils-colorPalettes.R, R/utils-colorRGB.R, R/utils-colorTable.R, R/utils-decor.R, R/utils-getS4.R, R/utils-interactivePlot.R, R/utils-symbolTable.R, R/utils-unitrootNA.R: functions sorted and partly scripts renamed * R/builtin-gelGmm2.R, R/builtin-glGld.R, R/builtin-gmmGmm2.R, R/builtin-hypHyperbolicDist.R, R/builtin-kweightsSandwhich.R, R/builtin-ssdenGss.R: builtin functions renamed 2009-12-03 wuertz * NAMESPACE, R/dist-distErrors.R, R/dist-distPlot.R, R/dist-gh.R, R/dist-ghFit.R, R/dist-ghMode.R, R/dist-ghMoments.R, R/dist-ghRobMoments.R, R/dist-nigFit.R, man/00fBasics-package.Rd, man/dist-gh.Rd, man/dist-ghFit.Rd, man/dist-ghMode.Rd, man/dist-ghMoments.Rd, man/dist-ght.Rd, man/dist-ghtFit.Rd, man/dist-ghtMode.Rd, man/dist-ghtMoments.Rd, man/dist-hypMoments.Rd, man/dist-nigMoments.Rd, man/dist-ssd.Rd, man/dist-ssdFit.Rd, man/dist-stable.Rd, man/dist-stableMode.Rd: man pages, NAMESPACE updated and som minor new functionalities added * R/dist-ghMoments.R, R/dist-ghtMoments.R, R/dist-hypMoments.R, R/dist-nigMoments.R, R/lines-recessionLines.R: true moments added * R/dist-nigFit.R, R/utils-hessian.R: standard errors for nigFit added 2009-12-02 wuertz * DistributionsStatus, NAMESPACE, R/dist-ghMoments.R, R/dist-ght.R, R/dist-hypMoments.R, R/dist-nigMoments.R, R/dist-sght.R: further moment functions added * R/dist-ghMoments.R: gh central moments added 2009-12-01 wuertz * NAMESPACE, R/dist-gh.R, R/dist-ghMoments.R, R/dist-ght.R, R/dist-ghtMode.R, R/dist-gld.R, R/dist-hyp.R, R/dist-hypMoments.R, R/dist-hypRobMoments.R, R/dist-nig.R, R/dist-nigMoments.R, R/dist-norm.R, R/dist-normFit.R, R/dist-normRobMoments.R, R/dist-sgh.R, R/dist-sghFit.R, R/dist-snig.R, R/dist-ssd.R, R/dist-stable.R, inst/unitTests/runit.HyperbolicDistribution.R, man/dist-StableDistribution.Rd, man/dist-ght.Rd, man/dist-gld.Rd, man/dist-hyp.Rd, man/dist-stable.Rd: distribution functionality upgraded, some man pages are still missing 2009-11-24 wuertz * DistributionsStatus, NAMESPACE, R/dist-nFit.R, R/dist-normFit.R: nFit renamed to normFit 2009-11-23 wuertz * R/dist-nigFit.R: log(var... updated in nigFit * R/dist-gldFit.R, man/dist-gldFit.Rd: gld mps modified 2009-11-22 wuertz * R/dist-gldFit.R, man/dist-gld.Rd: gld fit and distribution updated * DistributionsStatus, NAMESPACE, R/dist-gldFit.R, R/dist-nigFit.R, man/dist-nigFit.Rd: Yohan's var(MPS) for the nig added * NAMESPACE, R/builtin-gss.R, R/dist-gld.R, R/dist-gldFit.R, R/dist-ssd.R, R/dist-ssdFit.R, R/plot-varpie.R: namespace updated 2009-11-21 wuertz * R/plot-acfPlot.R, R/plot-boxPlot.R, R/plot-counts.R, R/plot-histPlot.R, R/plot-interactivePlot.R, R/plot-scalinglawPlot.R, R/plot-seriesPlot.R, R/plot-subPlot.R, R/plot-subStars.R, R/plot-varpie.R, R/plot.R: cod beautified and descriptions added for plot functions * R/plot-qqPlot.R, man/plot-qqPlot.Rd: qq plot for GLD distribution added * DESCRIPTION: builtin packages and authors added * man/dist-nigMode.Rd: nigMode manual page added * man/dist-ghtMode.Rd, man/dist-nigMoments.Rd: manual page information added * R/builtin-HyperbolicDist.R, R/builtin-gelGMM.R, R/builtin-gld.R, R/builtin-gmmGMM.R, R/builtin-gss.R, R/builtin-sandwichGMM.R, R/dist-hyp.R: builtin function documentation improved * R/builtin-HyperbolicDist.R: code borrowed from David Scott no moved to script file builtin-HyperbolicDist * man/dist-gld.Rd: examples modified * R/dist-nigFit.R: nig mps fit objective function renamed * NAMESPACE: namespace updated * DistributionsStatus: file distribution status added * R/dist-ghFit.R, R/dist-ghMode.R, R/dist-ghMoments.R, R/dist-ght.R, R/dist-ghtFit.R, R/dist-ghtMode.R, R/dist-gldMode.R, R/dist-hyp.R, R/dist-hypFit.R, R/dist-hypMode.R, R/dist-nFit.R, R/dist-nig.R, R/dist-nigFit.R, R/dist-nigMode.R, R/dist-nigMoments.R, R/dist-nigRobMoments.R, R/dist-nigStats.R, R/dist-sgh.R, R/dist-sghFit.R, R/dist-snig.R, R/dist-snigFit.R, R/dist-ssdFit.R, R/dist-stable.R, R/dist-stableFit.R, R/dist-stableMode.R, R/dist-tFit.R: distribution Functions updated, status list added * R/builtin-gelGMM.R, R/builtin-gld.R, R/builtin-gmmGMM.R, R/builtin-gss.R, R/color-Locator.R, R/color-Palettes.R, R/color-RGB.R, R/color-Table.R, R/dist-distCheck.R, R/dist-fDISTFIT.R, R/dist-gh.R, R/dist-ghRobMoments.R, R/dist-ght.R, R/dist-ghtFit.R, R/dist-ghtRobMoments.R, R/dist-gld.R, R/dist-gldFit.R, R/dist-gldMode.R, R/dist-gldRobMoments.R, R/dist-hyp.R, R/dist-hypFit.R, R/dist-hypMode.R, R/dist-nFit.R, R/dist-nig.R, R/dist-nigFit.R, R/dist-nigMode.R, R/dist-nigRobMoments.R, R/dist-nigStats.R, R/dist-sgh.R, R/dist-sghFit.R, R/dist-snig.R, R/dist-snigFit.R, R/dist-ssd.R, R/dist-ssdFit.R, R/dist-stable.R, R/dist-tFit.R, R/gui-HeavisideSlider.R, R/gui-hypSlider.R, R/gui-nigSlider.R, R/gui-returnSeries.R, R/gui-sliderMenu.R, R/gui-stableSlider.R, R/gui-stylizedFacts.R, R/interp-akimaInterp.R, R/interp-krigeInterp.R, R/interp-linearInterp.R, R/list-listFunctions.R, R/list-listIndex.R, R/matrix-colVec.R, R/matrix-gridVector.R, R/matrix-hilbert.R, R/matrix-inv.R, R/matrix-kron.R, R/matrix-norm.R, R/matrix-pascal.R, R/matrix-pdl.R, R/matrix-posDefinite.R, R/matrix-rk.R, R/matrix-tr.R, R/matrix-triang.R, R/matrix-tslag.R, R/matrix-vech.R, R/plot-acfPlot.R, R/plot-boxPlot.R, R/plot-histPlot.R, R/plot-interactivePlot.R, R/plot-qqPlot.R, R/plot-scalinglawPlot.R, R/plot-seriesPlot.R, R/plot-subPlot.R, R/plot-subStars.R, R/plot.R, R/print.R, R/rand-lcg.R, R/stats-maxdd.R, R/stats-rowStats.R, R/stats-sampleLmoments.R, R/test-NormalityTests.R, R/test-correlationTest.R, R/test-fHTEST.R, R/test-jbALM.R, R/test-jbLM.R, R/test-jbTable.R, R/test-ks2Test.R, R/test-locationTest.R, R/test-normalTest.R, R/test-scaleTest.R, R/test-varianceTest.R, R/utils-Heaviside.R, R/utils-Sys.putenv.R, R/utils-as.matrix.ts.R, R/utils-baseMethods.R, R/utils-characterTable.R, R/utils-colIds.R, R/utils-getS4.R, R/utils-symbolTable.R, R/utils-unitTest.R, R/utils-unitrootNA.R: code (header) beautified script files * NAMESPACE: namespace updated * R/builtin-gld.R, R/builtin-gss.R, R/dist-gh.R, R/dist-ghFit.R, R/dist-ghMoments.R, R/dist-ghRobMoments.R, R/dist-ghtRobMoments.R, R/dist-gld.R, R/dist-gldFit.R, R/dist-gldMode.R, R/dist-gldRobMoments.R, R/dist-gldRobust.R, R/dist-nigRobMoments.R, R/dist-nigRobust.R, R/dist-ssd.R, R/dist-ssdFit.R, R/stats-robMoments.R, R/stats-sampleLmoments.R, R/stats-sampleRobMoments.R: gh, gld and ssden distributions (new and extensions) added, Lmoments, robust stuff added * man/utils-characterTable.Rd: man page added * man/correlationTest.Rd, man/dist-gldRobMoments.Rd, man/test-correlationTest.Rd: same scripts renamed * man/Heaviside.Rd, man/NormalityTests.Rd, man/acfPlot.Rd, man/akimaInterp.Rd, man/basicStats.Rd, man/boxPlot.Rd, man/characterTable.Rd, man/colIds.Rd, man/colVec.Rd, man/decor.Rd, man/dist-distCheck.Rd, man/dist-gld.Rd, man/dist-gldFit.Rd, man/dist-nigRobMoments.Rd, man/distCheck.Rd, man/fHTEST.Rd, man/getS4.Rd, man/gridVector.Rd, man/hilbert.Rd, man/histPlot.Rd, man/interactivePlot.Rd, man/interp-linearInterp.Rd, man/inv.Rd, man/krigeInterp.Rd, man/kron.Rd, man/ks2Test.Rd, man/lcg.Rd, man/linearInterp.Rd, man/locationTest.Rd, man/matrix-tr.Rd, man/matrix-vech.Rd, man/matrixNorm.Rd, man/maxdd.Rd, man/pascal.Rd, man/pdl.Rd, man/positiveDefinite.Rd, man/qqPlot.Rd, man/rand-lcg.Rd, man/rk.Rd, man/rowStats.Rd, man/scaleTest.Rd, man/scalinglawPlot.Rd, man/seriesPlot.Rd, man/symbolTable.Rd, man/test-fHTEST.Rd, man/tr.Rd, man/triang.Rd, man/tslag.Rd, man/utild-Heaviside.Rd, man/utild-colIds.Rd, man/utild-decor.Rd, man/utild-getS4.Rd, man/utils-Heaviside.Rd, man/utils-colIds.Rd, man/utils-decor.Rd, man/utils-getS4.Rd, man/varianceTest.Rd, man/vec.Rd: some scripts renamed * man/utild-Heaviside.Rd, man/utild-colIds.Rd, man/utild-decor.Rd, man/utild-getS4.Rd, man/utils-symbolTable.Rd: utils script renames * man/stats-basicStats.Rd, man/stats-maxdd.Rd, man/stats-rowStats.Rd, man/stats-sampleLMoments.Rd, man/stats-sampleRobMoments.Rd: stats scripts renames * man/plot-acfPlot.Rd, man/plot-boxPlot.Rd, man/plot-histPlot.Rd, man/plot-interactivePlot.Rd, man/plot-qqPlot.Rd, man/plot-scalinglawPlot.Rd, man/plot-seriesPlot.Rd: plot script renames * man/interp-akimaInterp.Rd, man/interp-krigeInterp.Rd: interp script renames * man/test-ks2Test.Rd, man/test-locationTest.Rd, man/test-normalityTests.Rd, man/test-scaleTest.Rd, man/test-varianceTest.Rd: test-script renames * man/matrix-colVec.Rd, man/matrix-gridVector.Rd, man/matrix-hilbert.Rd, man/matrix-inv.Rd, man/matrix-kron.Rd, man/matrix-norm.Rd, man/matrix-pascal.Rd, man/matrix-pdl.Rd, man/matrix-posDefinite.Rd, man/matrix-rk.Rd, man/matrix-triang.Rd, man/matrix-tslag.Rd: matrix script renames 2009-11-13 wuertz * NAMESPACE, R/stats-sampleLmoments.R: namespace Lmoments added * R/stats-sampleLmoments.R: function to compute sample-Lmoments added, not yet documented * NAMESPACE: namespace robust moments added * R/dist-nigRobust.R: NIG robust moments added, not yet documented * R/dist-gldRobust.R: GLD robust moments added, not yet documented * NAMESPACE, R/dist-gld.R: gld functions added, not yet documented * R/dist-nigFit.R: misprint removed * NAMESPACE, R/stats-robMoments.R: functions for robust sample moments added, not yet documented * R/dist-nigFit.R: fitting methods added for nig 2009-09-28 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated DESCR and ChangeLog 2009-09-24 wuertz * R/dist-sgh.R, R/dist-snig.R: C implementation for NIG Distribution updated * man/00fBasics-package.Rd: text added to manual page 2009-09-11 wuertz * R/dist-nigFit.R, man/dist-nigFit.Rd: nigFit method argument added 2009-09-02 wuertz * R/dist-nigFit.R: maximum product spacings estimation added 2009-08-27 wuertz * src/nig.c: Bug in Kjersti Ass NIG Code fixed by Nikolai Eurich 2009-08-22 wuertz * NAMESPACE, R/dist-ghMoments.R, R/dist-nigFit.R: nig estimators added * R/builtin-gelGMM.R, R/builtin-gmmGMM.R: tabs replaced by spaces 2009-08-21 wuertz * R/dist-nigShapeTriangle.R, R/dist-nigStats.R: nig shape triangle added to stats script file * R/dist-nigStats.R: nig moments added * R/dist-nig.R: nig true moments added * NAMESPACE, R/dist-ghtMode.R, R/dist-nig.R, R/dist-nigMode.R, R/dist-stable.R, R/dist-stableMode.R: missing mode functions added * NAMESPACE: new NAMESPACE structure which should ease maintenance of packages. 2009-06-25 chalabi * DESCRIPTION, NAMESPACE: Merge branch 'devel-timeSeries' Conflicts: pkg/timeSeries/R/base-Extract.R pkg/timeSeries/R/timeSeries.R 2009-06-01 wuertz * NAMESPACE: nasmespace for recession lines updated * R/lines-recessionLines.R: function .recessionLines added 2009-05-18 wuertz * NAMESPACE, R/dist-stableFit.R, man/dist-DistributionFits.Rd: stable dist fit improved * man/as.matrix.ts.Rd.bak: .bak file removed * R/dist-stable.R, R/dist-stableFit.R, man/dist-DistributionFits.Rd: stable distribution nlminb added * R/plot-counts.R: a useful counts plot * R/plot-varpie.R: .varpiePlot, a useful plot for portfolio weights ... 2009-05-13 wuertz * R/list-listDescription.R: 2009-04-29 wuertz * NAMESPACE: NAMESPACE updated * R/dist-ghSlider.R, R/dist-hypSlider.R, R/dist-nigSlider.R, R/dist-stableSlider.R, R/gui-ghSlider.R, R/gui-hypSlider.R, R/gui-nigSlider.R, R/gui-stableSlider.R, R/list-listDescription.R, R/list-listFunctions.R, R/list-listIndex.R, R/listDescription.R, R/listFunctions.R, R/listIndex.R: gui and list functioms renamed in gui-foo and list-foo * R/Heaviside.R, R/HeavisideSlider.R, R/Sys.putenv.R, R/as.matrix.ts.R, R/baseMethods.R, R/basicStats.R, R/characterTable.R, R/colIds.R, R/colVec.R, R/decor.R, R/dist-distCheck.R, R/distCheck.R, R/getS4.R, R/gridVector.R, R/gui-HeavisideSlider.R, R/gui-returnSeries.R, R/gui-sliderMenu.R, R/gui-stylizedFacts.R, R/interactivePlot.R, R/jbALM.R, R/jbLM.R, R/jbTable.R, R/ks2Test.R, R/lcg.R, R/matrix-colVec.R, R/matrix-gridVector.R, R/matrix-tslag.R, R/maxdd.R, R/plot-interactivePlot.R, R/rand-lcg.R, R/returnSeriesGUI.R, R/rowStats.R, R/sliderMenu.R, R/stats-basicStats.R, R/stats-maxdd.R, R/stats-rowStats.R, R/stylizedFactsGUI.R, R/symbolTable.R, R/test-jbALM.R, R/test-jbLM.R, R/test-jbTable.R, R/test-ks2Test.R, R/tslag.R, R/unitTest.R, R/unitrootNA.R, R/utils-Heaviside.R, R/utils-Sys.putenv.R, R/utils-as.matrix.ts.R, R/utils-baseMethods.R, R/utils-characterTable.R, R/utils-colIds.R, R/utils-decor.R, R/utils-getS4.R, R/utils-symbolTable.R, R/utils-unitTest.R, R/utils-unitrootNA.R: utils and stats script renamed as stat-foo and utils-foo * R/akimaInterp.R, R/interp-akimaInterp.R, R/interp-krigeInterp.R, R/interp-linearInterp.R, R/krigeInterp.R, R/linearInterp.R, R/locationTest.R, R/test-locationTest.R: interpolation scripts renamed in interp-foo * R/color-Locator.R, R/color-Palettes.R, R/color-RGB.R, R/color-Table.R, R/colorLocator.R, R/colorPalette.R, R/colorTable.R, R/matrix-pdl.R, R/pdl.R, R/rgb.R: color script files renamed in color-foo * R/NormalityTests.R, R/correlationTest.R, R/dist-fDISTFIT.R, R/fDISTFIT.R, R/fHTEST.R, R/matrix-vech.R, R/normalTest.R, R/scaleTest.R, R/test-NormalityTests.R, R/test-correlationTest.R, R/test-fHTEST.R, R/test-normalTest.R, R/test-scaleTest.R, R/test-varianceTest.R, R/varianceTest.R, R/vec.R: test function scripts renamed in test-foo * R/hilbert.R, R/inv.R, R/kron.R, R/matrix-hilbert.R, R/matrix-inv.R, R/matrix-kron.R, R/matrix-norm.R, R/matrix-pascal.R, R/matrix-posDefinite.R, R/matrix-rk.R, R/matrix-tr.R, R/matrix-triang.R, R/matrixNorm.R, R/pascal.R, R/positiveDefinite.R, R/rk.R, R/tr.R, R/triang.R: matrix scripts renamed in matrix-foo.R to find them better * R/acfPlot.R, R/boxPlot.R, R/histPlot.R, R/qqPlot.R, R/scalinglawPlot.R, R/seriesPlot.R, R/subPlot.R, R/subStars.R: old plot files deleted * R/plot-acfPlot.R, R/plot-boxPlot.R, R/plot-histPlot.R, R/plot-qqPlot.R, R/plot-scalinglawPlot.R, R/plot-seriesPlot.R, R/plot-subPlot.R, R/plot-subStars.R: plot files renamed as plot-foo to find them better * R/builtin-gelGMM.R, R/builtin-gmmGMM.R, R/builtin-sandwichGMM.R: gmm builtin added for parameter estimation 2009-04-19 chalabi * DESCRIPTION: added explicit version number in Depends field for key packages 2009-04-02 chalabi * DESCRIPTION: more explicit depends and suggests field in DESC file. 2009-04-01 chalabi * DESCRIPTION: updated DESC file * R/qqPlot.R: 2009-02-09 wuertz * R/qqPlot.R: 2009-01-28 chalabi * DESCRIPTION: updated version number * DESCRIPTION: changed version number to match version on CRAN + 1 2009-01-12 chalabi * man/NormalityTests.Rd, man/correlationTest.Rd, man/fHTEST.Rd, man/ks2Test.Rd, man/locationTest.Rd, man/scaleTest.Rd, man/varianceTest.Rd: fixed warning with new Rd parser fBasics/NAMESPACE0000644000176200001440000001556714650724111013062 0ustar liggesusers################################################################################ ## Libraries ################################################################################ useDynLib("fBasics", .registration = TRUE, .fixes = "C_") ################################################################################ ## Imports ################################################################################ importFrom("grDevices", as.graphicsAnnot, cm.colors, col2rgb, colors, contourLines, gray.colors, heat.colors, rainbow, rgb, terrain.colors, topo.colors, xy.coords) importFrom("graphics", axis, barplot, box, boxplot, contour, grid, hist, layout, locator, matplot, mtext, panel.smooth, par, persp, pie, plot.new, plot.window, polygon, rect, rug, symbols, text, title, abline, lines, points) importFrom("stats", acf, ansari.test, approx, ar, arima, complete.cases, constrOptim, cor, cor.test, density, dnorm, dt, integrate, ks.test, lsfit, median, model.matrix, model.response, nlm, nlminb, numericDeriv, optim, optimize, pacf, pchisq, pf, pnorm, ppoints, qchisq, qf, qnorm, qqline, qqnorm, qt, residuals, rexp, rnorm, runif, sd, shapiro.test, spline, t.test, uniroot, var, na.omit, quantile) importFrom("utils", menu, packageVersion) importFrom("methods", getMethod, is, new, slot, show) importFrom("spatial", prmat, surf.gls) importFrom("timeDate", getRmetricsOptions, setRmetricsOptions, kurtosis, skewness) importFrom("timeSeries", timeSeries, is.timeSeries, as.timeSeries, isUnivariate, returns, description, drawdowns, colCumsums) ################################################################################ ## Exports ################################################################################ S3method("getModel", "default") S3method("print", "control") S3method("stdev", "default") S3method("termPlot", "default") S3method("volatility", "default") S3method(".plot", "histogram") S3method(".summary", "gel") S3method(".summary", "gmm") # To do before implementation: #The porfolio book needs: #export(.blue2redPalette) #export(.green2redPalette) #export(.blue2greenPalette) #export(.purple2greenPalette) #export(.blue2yellowPalette) #export(.cyan2magentaPalette) ## explicitly name exports, start with ## cat(ls("package:fBasics", all.names = TRUE), sep =",\n") ## then clean up; exportMethods("show") exportClasses("fDISTFIT", "fHTEST") export( as.timeSeries, # reexport, needs to be documented here timeSeries, # reexport, S4 generic, doesn't need doc. here returns, # reexport, S4 generic, doesn't need doc. here kurtosis, # from timeDate, reexport skewness, # from timeDate, reexport ## exports starting with '.' ## all not documented, it seems (regex search for aliases starting with '.') .acfPlot, # fRegression .contourPlot, # fAssets, fMultivar .firePlot, # fRegression .mrlPlot, # fRegression .pacfPlot, # fRegression .perspPlot, # fAssets, fMultivar .plot, # fRegression .predict, # fRegression .qStableFit, # StableEstim .residualsPlot, # fRegression .responsesPlot, # fRegression .sliderMenu, # fCopulae, fGarch and others .unirootNA, # fCopulae ## look like S3 methods but aren't get.lcgseed, set.lcgseed, # get/set an Rmetric option rnorm.lcg, # lcg stand for linear congruational generator rt.lcg, runif.lcg, ## (mostly) normally named exports acfPlot, adTest, akimaInterp, akimaInterpp, basicStats, box_, Boxcar, boxL, boxPercentilePlot, boxPlot, characterTable, cmPalette, colIds, "colIds<-", colorLocator, colorMatrix, colorTable, colVec, copyright, correlationTest, countFunctions, cumulatedPlot, cvmTest, dagoTest, decor, Delta, densityPlot, dgh, dght, dgld, dhyp, distCheck, .distCheck, # can't drop it since fGarch (< 4031.90) imported it explicitly; # difficult to ask everybody to require fGarch (>= 4031.90) divPalette, dmaxdd, dnig, drawdownPlot, dsgh, dsght, dsnig, dssd, focusPalette, getArgs, getDescription, getModel, getSlot, getTitle, ghFit, ghIQR, ghKurt, ghKURT, ghMean, ghMED, ghMode, ghMoments, ghSkew, ghSKEW, ghSlider, ghtFit, ghtIQR, ghtKurt, ghtKURT, ghtMean, ghtMED, ghtMode, ghtMoments, ghtSkew, ghtSKEW, ghtVar, ghVar, gldFit, gldIQR, gldKURT, gldMED, gldMode, gldSKEW, greyPalette, gridVector, heatPalette, Heaviside, hgrid, hilbert, histPlot, hypFit, hypIQR, hypKurt, hypKURT, hypMean, hypMED, hypMode, hypMoments, hypSkew, hypSKEW, hypSlider, hypVar, interactivePlot, inv, isPositiveDefinite, jarqueberaTest, jbTest, kendallTest, krigeInterp, kron, ks2Test, ksnormTest, lacfPlot, lillieTest, linearInterp, linearInterpp, listFunctions, listIndex, locationTest, logDensityPlot, makePositiveDefinite, maxddStats, monoPalette, nFit, nigFit, nigIQR, nigKurt, nigKURT, nigMean, nigMED, nigMode, nigMoments, nigShapeTriangle, nigSkew, nigSKEW, nigSlider, nigVar, norm2, normalTest, normIQR, normKURT, normMED, normSKEW, pacfPlot, pascal, pchiTest, pdl, pearsonTest, pgh, pght, pgld, phyp, pmaxdd, pnig, psgh, psght, psnig, pssd, qgh, qght, qgld, qhyp, qnig, qqghtPlot, qqgldPlot, qqnigPlot, qqnormPlot, qsgh, qsght, qsnig, qssd, qualiPalette, rainbowPalette, Ramp, rampPalette, returnPlot, returnSeriesGUI, rgh, rght, rgld, rhyp, rk, rmaxdd, rnig, rowAvgs, rowIds, "rowIds<-", rowKurtosis, rowMaxs, rowMins, rowProds, rowQuantiles, rowSds, rowSkewness, rowStats, rowStdevs, rowVars, rowVec, rsgh, rsght, rsnig, rssd, sampleIQR, sampleKURT, sampleLmoments, sampleMED, sampleSKEW, scaleTest, scalinglawPlot, seqPalette, seriesPlot, sfTest, sghFit, shapiroTest, Sign, snigFit, spearmanTest, ssdFit, stableFit, stableSlider, stdev, symbolTable, teffectPlot, termPlot, terrainPalette, tFit, timPalette, topoPalette, tr, triang, Triang, tsHessian, tslag, varianceTest, vec, vech, vgrid, volatility ) fBasics/NEWS.md0000644000176200001440000001431214660437451012735 0ustar liggesusers# fBasics 4041.97 - fixed the missing package qualifier in a link to another ackage. # fBasics 4032.96 - modified `interactivePlot` to allow also functions in argument `plotFun`. Previously `plotFun` had to be a character vector containing the names of the functions. Adjusted the documentation and added an example to this effect. - function `.distCheck` is defunct, use `distCheck` instead. To allow compatibility with older versions of packages that import fBasics (fGarch, fExtremes), `.distCheck` is exported but new code should not use it. - removed generic `.print`, its 'ssd' method, and the S3 registration for the latter, all unused. - clarified in the documentation for `histPlot`, `densityPlot` and `logDensityPlot` that argument `x` must be from class "timeSeries" (each of them starts with `stopifnot(is.timeSeries(x))`). Previously it was stated that argument `x` can be of any class that can be converted to "timeSeries" with `as.timeSeries`. - contrary to the documentation, the return values from the \code{p/d/q/r*} functions (such as `dhyp`, `phyp`, `qhyp`) for the generalised distributions do not contain attribute \code{"param"}. This is now documented correctly. - removed attribute 'control' from the return value of `rhyp` and similar functions. It was undocumented (or rather, the documentation stated that it is called "param"; in any case, wouldn't be usable in code). - argument 'alpha' of `dhyp`, `phyp`, `qhyp` and `rhyp` can now be also a vector of length 4 containing the four parameters. - `hypMode` now throws error if argument 'pm' doesn't have one of the allowed values. - the return values of `*Mean()`, `*Var()`, `*Skew()`, `*Kurt()`, `*Moments()`, where '*' is a distribution prefix (such as 'gh' or 'ght'), are now all named. Previously, only some of them were. - removed a number of inconsistencies in the documentation. # fBasics 4031.95 - now `characterTable` doesn't try to print/plot invalid non-ASCII UTF8 characters (fixes CRAN issue from around 2023-10-06). - `symbolTable` now plots more characters and issues warnings only for characters in `26:31` which are ignored according to `?points` but are actually plotted. - now using default R Random generators in tests, since the old ones were causing numerous warnings from tests. - removed deprecated function `listDescription`, use `utils::packageDescription` instead. - fixed a bug in `histPlot`, causing a puzzling error when argument `fit = FALSE`. # fBasics 4022.94 - `dagotest()` was returning NaN for the value of the test statistic based on kurtosis when the argument of a cube root was negative. Reported by Cameron Willden who suggested the fix. - the name of the first column of dataset `msft.dat` is not mangled any more (it was "X.Y..m..d" and now is "%Y-%m-%d", as in the input file), the rest of the dataset is as before. The file from which the dataset is created is now taken from an identical file in package `timeSeries`. - the `show` methods for classes `fHTEST` and `fDISTFIT` no longer print 'Description:' when slot `description = ""`. - many fitting functions, e.g., `nFit`, were inadvertently ignoring argument `description` by using `description = description()` instead of `description = description` in the call to `new` when creating the return value. - `correlationTest`, `pearsonTest`, `spearmanTest`, `kendallTest`, `ks2Test`, `scaleTest`, `varianceTest`, and `locationTest` now set slot `description` of the result to `""` by default. Previously they were setting it to the current date/time, with the consequence that the functions gave different objects at each run. Use `description = date()` in the call if you want the date. Similarly for `ksnormTest`, `shapiroTest`, `jarqueberaTest`, `dagoTest`, `normalTest`, `adTest`, `cvmTest`, `lillieTest`, `pchiTest`, `sfTest`, `jbTest`, `nFit`, `tFit`, `stableFit`, and many others, which had default setting `description = description()`. # fBasics 4021.93 - fixed CRAN warnings about a function declaration without a prototype, which is deprecated by modern C compilers. - further updates and improvements to the documentation. - website built with `pkgdown` is now linked to in file `DESCRIPTION`. # fBasics 4021.92 - new maintainer: Georgi N. Boshnakov. - in DESCRIPTION, moved `timeDate` and `timeSeries` from `Depends:` to `Imports:`. This necessitated changes in the examples and unit tests to get them run smoothly. The implications for end users and developers are discussed in separate sections below. - in NAMESPACE, now export selectively rather than with a generic pattern. No longer export symbols starting with a dot. Reexported some functions that can reasonably be expected when `fBasics` is attached (the list of such functions can be adjusted). - tidied up the documentation somewhat. There is now a `_pkgdown` file with the functions in the package organised by topic. Run `pkgdown::build_site()` on the source directory (or unpacked tarball) to build the site locally. - removed `.HedgeFund1` and `.HedgeFund2` - they were just used to build the dataset `HedgeFund`. ## Notes for users The changes are aimed at making the package easier to manage and reduce the side effects for users from attaching it. - Since packages `timeDate` and `timeSeries` are no longer attached by `library(fBasics)`, users may need to attach them explicitly, if they use functions from them directly. That said, some functions from `timeDate` and `timeSeries` are reexported by `fBasics`, so most users may not even notice any change. - Undocumented functions (mostly starting with a dot, '.') are no longer exported. If you believe that a non-exported and undocumented function should be exported, please open a bug report, giving your reasons. ## Notes for developers The notes for users apply to developers, as well. In addition: - We continue to export some (undocumented) functions starting with a '.' to avoid breaking packages that import `fBasics`. Please consider using documented equivalents, if possible. Alternatively, let us know that they are useful, so that we can document them. # fBasics 3042.89.2 and older versions See file `ChangeLog` for changes before 4021.92. fBasics/inst/0000755000176200001440000000000014650724111012602 5ustar liggesusersfBasics/inst/pkgdown.yml0000644000176200001440000000033214657145575015016 0ustar liggesuserspandoc: '3.3' pkgdown: 2.1.0 pkgdown_sha: ~ articles: {} last_built: 2024-08-14T15:21Z urls: reference: https://geobosh.github.io/fBasicsDoc/reference article: https://geobosh.github.io/fBasicsDoc/articles fBasics/inst/_pkgdown.yml0000644000176200001440000001312614650724111015140 0ustar liggesusersurl: https://geobosh.github.io/fBasicsDoc/ deploy: install_metadata: true template: bootstrap: 5 search: exclude: ['news/index.html'] reference: - title: "Overview of package fBasics" contents: - "fBasics-package" - fBasicsData - title: "Tests" contents: - correlationTest - ksnormTest - jbTest - shapiroTest - normalTest - jarqueberaTest - dagoTest - adTest - cvmTest - lillieTest - pchiTest - sfTest - pearsonTest - kendallTest - spearmanTest - ks2Test - locationTest - scaleTest - varianceTest - fHTEST - title: "Other statistical functions" contents: - pdl - basicStats - dmaxdd - pmaxdd - rmaxdd - maxddStats - rowStats - rowSds - rowVars - rowSkewness - rowKurtosis - rowMaxs - rowMins - rowProds - rowQuantiles - rowStdevs - rowAvgs - normMED - normIQR - normSKEW - normKURT - sampleMED - sampleIQR - sampleSKEW - sampleKURT - sampleLmoments - set.lcgseed - get.lcgseed - runif.lcg - rnorm.lcg - rt.lcg - volatility - title: "Distributions and distribution fitting" contents: - nFit - tFit - stableFit - "fDISTFIT-class" - distCheck - title: "Hyperbolic Distribution" contents: - dhyp - phyp - qhyp - rhyp - hypFit - hypMode - hypMean - hypVar - hypSkew - hypKurt - hypMoments - hypMED - hypIQR - hypSKEW - hypKURT - hypSlider - title: "Generalized Hyperbolic Distribution" contents: - dgh - pgh - qgh - rgh - ghFit - ghMode - ghMean - ghVar - ghSkew - ghKurt - ghMoments - ghMED - ghIQR - ghSKEW - ghKURT - ghSlider - title: "Generalized Hyperbolic Student-t" contents: - dght - pght - qght - rght - ghtFit - ghtMode - ghtMean - ghtVar - ghtSkew - ghtKurt - ghtMoments - ghtMED - ghtIQR - ghtSKEW - ghtKURT - title: "Generalized Lambda Distribution" contents: - dgld - pgld - qgld - rgld - gldFit - gldMode - gldMED - gldIQR - gldSKEW - gldKURT - title: "Normal Inverse Gaussian Distribution" contents: - dnig - pnig - qnig - rnig - nigFit - nigMode - nigMean - nigVar - nigSkew - nigKurt - nigMED - nigIQR - nigSKEW - nigKURT - nigShapeTriangle - nigSlider - title: "Standardized distributions" contents: - dsgh - psgh - qsgh - rsgh - sghFit - dsght - psght - qsght - rsght - dsnig - psnig - qsnig - rsnig - snigFit - title: "Spline smoothed distribution" contents: - dssd - pssd - qssd - rssd - ssdFit - title: "Vector and matrix utilities" contents: - colVec - rowVec - gridVector - hilbert - inv - kron - norm2 - pascal - isPositiveDefinite - makePositiveDefinite - rk - tr - triang - Triang - tslag - vec - vech - title: "Plotting" contents: - acfPlot - pacfPlot - lacfPlot - teffectPlot - boxPlot - boxPercentilePlot - histPlot - densityPlot - logDensityPlot - qqnormPlot - qqnigPlot - qqghtPlot - qqgldPlot - returnSeriesGUI - scalinglawPlot - seriesPlot - cumulatedPlot - returnPlot - drawdownPlot - stableSlider - interactivePlot - title: "Interpolation" contents: - linearInterp - linearInterpp - krigeInterp - akimaInterp - akimaInterpp - title: "Special functions" contents: - Heaviside - Sign - Delta - Boxcar - Ramp - tsHessian - title: "Colour" contents: - colorLocator - colorMatrix - rainbowPalette - heatPalette - terrainPalette - topoPalette - cmPalette - greyPalette - timPalette - rampPalette - seqPalette - divPalette - qualiPalette - focusPalette - monoPalette - characterTable - colorTable - symbolTable - decor - hgrid - vgrid - boxL - box_ - copyright - title: "S4 utilities" contents: - getModel - getTitle - getDescription - getSlot - getArgs - title: "Package utilities" contents: - listIndex - listFunctions - countFunctions - print.control - title: "Deprecated functions" contents: - "fBasics-deprecated" - title: "Reexported functions" desc: > The following functions are re-exported from packages 'timeDate' and 'timeSeries', for convenience. If you need more functionality from those packages, run 'library()' in your session. contents: - fBasics_reexports - title: "Unclassified" desc: > TODO: these are generic functions for the corresponding base R ones (which serve as default methods). Seem abandoned. Remove? contents: - stdev - termPlot - title: "S+ compatibility" contents: - colIds - rowIds fBasics/inst/unitTests/0000755000176200001440000000000014650724111014604 5ustar liggesusersfBasics/inst/unitTests/runit.distCheck.R0000644000176200001440000000302314650724111017766 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # distCheck Checks consistency of distributions ################################################################################ test.distCheck <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colorTable.R0000644000176200001440000000312714263246022020160 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # colorTable Shows a table of plot color codes ################################################################################ test.colorTable = function() { # Color Table: args(colorTable) par(mfrow = c(1, 1)) colorTable() # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.plot.R0000644000176200001440000000440414263246022017047 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: INTERNAL USED PLOT FUNCTIONS: # .residualsPlot Returns a residual series plot # .acfPlot Returns a autocorrelation function plot # .pacfPlot Returns a partial ACF plot # .mrlPlot Returns a mean residual life plot # FUNCTION: INTERNAL USED BIVARIATE PLOT FUNCTIONS: # .responsesPlot Returns a response series plot # .firePlot Returns a fitted values vs.residuals plot # FUNCTION: INTERNAL THREE-DIMENSIONAL PLOT UTILITIES: # .circlesPlot Returns a circles plot indexing 3rd variable # .perspPlot Returns a perspective plot in 2 dimensions # .contourPlot Returns a contour plot in 2 dimensions # .histStack Returns a stacked histogram plot ################################################################################ test.plot <- function() { # Plot: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.HyperbolicDistribution.R0000644000176200001440000001275114650724111022575 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # dgh Returns density for generalized hyperbolic DF # pgh Returns probability for generalized hyperbolic DF # qgh Returns quantiles for generalized hyperbolic DF # rgh Returns random variates for generalized hyperbolic DF # FUNCTION: DESCRIPTION: # dhyp Returns density for hyperbolic DF # phyp Returns probability for hyperbolic DF # qhyp Returns quantiles for hyperbolic DF # rhyp Returns random variates for hyperbolic DF # hypMode Computes the hyperbolic mode # FUNCTION: DESCRIPTION: # dnig Returns density for inverse Gaussian DF # pnig Returns probability for for inverse Gaussian DF # qnig Returns quantiles for for inverse Gaussian DF # rnig Returns random variates for inverse Gaussian DF # FUNCTION: DESCRIPTION: # hypSlider Displays hyperbolic distribution function # nigSlider Displays normal inverse Gausssian distribution function ################################################################################ test.gh = function() { # gh() Distribution: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) test = fBasics::distCheck("gh", alpha = 1.3, beta = 0.3, delta = 1.7, mu = 0.2, lambda = 0.8, n = 2000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # Return Value: return() } # ------------------------------------------------------------------------------ test.hyp = function() { # hyp() Distribution - Parameterization 1: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) test = fBasics::distCheck("hyp", alpha = 1.2, beta = 0.2, delta = 1.9, mu = 0.1, pm = "1", n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # hyp() Distribution - Parameterization 2: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) test = fBasics::distCheck("hyp", alpha = 0.9, beta = -0.3, delta = 1.4, mu = -0.1, pm = "2", n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # hyp() Distribution - Parameterization 3: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) fBasics::distCheck("hyp", alpha = 0.9, beta = -0.3, delta = 1.4, mu = -0.1, pm = "3", n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # hyp() Distribution - Parameterization 4: if (FALSE) { RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") fBasics::distCheck("hyp", alpha = 1.6, beta = -0.3, delta = 1.4, mu = 0.1, pm = "4", n = 1000, robust = FALSE) # CHECK print(test) checkTrue(mean(test) == 1) } # Return Value: return() } # ------------------------------------------------------------------------------ test.nig = function() { # nig() Distribution: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) test = fBasics::distCheck("nig", alpha = 2.1, beta = 0.1, delta = 1.5, mu = -0.1, n = 1000, robust = FALSE) print(test) checkTrue(mean(test) == 1) # Return Value: return() } # ------------------------------------------------------------------------------ test.hypSlider = function() { # Arguments ? # hypSlider() # Try: # hypSlider() NA # Return Value: return() } # ------------------------------------------------------------------------------ test.nigSlider = function() { # Arguments ? # nigSlider # Try: # nigSlider() NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.rowStats.R0000644000176200001440000000436014433645642017732 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: ROW AND COLUMN STATISTICS: # rowStats Computes sample statistics by row # rowAvgs Computes sample mean by row # rowVars Computes sample variance by row # rowStdevs Computes sample variance by row # rowSkewness Computes sample skewness by row # rowKurtosis Computes sample kurtosis by row # rowCumsums Computes sample cumulated sums by row ################################################################################ test.colStats = function() { # colStats Computes sample statistics by column # colAvgs Computes sample mean by column # colVars Computes sample variance by column # colSkewness Computes sample skewness by column # colKurtosis Computes sample kurtosis by column # colCumsums Computes sample cumulated sums by column # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.ReturnSeriesBasics.R0000644000176200001440000001237314650724111021654 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: TAILORED PLOT FUNCTIONS: # seriesPlot Returns a tailored return series plot # histPlot Returns a tailored histogram plot # densityPlot Returns a tailored kernel density estimate plot # qqnormPlot Returns a tailored normal quantile-quantile plot # FUNCTION: BASIC STATISTICS: # basicStats Returns a basic statistics summary # FUNCTION: DESCRIPTION: # distCheck Checks consistency of distributions # FUNCTION: SPLUS FUNCTIONALITY: # stdev S-PLUS: Returns the standard deviation of a vector ################################################################################ test.seriesPlot = function() { # Description: # Returns a tailored return series plot # Time Series: data("MSFT", package = "timeSeries", envir = environment()) tS = MSFT[, "Close"] # Series Plot: par(mfrow = c(2, 1)) seriesPlot(tS) seriesPlot(tS) points(tS, col = "red", pch = 19, cex = 0.7) # Return Value: return() } # ------------------------------------------------------------------------------ test.histPlot = function() { # Description: # Returns a tailored histogram plot # Time Series: data("MSFT", package = "timeSeries", envir = environment()) tS = MSFT[, "Close"] # Histogram Plot: par(mfrow = c(1, 1)) histPlot(tS) histPlot(tS, fit = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.densityPlot = function() { # Description: # Returns a tailored kernel density estimate plot # Time Series: data("MSFT", package = "timeSeries", envir = environment()) tS = MSFT[, "Close"] # Density Plot: par(mfrow = c(1, 1)) densityPlot(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.qqnormPlot = function() { # Description: # Returns a tailored normal quantile-quantile plot # Time Series: data("MSFT", package = "timeSeries", envir = environment()) tS = MSFT[, "Close"] # Quantile Plot: par(mfrow = c(1, 1)) qqnormPlot(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.basicStats = function() { # Description: # Returns a basic statistics summary # Time Series: data("MSFT", package = "timeSeries", envir = environment()) tS = MSFT Close = tS[, "Close"] # Univariate timeSeries - basicStats(x, ci = 0.95) basicStats(Close) # basicStats(as.numeric(Close)) basicStats(as.matrix(Close)) basicStats(as.data.frame(Close)) basicStats(as.ts(Close)) # Multivariate - timeSeries - basicStats(x, ci = 0.95) basicStats(tS) basicStats(as.matrix(tS)) basicStats(as.data.frame(tS)) basicStats(as.ts(tS)) # Return Value: return() } # ------------------------------------------------------------------------------ test.distCheck = function() { # Description: # Checks consistency of distributions # Arguments: # .distCheck(fun = "norm", n = 1000, seed = 4711, ...) # Normal Distribution Check: fBasics::distCheck() # Return Value: return() } # ------------------------------------------------------------------------------ test.stdev = function() { # Description: # S-PLUS: Returns the standard deviation of a vector # Time Series: data("MSFT", package = "timeSeries", envir = environment()) tS = MSFT # stdev - Univariate: tU = tS[, 1] # S-Plus Compatible: stdev(tU) ## stdev(as.numeric(tU)) ## CHECK !!! stdev(as.vector(tU)) stdev(as.ts(tU)) # Base R: sd(tU) ## sd(as.numeric(tU)) ## CHECK !!! sd(as.vector(tU)) sd(as.ts(tU)) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.gridVector.R0000644000176200001440000000325214263246022020201 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # gridVector Creates from two vectors rectangular grid coordinates ################################################################################ test.gridVector = function() { # Grid Vector - gridVector(x, y) gridVector(1:3, 1:3) as.data.frame(gridVector(1:3, 1:3)) as.matrix(as.data.frame(gridVector(1:3, 1:3))) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/HeavisideSlider.R0000644000176200001440000000312014263246022017767 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: HEAVISIDE AND RELATED FUNCTIONS: # .heavisideSlider Displays Heaviside and related functions ################################################################################ test.heavisideSlider = function() { # Slider: # .heavisideSlider() NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.listIndex.R0000644000176200001440000000306514263246022020036 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # listIndex Extracts R package index ################################################################################ test.listIndex <- function() { # List Functions: print(listIndex(package = "fBasics")) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.print.R0000644000176200001440000000320514263246022017223 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: CONTROL ATTRIBUTES: # print.control Prints unlisted control attributes # FUNCTION: DESCRIPTION: # .print Used in regression package ################################################################################ test.print <- function() { # Print: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.krigeInterp.R0000644000176200001440000000305614263246022020356 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # krigeInterp Kriges irregularly distributed data points ################################################################################ test.krigeInterp <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.rgb.R0000644000176200001440000000452214263246022016644 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: UTILITIES: # .asRGB Converts any R color to RGB (red/green/blue) # .chcode Changes from one to another number system # .hex.to.dec Converts heximal numbers do decimal numbers # .dec.to.hex Converts decimal numbers do heximal numbers ################################################################################ test.hexCode <- function() { # .chcode Changes from one to another number system # .hex.to.dec Converts heximal numbers do decimal numbers # .dec.to.hex Converts decimal numbers do heximal numbers # Change from one to another number system # .chcode(b, base.in = 2, base.out = 10, digits="0123456789ABCDEF") # Convert heximal numbers do decimal numbers # .hex.to.dec(b) fBasics:::.hex.to.dec("AA") # Convert decimal numbers do heximal numbers # .dec.to.hex(b) fBasics:::.dec.to.hex(170) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.TwoSampleTests.R0000644000176200001440000001347214650724111021034 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DISTRIBUTIONAL TESTS: # ks2Test Performs a two sample Kolmogorov-Smirnov test # FUNCTION: LOCATION TESTS: # locationTest Performs locations tests on two samples # .tTest Unpaired t test for differences in mean # .kw2Test Kruskal-Wallis test for differences in locations # FUNCTION: VARIANCE TESTS: # varianceTest Performs variance tests on two samples # .varfTest F test for differences in variances # .bartlett2Test Bartlett's test for differences in variances # .fligner2Test Fligner-Killeen test for differences in variances # FUNCTION: SCALE TESTS: # scaleTest Performs scale tests on two samples # .ansariTest Ansari-Bradley test for differences in scale # .moodTest Mood test for differences in scale # dansariw Returns density of the Ansari W statistic # pansariw Returns probabilities of the Ansari W statistic # qansariw Returns quantiles of the Ansari W statistic # FUNCTION: CORRELATION TESTS: # correlationTest Performs correlation tests on two samples # pearsonTest Pearson product moment correlation coefficient # kendallTest Kendall's tau correlation test # spearmanTest Spearman's rho correlation test ################################################################################ test.distributionTest = function() { # Data: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) X = rnorm(100) Y = rt(50, df = 3) # Two Sample Kolmogorov-Smirnov Test: TEST = ks2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.locationTests = function() { # Data: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) X = rnorm(100) Y = rt(50, df = 3) # Location t-Test: TEST = fBasics:::.tTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Location kw2-Test: TEST = fBasics:::.kw2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.varianceTests = function() { # Data: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) X = rnorm(100) Y = rt(50, df = 3) # Variance F-Test: TEST = fBasics:::.varfTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Variance Bartlett-Test: TEST = fBasics:::.bartlett2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Variance Fligner-Test: TEST = fBasics:::.fligner2Test(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.scaleTests = function() { # Data: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) X = rnorm(100) Y = rt(50, df = 3) # Scale Ansari-Test: TEST = fBasics:::.ansariTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Scale Mood-Test: TEST = fBasics:::.moodTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.correlationTests = function() { # Data: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) X = rnorm(100) Y = rt(100, df = 3) # Correlation Pearson-Test: TEST = pearsonTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Correlation Kendall-Test: TEST = kendallTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Correlation Spearman-Test: TEST = spearmanTest(X, Y) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.matrixAddon.R0000644000176200001440000002344114263246022020345 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # GENERATION: DESCRIPTION: # matrix R Creates a matrix from the given set of values # diag R Creates a diagonal matrix or extracts diagonals # triang M Extracs the lower tridiagonal part from a matrix # Triang M Extracs the upper tridiagonal part from a matrix # pascal M Creates a Pascal matrix # hilbert M Creates a Hilbert matrix # colVec M Creates a column vector from a data vector # rowVec M Creates a row vector from a data vector # as.matrix R Attempts to turn its argument into a matrix # is.matrix R Tests if its argument is a (strict) matrix # isPositiveDefinite M Checks if the matrix X is positive definite # makePositiveDefinite M Forces the matrix x to be positive definite # dimnames R Retrieves or sets the dimnames of an object # colnames|rownames R Retrieves or sets the row or column names # colIds|rowIds M ... use alternatively # colIds<-|rowIds<- M ... for assignments # SUBSETS: DESCRIPTION: # dim R Returns the dimension of a matrix object # ncol|nrow R Counts columns|rows of a matrix object # length R Counts elements of a matrix object # "["|"[[" R Subsets a matrix object # (Arith) R Elementwise Arithmetic: + - * / # (Lops) R Elementwise logical Ops: > < >= <= == != # cbind|rbind R Augments a matrix object by columns|rows # na.omit R Removes NA from a matrix object # BASIC STATISTICS: DESCRIPTION: # var R Returns the variance matrix # cov R Returns the covariance matrix # col|rowStats B calculates column|row statistics # col|rowMeans R calculates column|row means # col|rowAvgs B calculates column|row averages # col|rowVars B calculates column|row variances # col|rowStdevs B calculates column|row standard deviations # col|rowSkewness B calculates column|row skewness # col|rowKurtosis B calculates column|row kurtosis # col|rowCumsums B calculates column|row cumulated sums # LINEAR ALGEBRA: DESCRIPTION: # t R Returns the transposed matrix # det R Returns the determinant of a matrix # inv M returns the inverse of a matrix, synonyme # chol2inv R Returns the inverse of a matrix # norm M returns the norm of a matrix # rk M returns the rank of a matrix # tr M returns the trace of a matrix # %*% R Returns the product of two matrices # %x% R Returns the Kronecker product # kron S returns the Kronecker product # vec M is the operator that stacks a matrix # vech M is the operator that stacks the lower triangle # MORE LINEAR ALGEBRA: DESCRIPTION: # chol R Returns the Cholesky factor matrix # eigen R Returns eigenvalues and eigenvectors # svd R Returns the singular value decomposition # kappa R Returns the condition number of a matrix # qr R Returns the QR decomposition of a matrix # solve R Solves a system of linear equations # backsolve R ... use when the matrix is upper triangular # forwardsolve R ... use when the matrix is lower triangular # TIME SERIES DESCRIPTION: # tslag R Lagged/leading vector/matrix of selected orders # .tslag1 Internal Function used by tslag # pdl R Regressor matrix for polynomial distributed lags # NOTES: WHERE YOU FIND THE FUCTIONS? # R Basic R Package # B Rmetrics fBasics Package # M This Rmetrics fMultivar Package ################################################################################ test.creation = function() { # Create Pascal Matrix: P = pascal(3) P # Create lower triangle matrix L = triang(P) L # Extract diagonal part diag(P) # Return Value: return() } # ------------------------------------------------------------------------------ test.mathOps = function() { # Create Pascal Matrix: P = pascal(3) P # Add/Subtract/Multiply/Divide: X = P # Multiply matrix with a constant 3 * X # Multiply two matrices elementwise X * P # Multiplies rows/columns of a matrix by a vector X %*% diag(P) diag(P) %*% X # Return Value: return() } # ------------------------------------------------------------------------------ test.subsets = function() { # Create Pascal Matrix: P = pascal(3) P # Operate on Subsets of a Matrix: n = 3 i = 2 j = 3 D = diag(1:3) # Return the dimension of a matrix dim(P) # Get the last colum of a matrix P[, ncol(P)] # Delete a column of a matrix P[, -i] # Permute the columns of a matrix P[c(3, 1, 2), ] # Augments matrix horizontally cbind(P, D) # Return Value: return() } # ------------------------------------------------------------------------------ test.apply = function() { # Apply a function to all Elements of a Matrix: # Create Pascal Matrix: P = pascal(3) P # Return square root for each element sqrt(P) # Exponentiate the matrix elementwise exp(P) # Compute the median of each column apply(P, 2, "median") # Test on all elements of a matrix all( P > 2 ) # test on any element in a matrix any( P > 2 ) # Return Value: return() } # ------------------------------------------------------------------------------ test.moreOperations = function() { # More Matrix Operations: # Create Pascal Matrix: P = pascal(3) P # Create Diagonal Matrix: D = diag(1:3) # Return the product of two matrices P %*% D # Return the Kronecker Product P %x% D # Return the transposed matrix t(P) # Return the inverse of a matrix inv(P) # Return the norm of a matrix norm(P) # Return the determinante of a matrix det(P) # Return the rank of a matrix rk(P) # Return trace of a matrix tr(P) # Return the variance matrix var(P) # Return the covariance matrix cov(P) # Stack a matrix vec(P) # Stack the lower triangle vech(P) # Return Value: return() } # ------------------------------------------------------------------------------ test.linearAlgebra = function() { # More Linear Algebra: # Create Pascal Matrix: P = pascal(3) P # Example Matrix and Vector X = P b = c(1, 2, 3) # Return the Cholesky factor matrix chol(X) # Return eigenvalues and eigenvectors eigen(X) # Return the singular value decomposition svd(X) # Return the condition number of a matrix kappa(X) # Return the QR decomposition of a matrix qr(X) # Solve a system of linear equations # ... use backsolve when the matrix is upper triangular # ... use forwardsolve when the matrix is lower triangular solve(X, b) backsolve(Triang(X), b) solve(Triang(X), b) forwardsolve(triang(X), b) solve(triang(X), b) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.StylizedFacts.R0000644000176200001440000001471014266254702020671 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # acfPlot Displays autocorrelations function plot # pacfPlot Displays partial autocorrelation function plot # teffectPlot Estimates and plots the Taylor effect # lmacfPlot Estimates and plots the long memory ACF # lacfPlot Plots lagged autocorrelations # .logpdfPlot Returns a pdf plot on logarithmic scale(s) # .qqgaussPlot Returns a Gaussian quantile-quantile plot # scalinglawPlot Evaluates and plots scaling law behavior ################################################################################ test.acfPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # acfPlot - acfPlot(x = msft.ret) # acfPlot - acfPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.pacfPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # pacfPlot - pacfPlot(x = msft.ret) # pacfPlot - pacfPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.teffectPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # teffectPlot - teffectPlot(x = msft.ret) # teffectPlot - teffectPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.lmacfPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # lmacfPlot - ## lmacfPlot(x = abs(msft.ret), type = "acf") ## lmacfPlot(x = abs(msft.ret), type = "hurst") # ... CHECK ACF OF RETURNS # lmacfPlot - ## lacfPlot(x = msft, n = 4, type = "values") ## CHECK !!! # lmacfPlot - ## lmacfPlot(x = abs(msft.ret), type = "acf", labels = FALSE) ## lmacfPlot(x = abs(msft.ret), type = "hurst", labels = FALSE) # ... CHECK ACF OF RETURNS # lmacfPlot - ## lacfPlot(x = msft, n = 4, labels = FALSE, type = "values") ## CHECK !!! # Return Value: return() } # ------------------------------------------------------------------------------ test.lacfPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # Return Value: return() } # ------------------------------------------------------------------------------ test.logpdfPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # logpdfPlot - fBasics:::.logpdfPlot(x = msft.ret, labels = FALSE) fBasics:::.logpdfPlot(x = msft.ret, type = "log-log") # ... CHECK WARNINGS # ... CHECK COLORS # logpdfPlot - fBasics:::.logpdfPlot(x = msft.ret, labels = FALSE) fBasics:::.logpdfPlot(x = msft.ret, type = "log-log", labels = FALSE) # ... CHECK WARNINGS # ... CHECK COLORS # Return Value: return() } # ------------------------------------------------------------------------------ test.qqgausPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # qqgaussPlot - fBasics:::.qqgaussPlot(x = msft.ret) # qqgaussPlot - fBasics:::.qqgaussPlot(x = msft.ret, labels = FALSE) # Return Value: return() } # ------------------------------------------------------------------------------ test.scalinglawPlot = function() { # MSFT Data: data("MSFT", package = "timeSeries", envir = environment()) msft.dat = MSFT msft = msft.dat[, 1] msft.vol = msft.dat[ , 5]/10^6 msft.ret = timeSeries::returns(msft) # Graph Frame: par(mfrow = c(1, 1)) # scalinglawPlot - scalinglawPlot(x = msft.ret, span = 4) # ... CHECK COLORS # scalinglawPlot - scalinglawPlot(x = msft.ret, span = 4, labels = FALSE) # ... CHECK COLORS # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.DistributionFits.R0000644000176200001440000001747514650724111021412 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # 'fDISTFIT' S4 Class representation # print.fDISTFIT Prints Results from a Fitted Distribution # FUNCTION: NORMAL AND T DISTRIBUTION: # nFit Fits parameters of a Normal density # tFit Fits parameters of a Student-t density # FUNCTION: STABLE DISTRIBUTION: # stableFit Fits parameters of a stable density # FUNCTION: GENERALIZED DISTRIBUTION: # ghFit Fits parameters of a generalized hyperbolic density # hypFit Fits parameters of a hyperbolic density # nigFit Fits parameters of a normal inverse Gaussian density ################################################################################ test.nFit <- function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate normal random variates: ## GNB: was ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) s = rnorm(n = 2000, mean = 1, sd = 0.5) # Fit: cat("nFit():\n") print(ans <- nFit(x = s)) # Precision within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 1.0)/1.0 < 0.10 ), ( (ans@fit$estimate[2] - 0.5)/0.5 < 0.10 )) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.tFit = function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate random variates t(4): ## GNB: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) s = rt(n = 2000, df = 4) # Fit: ans = tFit(x = s, df = 2*var(s)/(var(s)-1), trace = FALSE) # Precision of df within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 4.0)/4.0 < 0.10 )) print(ans) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.ghFit = function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate random variates: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) s = rgh(n = 2000, alpha = 0.8, beta = 0.2, delta = 2, mu = -0.4, lambda = 1) # Fit: ans = ghFit(x = s, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = 1, trace = FALSE) # Precision of parameters within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 0.8)/( 0.8) < 0.10 ), ( (ans@fit$estimate[2] - 0.2)/( 0.2) < 0.10 ), ( (ans@fit$estimate[3] - 2.0)/( 2.0) < 0.10 ), ( (ans@fit$estimate[4] + 0.4)/(-0.4) < 0.10 ), ( (ans@fit$estimate[5] - 2.0)/( 2.0) < 0.10 )) print(ans) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.hypFit = function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate normal random variates: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) s = rhyp(n = 2000, alpha = 1.5, beta = 0.8, delta = 0.5, mu = -1) # Fit: ans = hypFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), trace = FALSE) # Precision of parameters within 10% ? relErrorTest = c( ( (ans@fit$estimate[1] - 1.5)/( 1.5) < 0.10 ), ( (ans@fit$estimate[2] - 0.8)/( 0.8) < 0.10 ), ( (ans@fit$estimate[3] - 0.5)/( 0.5) < 0.10 ), ( (ans@fit$estimate[4] + 1.0)/(-1.0) < 0.10 )) print(ans) print(relErrorTest) checkTrue(as.logical(mean(relErrorTest))) # Return Value: return() } # ------------------------------------------------------------------------------ test.nigFit <- function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate normal random variates: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4712) th.0 <- c(alpha = 1.5, beta = -0.7, delta = 0.5, mu = -1.0) s <- do.call(rnig, c(list(n = 2000), th.0)) # Fit {includes plot !} print(ans <- nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), trace = FALSE)) # Precision of parameters within 10% / 35% ? est <- ans@fit$estimate relErr <- (est - th.0) / th.0 checkTrue(all(abs(relErr) < 0.35))# 35% ## 10% / 35$ is silly, rather use SE scale: print(SE <- ans@fit$se.coef) print(cbind(th.0, est, SE))# NaN SE[is.na(SE)] <- .1 # arbitrary cat("t-ratios:\n"); print(t. <- (est - th.0)/ SE) checkTrue(all(abs(t.) < 6))## 6 is too large, but have -5.476 for beta # Return Value: return() } # ------------------------------------------------------------------------------ test.stableFit <- function() { # Graph Frame: par(mfrow = c(1, 1)) # Simulate stable random variates: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4713) th.0 <- c(alpha=1.8, beta=0.3, gamma = 1, delta = 0.1) s <- do.call(stabledist::rstable, c(list(n = 500), th.0)) # Fit {includes plot !} cat("stableFit():\n") print(ans <- stableFit(x = s, alpha = 1.5)) # Precision of parameters within 5% / 10% / ...? est <- ans@fit$estimate arelErr <- abs(relErr <- (est - th.0) / th.0) print(cbind(th.0, est, relErr)) checkTrue(all(arelErr[c("alpha","gamma")] < 0.05) && arelErr[["beta"]] < 0.20 && arelErr[["delta"]] < 0.50) # MLE Fit: if (FALSE) { # as this takes rather too long time ... print(system.time( ans <- stableFit(x = s, alpha = 1.5, type = "mle", trace = TRUE) ## .mleStableFit(s, 1.75, 0, 1, 0) )) ## user {lynne, 2012-09} ## 159.7 # The result would be: print(ans) ## Precision of parameters within 5% / 10% / ...? est <- ans@fit$estimate arelErr <- abs(relErr <- (est - th.0) / th.0) print(cbind(th.0, est, relErr)) checkTrue(all(arelErr[1:3] < 0.02) && arelErr[["delta"]] < 0.42) } # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.characterTable.R0000644000176200001440000000321514650724111020774 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # characterTable Shows a table of character codes ################################################################################ ## test.characterTable <- ## function() ## { ## # Character Table: ## args(characterTable) ## par(mfrow = c(1, 1)) ## characterTable() ## ## # Return Value: ## return() ## } ################################################################################ fBasics/inst/unitTests/runit.interactivePlot.R0000644000176200001440000000475214263246022021253 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: PLOT UTILITIES: # interactivePlot Plots several graphs interactively ################################################################################ test.interactivePlot <- function() { # interactivePlot(x, choices = paste("Plot", 1:9), # plotFUN = paste("plot.", 1:9, sep = ""), which = "all", ...) # Test Plot Function: testPlot = function(x, which = "all", ...) { # Plot Function and Addons: plot.1 <<- function(x, ...) plot(x, ...) plot.2 <<- function(x, ...) acf(x, ...) plot.3 <<- function(x, ...) hist(x, ...) plot.4 <<- function(x, ...) qqnorm(x, ...) # Plot: interactivePlot(x, choices = c("Series Plot", "ACF", "Histogram", "QQ Plot"), plotFUN = c("plot.1", "plot.2", "plot.3", "plot.4"), which = which, ...) # Return Value: invisible() } # Plot: par(mfrow = c(2, 2), cex = 0.7) testPlot(rnorm(500)) # Reorder 2: par(mfrow = c(2, 1), cex = 0.7) testPlot(rnorm(500), which = c(1, 2)) # Try: # par(mfrow = c(1,1)); testPlot(rnorm(500), which = "ask") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/Makefile0000644000176200001440000000042014660437567016261 0ustar liggesusersPKG=fBasics TOP=../.. SUITE=doRUnit.R R=R all: inst test inst: # Install package -- but where ?? -- will that be in R_LIBS ? cd ${TOP}/..;\ ${R} CMD INSTALL ${PKG} test: # Run unit tests export RCMDCHECK=FALSE;\ cd ${TOP}/tests;\ ${R} --vanilla --slave < ${SUITE} fBasics/inst/unitTests/runit.subPlot.R0000644000176200001440000000316614263246022017525 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .emptyPlot Creates an empty plot page # .subPlot Creates a sub plot # .cnvrt.coords Converts coordinates ################################################################################ test.subplot <- function() { # Plot: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.NormalityTests.R0000644000176200001440000001170214650724111021071 0ustar liggesusers# This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2006, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: NORMALITY TESTS: # normalTest Test suite for normality tests # ksnormTest One sample Kolmogorov-Smirnov normality test # shapiroTest Shapiro-Wilk normality test # jarqueberaTest Jarque-Bera normality test # dagoTest D'Agostino normality test # FUNCTION: FROM NORTEST PACKAGE: # adTest Anderson-Darling normality test # cvmTest Cramer-von Mises normality test # lillieTest Lilliefors (Kolmogorov-Smirnov) normality test # pchiTest Pearson chi-square normality test # sfTest Shapiro-Francia normality test # FUNCTION: MORE TESTS ... # runsTest Runs test for detecting non-randomness [tseries] # gofnorm Reports on several tests of normality # FUNCTION ADDON: DESCRIPTION: # jbTable Table of finite sample p values for the JB test # pjb Computes probabilities for the Jarque Bera Test # qjb Computes quantiles for the Jarque Bera Test # jbTest Performs finite sample adjusted JB LM and ALM test ################################################################################ test.NormalityTests = function() { # Normal Data: ## RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") ## set.seed(4711, kind = "Marsaglia-Multicarry") set.seed(4711) X = rnorm(50) TEST = ksnormTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = shapiroTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = jarqueberaTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") if(try(require(interp))) { TEST = jbTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") } TEST = dagoTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = adTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = cvmTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = lillieTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = pchiTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = sfTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } # ------------------------------------------------------------------------------ test.NormalityTests.MSFT = function() { data("MSFT", package = "timeSeries", envir = environment()) X = returns(MSFT)[, 1] TEST = ksnormTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = shapiroTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = jarqueberaTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") if(try(require(interp))) { TEST = jbTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST")} TEST = dagoTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = adTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = cvmTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = lillieTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = pchiTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") TEST = sfTest(X) print(TEST) checkIdentical(as.character(class(TEST)), "fHTEST") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.subStars.R0000644000176200001440000000316614263246022017703 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .emptyPlot Creates an empty plot page # .subPlot Creates a sub plot # .cnvrt.coords Converts coordinates ################################################################################ test.subplot <- function() { # Plot: NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.fHTEST.R0000644000176200001440000000314014263246022017122 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: NORMALITY TESTS: # 'fHTEST' S4 Class Representation # show.fHTEST S4 Print Method ################################################################################ test.fHTEST = function() { # Class: getClass("fHTEST") # Slots: getSlots("fHTEST") # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.asPOSIXlt.R0000644000176200001440000000376714263246022017672 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # as.POSIXlt.default Default Method ################################################################################ # IMPORTANT NOTE: # as.POSIXlt was introduced as generic function in R 2.7.0 # Preliminarily, keep the function here to ne downward # compatible with previous R Versions. DW as.POSIXlt.default <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.decor.R0000644000176200001440000000277114263246022017172 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: # decor # hgrid # vgrid # boxL # box_ # xrug # yrug # copyright ################################################################################ test.decor <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.Sys.putenv.R0000644000176200001440000000354714263246022020176 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # Sys.putenv depreciated after 2.4.1 # head.ts Adds head method ################################################################################ test.Sys.putenv <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colorPalette.R0000644000176200001440000000452014263246022020525 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLOR PALETTES: # rainbowPalette Creates a rainbow color palette # heatPalette Creates a heat color palette # terrainPalette Creates a terrain color palette # topoPalette Creates a topo color palette # cmPalette Creates a cm color palette # greyPalette Creates a grey palette # timPalette Creates a cyan, yellow, to orange palette # FUNCTION: COLOR RAMPS: # rampPalette Creates a color ramp palette # seqPalette Creates a sequential color palette # divPalette Creates a diverging color palette # qualiPalette Creates a qualitative color palette # focusPalette Creates a focus color palette # monoPalette Creates a mono color palette ################################################################################ test.greyPalette <- function() { # Grey Palette: args(greyPalette) greyPalette() # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.Heaviside.R0000644000176200001440000000335114263246022017772 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: HEAVISIDE AND RELATED FUNCTIONS: # Heaviside Computes Heaviside unit step function # Sign Another signum function # Delta Computes delta function # Boxcar Computes boxcar function # Ramp Computes ramp function ################################################################################ test.Heaviside <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.symbolTable.R0000644000176200001440000000322714650724111020350 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: CHARACTER, SYMBOL AND COLOR TABLES: # symbolTable Shows a table of plot symbols ################################################################################ ## test.symbolTable <- ## function() ## { ## # Symbol Table: ## args(symbolTable) ## par(mfrow = c(1, 1)) ## symbolTable() ## ## # Return Value: ## return() ## } ################################################################################ fBasics/inst/unitTests/runit.lcg.R0000644000176200001440000000530714263246022016641 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: PORTABLE INNOVATIONS: # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # FUNCTION: DISTRIBUTIONS: # runif.lcg Generates portable uniform linear congruational rvs # rnorm.lcg Generates portable normal linear congruational rvs # rt.lcg Generates portable Student-t linear congruational rvs ################################################################################ test.portableInnovations <- function() { # set.lcgseed Sets initial random seed # get.lcgseed Gets the current valus of the random seed # Set initial random seed - set.lcgseed(seed = 4711) set.lcgseed() # Get the current valus of the random seed - get.lcgseed() get.lcgseed() # Return Value: return() } # ------------------------------------------------------------------------------ test.randomNumbers <- function() { # runif.lcg Generates portable uniform linear congruational rvs # rnorm.lcg Generates portable normal linear congruational rvs # rt.lcg Generates portable Student-t linear congruational rvs # Uniform: x = runif.lcg(n = 1000, min = 0, max = 1) hist(x) # Normal: x = rnorm.lcg(n = 1000, mean = 0, sd = 1) hist(x) # Student-t: x = rt.lcg(n = 1000, df = 4) hist(x) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.julian.R0000644000176200001440000000335514263246022017357 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DATE FUNCTIONS: # .fjulian Transforms formatted dates to julian day numbers # .julian Implements SPlus like 'julian' # .isPOSIX Checks for an object of class POSIX # .by2seconds Converts 'by' string into numeric value of seconds ################################################################################ test.fjulian <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runTests.R0000644000176200001440000000507714650724111016567 0ustar liggesuserspkg <- "fBasics" if(require("RUnit", quietly = TRUE)) { library(package=pkg, character.only = TRUE) if(!(exists("path") && file.exists(path))) path <- system.file("unitTests", package = pkg) ## --- Testing --- ## Define tests ## (2023-10-07) GNB: use default generators as the defaults for RUinit issue warnings testSuite <- defineTestSuite(name = paste(pkg, "unit testing"), rngKind = RNGkind()[1], # GNB rngNormalKind = RNGkind()[2], # GNB dirs = path) if(interactive()) { cat("Now have RUnit Test Suite 'testSuite' for package '", pkg, "' :\n", sep='') str(testSuite) cat('', "Consider doing", "\t tests <- runTestSuite(testSuite)", "\nand later", "\t printTextProtocol(tests)", '', sep = "\n") } else { ## run from shell / Rscript / R CMD Batch / ... ## Run tests <- runTestSuite(testSuite) if(file.access(path, 02) != 0) { ## cannot write to path -> use writable one tdir <- tempfile(paste(pkg, "unitTests", sep="_")) dir.create(tdir) pathReport <- file.path(tdir, "report") cat("RUnit reports are written into ", tdir, "/report.(txt|html)", sep = "") } else { pathReport <- file.path(path, "report") } ## Print Results: printTextProtocol(tests, showDetails = FALSE) printTextProtocol(tests, showDetails = FALSE, fileName = paste(pathReport, "Summary.txt", sep = "")) printTextProtocol(tests, showDetails = TRUE, fileName = paste(pathReport, ".txt", sep = "")) ## Print HTML Version to a File: ## printHTMLProtocol has problems on Mac OS X if (Sys.info()["sysname"] != "Darwin") printHTMLProtocol(tests, fileName = paste(pathReport, ".html", sep = "")) ## stop() if there are any failures i.e. FALSE to unit test. ## This will cause R CMD check to return error and stop tmp <- getErrors(tests) if(tmp$nFail > 0 | tmp$nErr > 0) { stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail, ", R errors: ", tmp$nErr, ")\n\n", sep="")) } } } else { cat("R package 'RUnit' cannot be loaded -- no unit tests run\n", "for package", pkg,"\n") } ################################################################################ fBasics/inst/unitTests/runit.as.matrix.ts.R0000644000176200001440000000407514263246022020430 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # as.matrix.ts Converts univariate ts to 1-column matrix # as.matrix.mts Converts multivariate ts to matrix ################################################################################ test.as.matrix.ts <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ as.matrix.mts <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.baseMethods.R0000644000176200001440000000724014263246022020330 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BASIC EXTENSIONS: # align.default align default method # atoms.default atoms default method # attach.default attach default method # colnames<-.default colnames<- default method # cor.default cor default method # cov.default var default method # log.default log default method # outlier.default outlier default method # rownames<-.default rownames<- default method # rank.default rank default method # sample.default sample default method # sort.default sort default method # stdev.default stdev default method # termPlot.default termPlot default method # var.default var default method # volatility.default volatility default method ################################################################################ test.as.align.default <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.colnames.default = function() { # "colnames<-.default" colnames<- default method # Row and Column Names: m = matrix(1:8, ncol = 2) m # Set Names: colnames(m) <- c("A", "B") m # Get Names: colnames(m) # Return Value: return() } # ------------------------------------------------------------------------------ test.rownames.default = function() { # "rownames<-.default" rownames<- default methodd # Row and Column Names: m = matrix(1:8, ncol = 2) m # Set Names: rownames(m) = as.character(1:4) m # Get Names: rownames(m) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.kurtosis.R0000644000176200001440000000435014263246022017754 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # kurtosis.default Default method # kurtosis.data.frame Method for objects of class data.frame # kurtosis.POSIXct Method for objects of class POSIXct # kurtosis.POSIXlt Method for objects of class POSIXlt ################################################################################ test.kurtosis.default <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.kurtosis.data.frame <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.kurtosis.POSIXct <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.kurtosis.POSIXlt <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colStats.R0000644000176200001440000000374014433645567017707 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: COLUMN STATISTICS: # colStats Computes sample statistics by column # colSums Computes sums of all values in each column # colMeans Computes means of all values in each column # colSds Computes standardard deviation of each column # colVars Computes sample variance by column # colSkewness Computes sample skewness by column # colKurtosis Computes sample kurtosis by column # colMaxs Computes maximum values in each colum # colMins Computes minimum values in each colum # colProds Computes product of all values in each colum # FUNCTION: NO LONGER USED: # colAvgs Computes sample mean by column # mean.timeSeries Computes sample means by column # var.timeSeries Computes sample variance by column ################################################################################ test.colStats <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.JarqueBeraPValues.R0000644000176200001440000000502014264011647021411 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2007, Diethelm Wuertz, GPL # Diethelm Wuertz # info@rmetrics.org # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: JARQUE-BERA DATA TABLES: # .jbTable Finite sample p values for the Jarque Bera test # .jbPlot Plots probability # .pjb Returns probabilities for the JB Test given quantiles # .qjb Returns quantiles for the ADF Test given probabilities # DATA: Description: # .jbLM Jarque-Bera Lagrange Multiplier Test Data # .jbALM Jarque Bera Augmented Lagrange Multiplier Test Data ################################################################################ test.jbTable = function() { if (FALSE) { require(interp) # Jarque-Bera Table: # .jbTable(type = c("LM", "ALM"), size = c("mini", "small", "all")) table = .jbTable() table # Perspective Plot: # .jbPlot(type = c("LM", "ALM")) .jbPlot() } # Return Value: return() } # ------------------------------------------------------------------------------ test.jbData = function() { # Jarque-Bera LM Data: class(fBasics:::.jbLM) class(fBasics:::.jbLM()) head(fBasics:::.jbLM()) # Jarque-Bera ALM Data: class(fBasics:::.jbALM) class(fBasics:::.jbALM()) head(fBasics:::.jbALM()) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.skewness.R0000644000176200001440000000432514263246022017735 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: SKEWNESS: # skewness.default Default method # skewness.data.frame Method for objects of class data.frame # skewness.POSIXct Method for objects of class POSIXct # skewness.POSIXlt Method for objects of class POSIXlt ################################################################################ test.skewness.default <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.skewness.data.frame <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.skewness.POSIXct <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.skewness.POSIXlt <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.listFunctions.R0000644000176200001440000000355714263246022020745 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # listFunctionss Lists all functions in a package # countFunctions Counts all functions in a package ################################################################################ test.listFunctions <- function() { # List Functions: print(listFunctions("fUtilities")) # Return Value: return() } # ------------------------------------------------------------------------------ test.countFunctions <- function() { # Count Functions: print(countFunctions("fUtilities")) # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.linearInterp.R0000644000176200001440000000344014263246022020524 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # linearInterp Interpolates linearly irregularly spaced data points # linearInterpp Interpolates linearly pointwise ################################################################################ test.linearInterp <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.linearInterp <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.getS4.R0000644000176200001440000000360214263246022017056 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: GENERAL EXTRACTORS FOR S4: # isS4 Checks if a function is a S4 object # getCall Extracts the call slot from a S4 object # getModel Extracts the model slot from a S4 object # getTitle Extracts the title slot from a S4 object # getDescription Extracts the description slot from a S4 object # getSlot Extracts a specified slot from a S4 object ################################################################################ test.isS4 <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.unitrootNA.R0000644000176200001440000000302314263246022020167 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # .unirootNA Computes zero without error exit ################################################################################ test.unitrootNA <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.colorLocator.R0000644000176200001440000000313214263246022020530 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: COLOR NAMES: # colorLocator Plots R's 657 named colors for selection # colorMatrix Returns matrix of R's color names ################################################################################ test.colorLocator <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.akimaInterp.R0000644000176200001440000000263314263246022020337 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: BIVARIATE GRIDDED INTERPOLATION: # akimaInterp Interpolates and smoothes irregularly spaced points # akimaInterpp Interpolates and smoothes pointwise ################################################################################ test.akimaInterp <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.akimaInterpp <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.sliderMenu.R0000644000176200001440000000340414263246022020177 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: SLIDER MENU: # .sliderMenu Opens a teching demo slider menu # .tdSliderMenu Opens a teching demo slider and button menu ################################################################################ test.sliderMenu <- function() { NA # Return Value: return() } # ------------------------------------------------------------------------------ test.sliderMenu <- function() { NA # Return Value: return() } ################################################################################ fBasics/inst/unitTests/runit.description.R0000644000176200001440000000302114263246022020406 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA # Copyrights (C) # for this R-port: # 1999 - 2008, Diethelm Wuertz, Rmetrics Foundation, GPL # Diethelm Wuertz # www.rmetrics.org # for the code accessed (or partly included) from other R-ports: # see R's copyright and license files # for the code accessed (or partly included) from contributed R-ports # and other sources # see Rmetrics's copyright file ################################################################################ # FUNCTION: DESCRIPTION: # description Sets default description string ################################################################################ test.description <- function() { NA # Return Value: return() } ################################################################################ fBasics/README.md0000644000176200001440000000336014325165317013114 0ustar liggesusers [![CRANStatusBadge](http://www.r-pkg.org/badges/version/fBasics)](https://cran.r-project.org/package=fBasics) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/fBasics)](https://www.r-pkg.org/pkg/fBasics) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/fBasics?color=blue)](https://r-pkg.org/pkg/fBasics) The R package 'fBasics' provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management. Package `fBasics` is part of the Rmetrics suite of R packages and is developed on R-forge at [fBasics](https://r-forge.r-project.org/scm/viewvc.php/pkg/fBasics/?root=rmetrics). The root of Rmetrics is at [R-forge](https://r-forge.r-project.org/projects/rmetrics). # Installing fBasics Install the [latest stable version](https://cran.r-project.org/package=fBasics) of `fBasics` from CRAN: install.packages("fBasics") You can install the [development version](https://r-forge.r-project.org/scm/viewvc.php/pkg/fBasics/?root=rmetrics) of `fBasics` from R-forge: install.packages("fBasics", repos = "http://R-Forge.R-project.org") To report bugs visit [Rmetrics](https://r-forge.r-project.org/projects/rmetrics/). # Documentation You can view the documentation of `fBasics` at [fBasicsDoc](https://geobosh.github.io/fBasicsDoc/) or download the [reference manual](https://cran.r-project.org/package=fBasics/fBasics.pdf) of the latest release from CRAN. fBasics/man/0000755000176200001440000000000014660436547012416 5ustar liggesusersfBasics/man/matrix-kron.Rd0000644000176200001440000000134514660350210015141 0ustar liggesusers\name{kron} \alias{kron} \title{Kronecker product} \description{ Computes the Kronecker product of two matrices. } \usage{ kron(x, y) } \arguments{ \item{x, y}{ numeric matrices. } } \details{ The \emph{Kronecker product} can be computed using the operator \code{\%x\%} or alternatively using the function \code{kron} for SPlus compatibility. } \note{ \code{kron} is a synonym to \code{\%x\%}. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(3) P ## Return the Kronecker Product kron(P, diag(3)) P \%x\% diag(3) } \keyword{math} fBasics/man/matrix-gridVector.Rd0000644000176200001440000000204614440717440016307 0ustar liggesusers\name{gridVector} \alias{gridVector} \title{Grid vector coordinates} \description{ Creates rectangular grid coordinates from two vectors. } \usage{ gridVector(x, y = NULL) } \arguments{ \item{x,y}{ numeric vectors } } \details{ The grid is obtained by pairing each element of \code{y} with all elements of \code{x}. The \code{X} and \code{Y} coordinates of the points are stored in separate vectors. This is convenient, for example, for plotting. It can be useful also for brute force optimisation or simulation. If \code{y} is \code{NULL}, the default, then \code{y = x}. } \value{ a list with two components, \code{X} and \code{Y}, giving the coordinates which span the bivariate grid. } \seealso{ \code{\link{expand.grid}} } \examples{ ## a small grid vector with row and col transformations gridVector(0:2) data.frame(gridVector(0:2)) do.call("rbind", gridVector(0:2)) gridVector(0:2, 0:3) ## grid over a unit square gridVector((0:10)/10) # equivalently: gridVector((0:10)/10, (0:10)/10) } \keyword{hplot} fBasics/man/test-varianceTest.Rd0000644000176200001440000000452314660434443016307 0ustar liggesusers\name{varianceTest} \alias{varianceTest} \alias{Ftest} \concept{Bartlett's test for equal variances} \concept{Fligner-Killeen test for equal variances} \title{Two sample variance tests} \description{ Tests if two series differ in their distributional variance parameter. } \usage{ varianceTest(x, y, method = c("varf", "bartlett", "fligner"), title = NULL, description = NULL) } \arguments{ \item{x, y}{ numeric vectors of data values. } \item{method}{ a character string naming which test should be applied. } \item{title}{ an optional title string, if not specified the inputs data name is deparsed. } \item{description}{ optional description string, or a vector of character strings. } } \details{ The \code{method="varf"} can be used to compare variances of two normal samples performing an F test. The null hypothesis is that the ratio of the variances of the populations from which they were drawn is equal to one. The \code{method="bartlett"} performs the Bartlett test of the null hypothesis that the variances in each of the samples are the same. This fact of equal variances across samples is also called \emph{homogeneity of variances}. Note, that Bartlett's test is sensitive to departures from normality. That is, if the samples come from non-normal distributions, then Bartlett's test may simply be testing for non-normality. The Levene test (not yet implemented) is an alternative to the Bartlett test that is less sensitive to departures from normality. The \code{method="fligner"} performs the Fligner-Killeen test of the null that the variances in each of the two samples are the same. } \value{ an object from class \code{\link{fHTEST}} } \note{ Some of the test implementations are selected from \R's \code{ctest} package. } \references{ Conover, W. J. (1971); \emph{Practical nonparametric statistics}, New York: John Wiley & Sons. Lehmann E.L. (1986); \emph{Testing Statistical Hypotheses}, John Wiley and Sons, New York. } \author{ R-core team for hypothesis tests implemented from \R's package \code{ctest}. } \examples{ \dontshow{set.seed(1234)} x <- rnorm(50) y <- rnorm(50) varianceTest(x, y, "varf") varianceTest(x, y, "bartlett") varianceTest(x, y, "fligner") } \keyword{htest} fBasics/man/utils-randLCG.Rd0000644000176200001440000000450014660436747015316 0ustar liggesusers\name{lcg} \alias{lcg} \alias{set.lcgseed} \alias{get.lcgseed} \alias{runif.lcg} \alias{rnorm.lcg} \alias{rt.lcg} \title{Generator for Portable random innovations} \description{ Functions to generate portable random innovations. The functions run under R and S-Plus and generate the same sequence of random numbers. Supported are uniform, normal and Student-t distributed random numbers. \cr The functions are: \tabular{ll}{ \code{set.lcgseed} \tab Set initial random seed, \cr \code{get.lcgseed} \tab Get the current valus of the random seed, \cr \code{runif.lcg} \tab Uniform linear congruational generator, \cr \code{rnorm.lcg} \tab Normal linear congruational generator, \cr \code{rt.lcg} \tab Student-t linear congruential generator. } } \usage{ set.lcgseed(seed = 4711) get.lcgseed() runif.lcg(n, min = 0, max = 1) rnorm.lcg(n, mean = 0, sd = 1) rt.lcg(n, df) } \arguments{ \item{seed}{ an integer value, the random number seed. } \item{n}{ an integer, the number of random innovations to be generated. } \item{df}{ degrees of freedom, a positive number, may be non-integer. } \item{mean, sd}{ mean and standard deviation of the normally distributed innovations. } \item{min, max}{ lower and upper limits of the uniformly distributed innovations. } } \details{ A simple portable random number generator for use in R and SPlus. We recommend to use this generator only for comparisons of calculations in R and Splus. The generator is a linear congruential generator with parameters \code{LCG(a=13445, c=0, m=2^31-1, X=0)}. It is a simple random number generator which passes the bitwise randomness test. } \value{ A vector of generated random innovations. The value of the current seed is stored in the variable \code{lcg.seed}. } \references{ Altman, N.S. (1988); \emph{Bitwise Behavior of Random Number Generators}, SIAM J. Sci. Stat. Comput., 9(5), September, 941--949. } \examples{ set.lcgseed(seed = 65890) ## runif.lcg, rnorm.lcg, rt.lcg cbind(runif.lcg(10), rnorm.lcg(10), rt.lcg(10, df = 4)) get.lcgseed() ## Note, to overwrite rnorm, use # rnorm = rnorm.lcg # Going back to rnorm # rm(rnorm) } \keyword{distribution} fBasics/man/test-scaleTest.Rd0000644000176200001440000000407314660434241015602 0ustar liggesusers\name{scaleTest} \alias{scaleTest} \concept{Ansari-Bradley test} \concept{Mood test} \title{Two sample scale tests} \description{ Tests if two series differ in their distributional scale parameter. } \usage{ scaleTest(x, y, method = c("ansari", "mood"), title = NULL, description = NULL) } \arguments{ \item{x, y}{ numeric vectors of data values. } \item{method}{ a character string naming which test should be applied. } \item{title}{ an optional title string, if not specified the inputs data name is deparsed. } \item{description}{ optional description string, or a vector of character strings. } } \details{ The \code{method="ansari"} performs the Ansari-Bradley two-sample test for a difference in scale parameters. The test returns for any sizes of the series \code{x} and \code{y} the exact p value together with its asymptotic limit. The \code{method="mood"}, is another test which performs a two-sample test for a difference in scale parameters. The underlying model is that the two samples are drawn from \emph{f(x-l)} and \emph{f((x-l)/s)/s}, respectively, where \emph{l} is a common location parameter and \emph{s} is a scale parameter. The null hypothesis is \emph{s=1}. } \value{ an object from class \code{\link{fHTEST}} } \note{ Some of the test implementations are selected from \R's \code{ctest} package. } \references{ Conover, W. J. (1971); \emph{Practical nonparametric statistics}, New York: John Wiley & Sons. Lehmann E.L. (1986); \emph{Testing Statistical Hypotheses}, John Wiley and Sons, New York. Moore, D.S. (1986); \emph{Tests of the chi-squared type}, In: D'Agostino, R.B. and Stephens, M.A., eds., Goodness-of-Fit Techniques, Marcel Dekker, New York. } \author{ R-core team for hypothesis tests implemented from \R's package \code{ctest}. } \examples{ \dontshow{set.seed(1234)} ## Generate Series: x = rnorm(50) y = rnorm(50) scaleTest(x, y, "ansari") scaleTest(x, y, "mood") } \keyword{htest} fBasics/man/plot-boxPlot.Rd0000644000176200001440000000264414660350562015306 0ustar liggesusers\name{BoxPlot} \alias{boxPlot} \alias{boxPercentilePlot} \title{Time series box plots} \description{ Produce a box plot or a box percentile plot. } \usage{ boxPlot(x, col = "steelblue", title = TRUE, \dots) boxPercentilePlot(x, col = "steelblue", title = TRUE, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{title}{ a logical flag, by default \code{TRUE}. Should a default title added to the plot? } \item{\dots}{ optional arguments to be passed. } } \details{ \code{boxPlot} produces a side-by-side standard box plot, \code{boxPercentilePlot} produces a side-by-side box-percentile plot. } \value{ \code{NULL}, displays a time series plot } \examples{ ## data data(LPP2005REC, package = "timeSeries") LPP <- LPP2005REC[, 1:6] plot(LPP, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") boxPlot(LPP) } \keyword{hplot} fBasics/man/utils-Heaviside.Rd0000644000176200001440000000357414660435433015746 0ustar liggesusers\name{Heaviside} \alias{Heaviside} \alias{Sign} \alias{Delta} \alias{Boxcar} \alias{Ramp} \title{Heaviside and related functions} \description{ Functions which compute the Heaviside and related functions. These include the Heaviside function, the sign function, the delta function, the boxcar function, and the ramp function. } \usage{ Heaviside(x, a = 0) Sign(x, a = 0) Delta(x, a = 0) Boxcar(x, a = 0.5) Ramp(x, a = 0) } \arguments{ \item{x}{ a numeric vector. } \item{a}{ a numeric value, the location of the break. } } \details{ \code{Heaviside} computes the Heaviside unit step function. \code{Heaviside} is 1 for \code{x > a}, \code{1/2} for \code{x = a}, and \code{0} for \code{x < a}. \code{Sign} computes the sign function. \code{Sign} is \code{1} for \code{x > a}, \code{0} for \code{x = a}, and \code{-1} for \code{x < a}. \code{Delta} computes the delta function. \code{Delta} is defined as: \code{Delta(x) = d/dx H(x-a)}. \code{Boxcar} computes the boxcar function. \code{Boxcar} is defined as: \code{Boxcar(x) = H(x+a) - H(x-a)}. \code{Ramp} computes ramp function. The ramp function is defined as: \code{Ramp(x) = (x-a) * H(x-a)}. } \value{ numeric vector } \note{ The Heaviside function is used in the implementation of the skew Normal, Student-t, and Generalized Error distributions, distributions functions which play an important role in modelling GARCH processes. } \seealso{ \code{GarchDistribution}, \code{GarchDistributionFits} } \references{ Weisstein W. (2004); \emph{http://mathworld.wolfram.com/HeavisideStepFunction.html}, Mathworld. } \examples{ x <- sort(round(c(-1, -0.5, 0, 0.5, 1, 5*rnorm(5)), 2)) h <- Heaviside(x) s <- Sign(x) d <- Delta(x) Pi <- Boxcar(x) r <- Ramp(x) cbind(x = x, Step = h, Signum = s, Delta = d, Pi = Pi, R = r) } \keyword{math} fBasics/man/dist-ghMoments.Rd0000644000176200001440000000413414650724110015572 0ustar liggesusers\name{ghMoments} \alias{ghMoments} \alias{ghMean} \alias{ghVar} \alias{ghSkew} \alias{ghKurt} \alias{ghMoments} \title{Generalized Hyperbolic Distribution Moments} \description{ Calculates moments of the generalized hyperbolic distribution. } \usage{ ghMean(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghVar(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghSkew(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghKurt(alpha=1, beta=0, delta=1, mu=0, lambda=-1/2) ghMoments(order, type = c("raw", "central", "mu"), alpha = 1, beta=0, delta=1, mu=0, lambda=-1/2) } \arguments{ \item{alpha}{ numeric value, the first shape parameter. } \item{beta}{ numeric value, the second shape parameter in the range \code{(0, alpha)}. } \item{delta}{ numeric value, the scale parameter, must be zero or positive. } \item{mu}{ numeric value, the location parameter, by default 0. } \item{lambda}{ numeric value, defines the sublclass, by default \eqn{-1/2}. } \item{order}{ an integer value, the order of the moment. } \item{type}{ a character value, \code{"raw"} gives the moments about zero, \code{"central"} gives the central moments about the mean, and \code{"mu"} gives the moments about the location parameter \code{mu}. } } \value{ a named numerical value. The name is one of \code{mean}, \code{var}, \code{skew}, or \code{kurt}, obtained by dropping the \code{nig} prefix from the name of the corresponding function and lowercasing it. for \code{ghMoments}, the name is obtained by \code{paste0("m", order, type)}. } \references{ Scott, D. J., Wuertz, D. and Tran, T. T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz } \examples{ ## ghMean - ghMean(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) ## ghKurt - ghKurt(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) ## ghMoments - ghMoments(4, alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) ghMoments(4, "central", alpha=1.1, beta=0.1, delta=0.8, mu=-0.3, lambda=1) } \keyword{distribution} fBasics/man/dist-nigShapeTriangle.Rd0000644000176200001440000000323414650724110017055 0ustar liggesusers\name{nigShapeTriangle} \alias{nigShapeTriangle} \title{NIG Shape Triangle} \description{ Plots the normal inverse Gaussian Shape Triangle. } \usage{ nigShapeTriangle(object, add = FALSE, labels = TRUE, \dots) } \arguments{ \item{object}{ an object of class \code{"fDISTFIT"} as returned by the function \code{nigFit}. } \item{add}{ a logical value. Should another point added to the NIG shape triangle? By default FALSE, a new plot will be created. } \item{labels}{ a logical flag by default \code{TRUE}. Should the logarithm of the density be returned? } \item{\dots}{ arguments to be passed to the function \code{integrate}. } } \value{ displays the parameters of fitted distributions in the NIG shape triangle. } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## nigShapeTriangle - # } \keyword{distribution} fBasics/man/utils-hessian.Rd0000644000176200001440000000073314650724110015461 0ustar liggesusers\name{tsHessian} \alias{tsHessian} \title{Two sided approximated Hessian} \description{ Computes two sided (TS) approximated Hessian. } \usage{ tsHessian(x, fun, ...) } \arguments{ \item{x}{argument to be passed to \code{fun}.} \item{fun}{function.} \item{\dots}{additional parameters to be passed to \code{fun}.} } \author{ A function borrowed from Kevin Sheppard's Matlab garch toolbox as implemented by Alexios Ghalanos in his \pkg{rgarch} package. } fBasics/man/utils-characterTable.Rd0000644000176200001440000000226214660434515016742 0ustar liggesusers\name{characterTable} \alias{characterTable} \title{Table of characters} \description{ Displays a table of numerical equivalents to Latin characters. } \usage{ characterTable(font = 1, cex = 0.7) } \arguments{ \item{cex}{ a numeric value, determines the character size, the default size is 0.7. } \item{font}{ an integer value, the number of the \code{font}, by default font number 1. } } \value{ displays a table with the characters of the requested font. The character on line \code{"xy"} and column \code{"z"} of the table has code \code{"\xyz"}, e.g \code{cat("\\126")} prints: V for font number 1. These codes can be used as any other characters. } \note{ What happens with non-ASCII characters in plots is system dependent and depends on the graphics device, as well. Use of such characters is not recommended for portable code. } \seealso{ \code{\link{colorTable}}, \code{\link{symbolTable}} \code{\link[graphics]{points}} for use of characters in plotting } \examples{ ## Character Table for Font 1: # characterTable(font = 1) } \keyword{programming} fBasics/man/matrix-tslag.Rd0000644000176200001440000000122714440716626015316 0ustar liggesusers\name{tslag} \alias{tslag} \title{Lagged or leading vector/matrix} \description{ Creates a lagged or leading vector/matrix of selected order(s). } \usage{ tslag(x, k = 1, trim = FALSE) } \arguments{ \item{k}{ an integer value, the number of positions the new series is to lag or to lead the input series. } \item{x}{ a numeric vector or matrix, missing values are allowed. } \item{trim}{ a logical flag, if TRUE, the missing values at the beginning ans/or end of the returned series will be trimmed. The default value is FALSE. } } \seealso{ \code{\link{pdl}} } \examples{ ## tslag - } \keyword{math} fBasics/man/matrix-hilbert.Rd0000644000176200001440000000264214660350101015621 0ustar liggesusers\name{hilbert} \alias{hilbert} \title{Hilbert matrix} \description{ Creates a Hilbert matrix. } \usage{ hilbert(n) } \arguments{ \item{n}{ an integer value, the dimension of the square matrix. } } \details{ An \eqn{n,n} matrix with \eqn{(i,j)}th element equal to \eqn{1/(i+j-1)} is said to be a Hilbert matrix of order \eqn{n}. Hilbert matrices are symmetric and positive definite. They are canonical examples of ill-conditioned matrices, making them notoriously difficult to use in numerical computation. For example, the 2-norm condition number of a 5x5 Hilbert matrix above is about 4.8e5. } \value{ a matrix } \references{ Hilbert D., \emph{Collected papers}, vol. II, article 21. Beckermann B, (2000); \emph{The condition number of real Vandermonde, Krylov and positive definite Hankel matrices}, Numerische Mathematik 85, 553--577, 2000. Choi, M.D., (1983); \emph{Tricks or Treats with the Hilbert Matrix}, American Mathematical Monthly 90, 301--312, 1983. Todd, J., (1954); \emph{The Condition Number of the Finite Segment of the Hilbert Matrix}, National Bureau of Standards, Applied Mathematics Series 39, 109--116. Wilf, H.S., (1970); \emph{Finite Sections of Some Classical Inequalities}, Heidelberg, Springer. } \examples{ ## Create a Hilbert Matrix: H = hilbert(5) H } \keyword{math} fBasics/man/stats-maxdd.Rd0000644000176200001440000000747614660433114015137 0ustar liggesusers\name{maxdd} \alias{maxdd} \alias{dmaxdd} \alias{pmaxdd} \alias{rmaxdd} \alias{maxddStats} \title{Drawdown statistics} \description{ A collection of functions which compute drawdown statistics. Included are density, distribution function, and random generation for the maximum drawdown distribution. In addition the expectation of drawdowns for Brownian motion can be computed. } \usage{ dmaxdd(x, sd = 1, horizon = 100, N = 1000) pmaxdd(q, sd = 1, horizon = 100, N = 1000) rmaxdd(n, mean = 0, sd = 1, horizon = 100) maxddStats(mean = 0, sd = 1, horizon = 1000) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{n}{ an integer value, the number of observations. } \item{mean, sd}{ two numeric values, the mean and standard deviation. } \item{horizon}{ an integer value, the (run time) horizon of the investor. } \item{N}{ an integer value, the precession index for summations. Before you change this value please inspect Magdon-Ismail et. al. (2003). } } \details{ \code{dmaxdd} computes the density function of the maximum drawdown distribution. \code{pmaxdd} computes the distribution function. \code{rmaxdd} generates random numbers from that distribution. \code{maxddStats} computes the expectation of drawdowns. \code{dmaxdd} returns for a trendless Brownian process \code{mean=0} and standard deviation "sd" the density from the probability that the maximum drawdown "D" is larger or equal to "h" in the interval [0,T], where "T" denotes the time \code{horizon} of the investor. \code{pmaxdd} returns for a trendless Brownian process \code{mean=0} and standard deviation "sd" the probability that the maximum drawdown "D" is larger or equal to "h" in the interval [0,T], where "T" denotes the time \code{horizon} of the investor. \code{rmaxdd} returns for a Brownian Motion process with mean \code{mean} and standard deviation \code{sd} random variates of maximum drawdowns. \code{maxddStats} returns the expected value, E[D], of maximum drawdowns of Brownian Motion for a given drift \code{mean}, variance \code{sd}, and runtime \code{horizon} of the Brownian Motion process. } \note{ Currrently, only the driftless case is implemented. } %\value{ % %} \references{ Magdon-Ismail M., Atiya A.F., Pratap A., Abu-Mostafa Y.S. (2003); \emph{On the Maximum Drawdown of a Brownian Motion}, Preprint, CalTech, Pasadena USA, p. 24. } \examples{ ## rmaxdd ## Set a random seed set.seed(1953) ## horizon of the investor, time T horizon <- 1000 ## number of MC samples, N -> infinity samples <- 1000 ## Range of expected Drawdons xlim <- c(0, 5) * sqrt(horizon) ## Plot Histogram of Simulated Max Drawdowns: r <- rmaxdd(n = samples, mean = 0, sd = 1, horizon = horizon) hist(x = r, n = 40, probability = TRUE, xlim = xlim, col = "steelblue4", border = "white", main = "Max. Drawdown Density") points(r, rep(0, samples), pch = 20, col = "orange", cex = 0.7) ## dmaxdd x <- seq(0, xlim[2], length = 200) d <- dmaxdd(x = x, sd = 1, horizon = horizon, N = 1000) lines(x, d, lwd = 2) ## pmaxdd ## Count Frequencies of Drawdowns Greater or Equal to "h": n <- 50 x <- seq(0, xlim[2], length = n) g <- rep(0, times = n) for (i in 1:n) g[i] <- length (r[r > x[i]]) / samples plot(x, g, type ="h", lwd = 3, xlab = "q", main = "Max. Drawdown Probability") ## Compare with True Probability "G_D(h)": x <- seq(0, xlim[2], length = 5*n) p <- pmaxdd(q = x, sd = 1, horizon = horizon, N = 5000) lines(x, p, lwd = 2, col="steelblue4") ## maxddStats ## Compute expectation Value E[D]: maxddStats(mean = -0.5, sd = 1, horizon = 10^(1:4)) maxddStats(mean = 0.0, sd = 1, horizon = 10^(1:4)) maxddStats(mean = 0.5, sd = 1, horizon = 10^(1:4)) } \keyword{distribution} fBasics/man/dist-snigFit.Rd0000644000176200001440000000373214657125340015246 0ustar liggesusers\name{snigFit} \alias{snigFit} \concept{Standardized normal inverse Gaussian distribution} \title{Fit of a Standardized NIG Distribution} \description{ Estimates the parameters of a standardized normal inverse Gaussian distribution. } \usage{ snigFit(x, zeta = 1, rho = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{zeta, rho}{ shape parameter \code{zeta} is positive, skewness parameter \code{rho} is in the range (-1, 1). } \item{description}{ a character string which allows for a brief description. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{x}{ a numeric vector. } \item{\dots}{ parameters to be parsed. } } \value{ an object from class \code{"\linkS4class{fDISTFIT}"}. Slot \code{fit} is a list with the same components as the result from \code{\link{snigFit}}. } \examples{ ## Simulate Random Variates: set.seed(1953) s <- rsnig(n = 2000, zeta = 0.7, rho = 0.5) ## snigFit - # Fit Parameters: snigFit(s, zeta = 1, rho = 0, doplot = TRUE) } \keyword{distribution} fBasics/man/baseMethods.Rd0000644000176200001440000000163314650724110015127 0ustar liggesusers\name{baseMethods} \alias{baseMethods} \alias{termPlot} \alias{termPlot.default} \alias{stdev} \alias{stdev.default} \title{Generic functions extensions} \description{ Basic extensions which add and/or modify additional functionality which is not available in R's basic packages. } \usage{ \method{stdev}{default}(x, na.rm = FALSE) \method{termPlot}{default}(model, \dots) } \arguments{ \item{model}{ a fitted model object. } \item{x}{ an object for which to compute the standard deviation. } \item{na.rm}{ a logical value - should the NA values be removed. } \item{\dots}{ arguments to be passed. } } \details{ \code{stdev} and \code{termPlot} are generic functions with default methods \code{stats::sd} and \code{stats::termplot}, respectively. } \seealso{ \code{\link[stats]{sd}}, \code{\link[stats]{termplot}} } \keyword{programming} \keyword{stats} fBasics/man/dist-ght.Rd0000644000176200001440000000435214650724110014415 0ustar liggesusers\name{ght} \alias{dght} \alias{pght} \alias{qght} \alias{rght} \concept{generalized hyperbolic Student-t distribution} \title{Generalized Hyperbolic Student-t distribution} \description{ Density, distribution function, quantile function and random generation for the generalized hyperbolic Student-t distribution. } \usage{ dght(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) pght(q, beta = 0.1, delta = 1, mu = 0, nu = 10) qght(p, beta = 0.1, delta = 1, mu = 0, nu = 10) rght(n, beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{beta}{ numeric value, the skewness parameter in the range \code{(0, alpha)}. } \item{delta}{ numeric value, the scale parameter, must be zero or positive. } \item{mu}{ numeric value, the location parameter, by default 0. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } } \details{ \code{dght} gives the density, \code{pght} gives the distribution function, \code{qght} gives the quantile function, and \code{rght} generates random deviates. The parameters are as in the first parameterization. } \value{ numeric vector } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## ght - # } \keyword{distribution} fBasics/man/dist-hypFit.Rd0000644000176200001440000000453514657144767015126 0ustar liggesusers\name{hypFit} \alias{hypFit} \concept{hyperbolic distribution} \title{Fit a hyperbolic distribution} \description{ Estimates the parameters of a hyperbolic distribution. } \usage{ hypFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{alpha}{ shape parameter, a positive number. } \item{beta}{ skewness parameter, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \details{ The meaning of the parameters given above corresponds to the first parameterization, see \code{\link{dhyp}} for details. The function \code{\link{nlm}} is used to minimize the "negative" maximum log-likelihood function. \code{nlm} carries out a minimization using a Newton-type algorithm. } \value{ an object from class \code{"\linkS4class{fDISTFIT}"}. Slot \code{fit} is a list, currently with components \code{estimate}, \code{minimum} and \code{code}. } \examples{ set.seed(1953) s <- rhyp(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0) hypFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE, trace = FALSE) } \keyword{distribution} fBasics/man/matrix-tr.Rd0000644000176200001440000000113714660350402014617 0ustar liggesusers\name{tr} \alias{tr} \title{Trace of a matrix} \description{ Computes the trace of a matrix. } \usage{ tr(x) } \arguments{ \item{x}{ a numeric matrix. } } \details{ \code{tr} computes the trace of a square matrix, i.e., the sum of its diagonal elements. If the matrix is not square, \code{tr} returns \code{NA}. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(3) P tr(P) } \keyword{math} fBasics/man/utils-colorTable.Rd0000644000176200001440000000101714660434745016126 0ustar liggesusers\name{colorTable} \alias{colorTable} \title{Table of colors} \description{ Displays a table of color codes and plots the colors themselves. } \usage{ colorTable(cex = 0.7) } \arguments{ \item{cex}{ a numeric value, determines the character size in the color plot, the default is 0.7. } } \value{ a table of plot colors with the associated color numbers } \seealso{ \code{\link{characterTable}}, \code{\link{symbolTable}} } \examples{ colorTable() } \keyword{programming} fBasics/man/dist-hypMoments.Rd0000644000176200001440000000371714650724110016002 0ustar liggesusers\name{hypMoments} \alias{hypMoments} \alias{hypMean} \alias{hypVar} \alias{hypSkew} \alias{hypKurt} \alias{hypMoments} \concept{hyperbolic distribution} \title{Hyperbolic distribution moments} \description{ Calculates moments of the hyperbolic distribution function. } \usage{ hypMean(alpha=1, beta=0, delta=1, mu=0) hypVar(alpha=1, beta=0, delta=1, mu=0) hypSkew(alpha=1, beta=0, delta=1, mu=0) hypKurt(alpha=1, beta=0, delta=1, mu=0) hypMoments(order, type = c("raw", "central", "mu"), alpha=1, beta=0, delta=1, mu=0) } \arguments{ \item{alpha}{ numeric value, the first shape parameter. } \item{beta}{ numeric value, the second shape parameter in the range \code{(0, alpha)}. } \item{delta}{ numeric value, the scale parameter, must be zero or positive. } \item{mu}{ numeric value, the location parameter, by default 0. } \item{order}{ an integer value, the order of the moment. } \item{type}{ a character string, \code{"raw"} returns the moments about zero, \code{"central"} returns the central moments about the mean, and \code{"mu"} returns the moments about the location parameter \code{mu}. } } \value{ a named numerical value. The name is one of \code{mean}, \code{var}, \code{skew}, or \code{kurt}, obtained by dropping the \code{hyp} prefix from the name of the corresponding function and lowercasing it. for \code{hypMoments}, the name is obtained by \code{paste0("m", order, type)}. } \references{ Scott, D. J., Wuertz, D. and Tran, T. T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz } \examples{ ## hypMean - hypMean(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) ## ghKurt - hypKurt(alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) ## hypMoments - hypMoments(4, alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) hypMoments(4, "central", alpha=1.1, beta=0.1, delta=0.8, mu=-0.3) } \keyword{distribution} fBasics/man/stats-sampleLMoments.Rd0000644000176200001440000000153114660433333016767 0ustar liggesusers\name{sampleRobMoments} \alias{sampleRobMoments} \alias{sampleMED} \alias{sampleIQR} \alias{sampleSKEW} \alias{sampleKURT} \title{Robust moments for the GLD} \description{ Computes the first four robust moments for the Normal Inverse Gaussian Distribution. } \usage{ sampleMED(x) sampleIQR(x) sampleSKEW(x) sampleKURT(x) } \arguments{ \item{x}{ numeric vector, the sample values. } } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{sample} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz } \examples{ ## Sample x <- rt(100, 4) ## Median sampleMED(x) ## Inter-quartile Range sampleIQR(x) ## Robust Skewness sampleSKEW(x) ## Robust Kurtosis sampleKURT(x) } \keyword{distribution} fBasics/man/dist-nigMoments.Rd0000644000176200001440000000305014650724110015745 0ustar liggesusers\name{nigMoments} \alias{nigMoments} \alias{nigMean} \alias{nigVar} \alias{nigSkew} \alias{nigKurt} \title{Moments for the Normal Inverse Gaussian} \description{ Computes the first four moments for the normal inverse Gaussian distribution. } \usage{ nigMean(alpha = 1, beta = 0, delta = 1, mu = 0) nigVar(alpha = 1, beta = 0, delta = 1, mu = 0) nigSkew(alpha = 1, beta = 0, delta = 1, mu = 0) nigKurt(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha}{ shape parameter. } \item{beta}{ skewness parameter \code{beta}, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } } \value{ a named numerical value. The name is one of \code{mean}, \code{var}, \code{skew}, or \code{kurt}, obtained by dropping the \code{nig} prefix from the name of the corresponding function and lowercasing it. } \references{ Scott, D. J., Wuertz, D. and Tran, T. T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz. } \examples{ ## nigMean - # Median: nigMean(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigVar - # Inter-quartile Range: nigVar(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigSKEW - # Robust Skewness: nigSkew(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigKurt - # Robust Kurtosis: nigKurt(alpha = 1, beta = 0, delta = 1, mu = 0) } \keyword{distribution} fBasics/man/matrix-vech.Rd0000644000176200001440000000160614660350440015122 0ustar liggesusers\name{vec} \alias{vec} \alias{vech} \title{Stacking vectors and matrices} \description{ Stacks either a lower triangle matrix or a matrix. } \usage{ vec(x) vech(x) } \arguments{ \item{x}{ a numeric matrix. } } \details{ The function \code{vec} implements the operator that stacks a matrix as a column vector, to be more precise in a matrix with one column. \eqn{vec(X) = (X_{11}, X_{21}, ..., X_{N1}, X_{12}, X_{22}, ..., X_{NN})}. The function \code{vech} implements the operator that stacks the lower triangle of a NxN matrix as an N(N+1)/2x1 vector: \eqn{vech(X) =(X_{11}, X_{21}, X_{22}, X_{31}, ..., X_{NN})}, to be more precise in a matrix with one row. } \examples{ ## Create Pascal Matrix: P = pascal(3) ## Stack the matrix vec(P) ## Stack the lower triangle vech(P) } \keyword{math} fBasics/man/test-locationTest.Rd0000644000176200001440000000361314660433741016326 0ustar liggesusers\name{locationTest} \alias{locationTest} \concept{two sample t-test} \concept{Kruskal-Wallis rank sum test} \title{Two sample location tests} \description{ Tests if two series differ in their distributional location parameter. } \usage{ locationTest(x, y, method = c("t", "kw2"), title = NULL, description = NULL) } \arguments{ \item{x, y}{ numeric vectors of data values. } \item{method}{ a character string naming which test should be applied. } \item{title}{ an optional title string, if not specified the input's data name is deparsed. } \item{description}{ optional description string, or a vector of character strings. } } \details{ The \code{method = "t"} can be used to determine if the two sample means are equal for unpaired data sets. Two variants are used, assuming equal or unequal variances. The \code{method = "kw2"} performs a Kruskal-Wallis rank sum test of the null hypothesis that the central tendencies or medians of two samples are the same. The alternative is that they differ. Note, that it is not assumed that the two samples are drawn from the same distribution. It is also worth to know that the test assumes that the variables under consideration have underlying continuous distributions. } \value{ an object from class \code{\link{fHTEST}} } \note{ Some of the test implementations are selected from \R's \code{ctest} package. } \references{ Conover, W. J. (1971); \emph{Practical nonparametric statistics}, New York: John Wiley & Sons. Lehmann E.L. (1986); \emph{Testing Statistical Hypotheses}, John Wiley and Sons, New York. } \author{ R-core team for hypothesis tests implemented from \R's package \code{ctest}. } \examples{ \dontshow{set.seed(1234)} x <- rnorm(50) y <- rnorm(50) locationTest(x, y, "t") locationTest(x, y, "kw2") } \keyword{htest} fBasics/man/dist-DistributionFits.Rd0000644000176200001440000000700314657144700017144 0ustar liggesusers\name{DistributionFits} \alias{DistributionFits} \alias{nFit} \alias{tFit} \alias{stableFit} \title{Fit normal, Student-t and stable distributions} \description{ A collection of moment and maximum likelihood estimators to fit the parameters of a distribution. \cr The functions are: \tabular{ll}{ \code{nFit} \tab MLE parameter fit for a normal distribution, \cr \code{tFit} \tab MLE parameter fit for a Student t-distribution, \cr \code{stableFit} \tab MLE and Quantile Method stable parameter fit. } } \usage{ nFit(x, doplot = TRUE, span = "auto", title = NULL, description = NULL, \dots) tFit(x, df = 4, doplot = TRUE, span = "auto", trace = FALSE, title = NULL, description = NULL, \dots) stableFit(x, alpha = 1.75, beta = 0, gamma = 1, delta = 0, type = c("q", "mle"), doplot = TRUE, control = list(), trace = FALSE, title = NULL, description = NULL) } \arguments{ \item{x}{ a numeric vector. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{control}{ a list of control parameters, see function \code{nlminb}. } \item{alpha, beta, gamma, delta}{ The parameters are \code{alpha}, \code{beta}, \code{gamma}, and \code{delta}:\cr value of the index parameter \code{alpha} with \code{alpha = (0,2]}; skewness parameter \code{beta}, in the range [-1, 1]; scale parameter \code{gamma}; and shift parameter \code{delta}. } \item{description}{ a character string which allows for a brief description. } \item{df}{ the number of degrees of freedom for the Student distribution, \code{df > 2}, maybe non-integer. By default a value of 4 is assumed. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{type}{ a character string which allows to select the method for parameter estimation: \code{"mle"}, the maximum log likelihood approach, or \code{"qm"}, McCulloch's quantile method. } \item{\dots}{ parameters to be parsed. } } \value{ an object from class \code{"\linkS4class{fDISTFIT}"}. Slot \code{fit} has components \code{estimate}, \code{minimum}, \code{code} and \code{gradient} (but for \code{nFit} \code{code} is \code{NA} and \code{gradient} is missing). } \details{ \bold{Stable Parameter Estimation:} Estimation techniques based on the quantiles of an empirical sample were first suggested by Fama and Roll [1971]. However their technique was limited to symmetric distributions and suffered from a small asymptotic bias. McCulloch [1986] developed a technique that uses five quantiles from a sample to estimate \code{alpha} and \code{beta} without asymptotic bias. Unfortunately, the estimators provided by McCulloch have restriction \code{alpha>0.6}. \emph{Remark:} The parameter estimation for the stable distribution via the maximum Log-Likelihood approach may take a quite long time. } \examples{ set.seed(1953) s <- rnorm(n = 1000, 0.5, 2) nFit(s, doplot = TRUE) } \keyword{distribution} fBasics/man/test-ks2Test.Rd0000644000176200001440000000317714660433633015222 0ustar liggesusers\name{ks2Test} \alias{ks2Test} \title{Two sample Kolmogorov-Smirnov test} \description{ Tests if two series are distributionally equivalent using two sample Kolmogorov-Smirnov test. } \usage{ ks2Test(x, y, title = NULL, description = NULL) } \arguments{ \item{x, y}{ numeric vectors of data values. } \item{title}{ an optional title string, if not specified the inputs data name is deparsed. } \item{description}{ optional description string, or a vector of character strings. } } \details{ The test \code{ks2Test} performs a Kolmogorov-Smirnov two sample test that the two data samples, \code{x} and \code{y}, come from the same distribution, not necessarily a normal distribution. That means that it is not specified what that common distribution is. \code{ks2Test} calls several times base R's \code{\link[stats]{ks.test}} p-values for all three alternatives (two-sided, less, and greater), as well as the exact p-value for the two-sided case. Note that the p-values are computed under a hypothesis of i.i.d., which is rarely the case for time series. So, the results should be interpreted cautiosly if that is the case. The same applies when the data are residuals from fitted models. } \value{ an object from class \code{\link{fHTEST}} } \references{ Conover, W. J. (1971); \emph{Practical nonparametric statistics}, New York: John Wiley & Sons. Lehmann E.L. (1986); \emph{Testing Statistical Hypotheses}, John Wiley and Sons, New York. } \examples{ \dontshow{set.seed(1234)} x <- rnorm(50) y <- rnorm(50) ks2Test(x, y) } \keyword{htest} fBasics/man/dist-snig.Rd0000644000176200001440000000361714650724110014576 0ustar liggesusers\name{snig} \alias{snig} \alias{dsnig} \alias{psnig} \alias{qsnig} \alias{rsnig} \concept{standardized normal inverse Gaussian distribution} \title{Standardized Normal Inverse Gaussian Distribution} \description{ Density, distribution function, quantile function and random generation for the standardized normal inverse Gaussian distribution. } \usage{ dsnig(x, zeta = 1, rho = 0, log = FALSE) psnig(q, zeta = 1, rho = 0) qsnig(p, zeta = 1, rho = 0) rsnig(n, zeta = 1, rho = 0) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{zeta}{ shape parameter \code{zeta} is positive. } \item{rho}{ skewness parameter, a number in the range \eqn{(-1, 1)}. } \item{log}{ a logical flag by default \code{FALSE}. If TRUE, log values are returned. } } \details{ \code{dsnig} gives the density, \code{psnig} gives the distribution function, \code{qsnig} gives the quantile function, and \code{rsnig} generates random deviates. The random deviates are calculated with the method described by Raible (2000). } \value{ numeric vector } \author{ Diethelm Wuertz } \examples{ ## snig - set.seed(1953) r = rsnig(5000, zeta = 1, rho = 0.5) plot(r, type = "l", col = "steelblue", main = "snig: zeta=1 rho=0.5") ## snig - # Plot empirical density and compare with true density: hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, length = 501) lines(x, dsnig(x, zeta = 1, rho = 0.5)) ## snig - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, psnig(x, zeta = 1, rho = 0.5)) ## snig - # Compute Quantiles: qsnig(psnig(seq(-5, 5, 1), zeta = 1, rho = 0.5), zeta = 1, rho = 0.5) } \keyword{distribution} fBasics/man/00fBasics-package.Rd0000644000176200001440000005552314657050125016010 0ustar liggesusers\name{fBasics-package} \alias{fBasics-package} \alias{fBasics} \docType{package} \title{Portfolio modelling, optimization and backtesting} \description{ The Rmetrics \pkg{fBasics} package is a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Evenmore there are several utility functions for data handling and management. } \author{ Diethelm Wuertz [aut] (original code), Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] (), Georgi N. Boshnakov [cre, ctb], CRAN Team [ctb] Maintainer: Georgi N. Boshnakov } \details{ \emph{Note by the maintainer (GNB):} Some of the information on this overview page may be outdated. The documentation website \url{https://geobosh.github.io/fBasicsDoc/} of the package (generated with pkgdown) provides up-to-date help pages, arranged by topic. } \section{1 Introduction}{ The fBasics package contains \emph{basics tools} often required in computational finance and financial engineering. The topics are: basic statistics functions, financial return distributions, hypothesis testing, plotting routines, matrix computations and linear algebra, and some usefule utility functions. } \section{2 Basic Statistics Functions}{ \emph{Financial Return Statistics} % stats-basicStats.R \preformatted{ basicStats Returns a basic statistics summary } \emph{Distribution Function of Maximum Drawdowns} % stats-maxdd.R \preformatted{ dmaxdd Density function of mean Max-Drawdowns pmaxdd Probability function of mean Max-Drawdowns rmaxdd Random Variates of mean Max-Drawdowns maxddStats Expectation of Drawdowns for BM with drift } \emph{Calculation of Sample Moments} % stats-sampleLMoments.R | stats-sampleRobMoments.R \preformatted{ sampleLmoments Computes sample L-moments sampleMED Returns sample median sampleIQR returns sample inter quartal range sampleSKEW returns robust sample skewness sampleKURT returns robust sample kurtosis } \emph{Bivariate Interpolation:} % stats-interpAkima.R | stats-interpKrige.R | stats-interpLinear.R \preformatted{ akimaInterp Interpolates irregularly spaced points akimaInterpp Interpolates and smoothes pointwise krigeInterp Kriges irregularly spaced data points linearInterp Interpolates irregularly spaced points linearInterpp Interpolates linearly pointwise } \emph{Utility Statistics Functions:} \preformatted{ colStats Computes sample statistics by col colSums Computes sums of values in each col colMeans Computes means of values in each col colSds Computes standard deviation of each col colVars Computes sample variance by col colSkewness Computes sample skewness by col colKurtosis Computes sample kurtosis by col colMaxs Computes maximum values in each col colMins Computes minimum values in each col colProds Computes product of values in each col colQuantiles Computes product of values in each col } % stats-rowStats.R \preformatted{ rowStats Computes sample statistics by row rowSums Computes sums of values in each row rowMeans Computes means of values in each row rowSds Computes standard deviation of each row rowVars Computes sample variance by row rowSkewness Computes sample skewness by row rowKurtosis Computes sample kurtosis by row rowMaxs Computes maximum values in each row rowMins Computes minimum values in each row rowProds Computes product of values in each row rowQuantiles Computes product of values in each row } } \section{3 Financial Return Distributions}{ \emph{Generalized Hyperbolic Distribution:} % dist-gh.R | dist-ghFit.R | dist-ghMode.R | dist-ghMoments.R | % dist-ghRobMoments.R \preformatted{ dghReturns Density for the GH distribution pghreturns Probability for the GH distribution qghreturns Quantiles for the GH distribution rghreturns Random variates for the GH distribution ghFitFits Fits parameters of the GH distribution ghMode Computes mode of the GH distribution. ghMean Returns true mean of the GH distribution ghVar Returns true variance of the GH distribution ghSkew Returns true skewness of the GH distribution ghKurt Returns true kurtosis of the GH distribution ghMoments Returns true n-th moment of the GH distribution ghMED Returns true median of te GH distribution ghIQR Returns true inter quartal range of te GH ghSKEW Returns true robust skewness of te GH ghKURT Returns true robust kurtosis of te GH } \emph{Hyperbolic Distribution:} % dist-hyp.R | dist-hypFit.R | dist-hypMode.R | dist-hypMoments.R % dist-hypRobMoments.R \preformatted{ dhyp Returns density for the HYP distribution phyp Returns probability for the HYP distribution qhyp Returns quantiles for the HYP distribution rhyp Returns random variates for the HYP distribution hypFit Fits parameters of the HYP distribution hypMode Computes mode of the HYP distribution hypMean Returns true mean of the HYP distribution hypVar R Returns true variance of the HYP distribution hypSkew Returns true skewness of the HYP distribution hypKurt Returns true kurtosis of the HYP distribution hypMoments Returns true n-th moment of the HYP distribution hypMED Returns true median of the HYP distribution hypIQR Returns true inter quartal range of the HYP hypSKEW Returns true robust skewness of the HYP hypKURT Returns true robust kurtosis of the HYP } \emph{Normal Inverse Gaussian:} % dist-nig.R | dist-nigFit.R | dist-nigMode.R | dist-nigMoments.R % dist-nigRobMoments.R \preformatted{ dnig Returns density for the NIG distribution pnig Returns probability for the NIG distribution qnig Returns quantiles for the NIG distribution rnig Returns random variates for the NIG distribution .pnigC fast C Implementation of function pnig() .qnigC fast CImplementation of function qnig() nigFit Fits parameters of a NIG distribution .nigFit.mle Uses max Log-likelihood estimation .nigFit.gmm Uses generalized method of moments .nigFit.mps Maximum product spacings estimation .nigFit.vmps Minimum variance mps estimation nigMode Computes mode of the NIG distribution nigMean Returns true mean of the NIG distribution nigVar Returns true variance of the NIG distribution nigSkew Returns true skewness of the NIG distribution nigKurt Returns true kurtosis of the NIG distribution nigMoments Returns true n-th moment of the NIG distribution nigMED Returns true median of the NIG distribution nigIQR Returns true inter quartal range of the NIG nigSKEW Returns true robust skewness of the NIG nigKURT Returns true robust kurtosis of the NIG } \emph{Generalized Hyperbolic Student-t Distribution:} % dist-ght.R | dist-ghtFit.R | dist-ghtMode.R | dist-ghtMoments.R | % dist-ghtRobMoments.R \preformatted{ dght Returns density for the GHT distribution pght Returns probability for the GHT distribution qght Returns quantiles for the GHT distribution rght Returns random variates for the GHT distribution ghtFit Fits parameters of the GHT distribution ghtMode Computes mode of the GHT distribution ghtMean Returns true mean of the NIG distribution ghtVar Returns true variance of the GHT distribution ghtSkew Returns true skewness of the GHT distribution ghtKurt Returns true kurtosis of the GHT distribution ghtMoments Returns true n-th moment of the GHT distribution ghtMED Returns true median of the GHT distribution ghtIQR Returns true inter quartal range of the GHT ghtSKEW Returns true robust skewness of the GHT ghtKURT Returns true robust kurtosis of the GHT } \emph{Stable Distribution:} % dist-stable.R | dist-stableFit.R | dist-stableMode.R \preformatted{ dstable Returns density for the stable distribution pstable Returns probability for the stable distribution qstable Returns quantiles for the stable distribution rstable Returns random variates for the dtsble distribution stableFit Fits parameters of a the stable distribution .phiStable Creates contour table for McCulloch estimators .PhiStable Contour table created by function .phiStable() .qStableFit Estimates parameters by McCulloch's approach .mleStableFit Estimates stable parameters by MLE approach .stablePlot Plots results of stable parameter estimates stableMode Computes mode of the stable distribution } \emph{Generalized Lambda Distribution:} % dist-gld.R | dist-gldFit.R | dist-gldMode.R | % dist-gldRobMoments.R \preformatted{ dgld Returns density for the GLD distribution pgld Returns probability for the GLD distribution qgld Returns quantiles for the GLD distribution rgld Returns random variates for the GLD distribution gldFit Fits parameters of the GLD distribution .gldFit.mle fits GLD using maximum log-likelihood .gldFit.mps fits GLD using maximum product spacings .gldFit.gof fits GLD using Goodness of Fit statistics % .ksGLDKolmogorov-Smirnov Statistics % .cvmGLDCramer-vonMise Statistics % .adGLDAnderson-Darling Statistics .gldFit.hist fits GLD using a histogram fit .gldFit.rob fits GLD using robust moments fit gldMode Computes mode of the GLD distribution. gldMED Returns true median of the GLD distribution gldIQR Returns true inter quartal range of the GLD gldSKEW Returns true robust skewness of the GLD gldKURT Returns true robust kurtosis of the GLD } \emph{Spline Smoothed Distribution:} % dist-ssd.R | dist-ssdFit.R \preformatted{ dssd Returns spline smoothed density function pssd Returns spline smoothed probability function qssd Returns spline smoothed quantile function rssd Returns spline smoothed random variates. ssdFit Fits parameters for a spline smoothed distribution } } \section{4 Hypthesis Testing}{ \emph{One Sample Nornality Tests:} % test-normalTest.R \preformatted{ ksnormTest One sample Kolmogorov-Smirnov normality test shapiroTest Shapiro-Wilk normality test jarqueberaTest Jarque-Bera normality test normalTest Normality tests S-Plus compatible call dagoTest D'Agostino normality test adTest Anderson-Darling normality test cvmTest Cramer-von Mises normality test lillieTest Lilliefors (KS) normality test pchiTest Pearson chi-square normality test sfTest Shapiro-Francia normality test jbTest Finite sample adjusted JB LM and ALM test } \emph{One Sample Location, Scale and variance Tests:} % test-locationTest.R | test-scaleTest.R | test-variance Test.R \preformatted{ locationTest Performs locations tests on two samples .tTest Unpaired t test for differences in mean .kw2Test Kruskal-Wallis test for differences in locations scaleTest Performs scale tests on two samples .ansariTest Ansari-Bradley test for differences in scale .moodTest Mood test for differences in scale varianceTest Performs variance tests on two samples .varfTest F test for differences in variances .bartlett2Test Bartlett's test for differences in variances .fligner2Test Fligner-Killeen test for differences in variances } \emph{Two Sample Tests:} % test-ks2Test.R \preformatted{ ks2Test Performs a two sample Kolmogorov-Smirnov test correlationTest Performs correlation tests on two samples pearsonTest Pearson product moment correlation coefficient kendallTest Kendall's tau correlation test spearmanTest Spearman's rho correlation test } \emph{Test Utilities:} % test-fHTEST.R \preformatted{ 'fHTEST' S4 Class Representation show.fHTEST S4 Print Method .jbALM Jarque Bera Augmented Lagrange Multiplier Data .jbLM Jarque-Bera Lagrange Multiplier Data .jbTable Finite sample p values for the Jarque Bera test .jbPlot Plots probabilities .pjb Returns probabilities for JB given quantiles .qjb Returns quantiles for JB given probabilities } } \section{5 Plotting Routines}{ \emph{Financial Time Series Plots:} % plot-seriesPlot.R \preformatted{ seriesPlot Dispalys a time series plot cumulatedPlot Displays cumulated series give returns returnPlot Displays returns given cumulated series drawdownPlot Displays drawdown series from returns } \emph{Correlation Plots:} % plot-acfPlot.R \preformatted{ acfPlot Displays tailored ACF plot pacfPlot Displays tailored partial ACF plot teffectPlot Displays the Taylor effect lacfPlot Displays lagged autocorrelations } \emph{Distribution Plots:} % plot-histPlot.R | plot-qqPlot.R plot-boxPlot.R \preformatted{ histPlot Returns tailored histogram plot densityPlot Returns tailored density plot logDensityPlot Returns tailored log density plot boxPlot Returns side-by-side standard box plot boxPercentile Plotreturns box-percentile plot qqnormPlot Returns normal quantile-quantile plot qqnigPlot Returns NIG quantile-quantile plot qqghtPlot Rreturns GHT quantile-quantile plot qqgldPlot Returns GLD quantile-quantile plot } \emph{ Time Series Aggregation Plots:} % plot-scalinglawPlot.R \preformatted{ scalinglawPlot Displays scaling law behavior } } \section{5. Matrix Computations and Linear Algebra}{ \emph{Elementar Matrix Operation Addons:} % matrix-kron.R | matrix-vech.R | matrix-pdl.R | matrix-tslag.R \preformatted{ kron Returns the Kronecker product vec Stacks a matrix as column vector vech Stacks a lower triangle matrix pdl Returns regressor matrix for polynomial lags tslag Returns Lagged/leading vector/matrix } \emph{Linear Algebra Addons:} % matrix-inv.R | matrix-norm.R | matrix-rk.R | matrix-tr.R \preformatted{ inv Returns the inverse of a matrix norm Returns the norm of a matrix rk Returns the rank of a matrix tr Returns the trace of a matrix } \emph{General Matrix Utility Addons:} % matrix-posDefinite.R | matrix-colVec.R | matrix-gridVector.R | % matrix-triang.R \preformatted{ isPositiveDefinite Checks if a matrix is positive definite makePositiveDefinite Forces a matrix to be positive definite colVec Creates a column vector from a data vector rowVec Creates a row vector from a data vector gridVector Creates from two vectors rectangular grid triang Extracs lower tridiagonal part from a matrix Triang Extracs upper tridiagonal part from a matrix } \emph{Selected Matrix Examples:} % matrix-hilbert.R | matrix-pascal.R \preformatted{ hilbert Creates a Hilbert matrix pascal Creates a Pascal matrix } } \section{6 Utility Functions}{ \emph{Color Utilities:} % utils-colorLocator.R | utils-colorTable.R | utils-colorPalette.R \preformatted{ colorLocator Plots Rs 657 named colors for selection colorMatrix Returns matrix of R's color names. colorTable Table of Color Codes and Plot Colors itself rainbowPalette Contiguous rainbow color palette heatPalette Contiguous heat color palette terrainPalette Contiguous terrain color palette topoPalette Contiguous topo color palette cmPalette Contiguous cm color palette greyPalette R's gamma-corrected gray palette timPalette Tim's Matlab like color palette rampPalette Color ramp palettes seqPalette Sequential color brewer palettes divPalette Diverging color brewer palettes qualiPalette Qualified color brewer palettes focusPalette Red, green blue focus palettes monoPalette Red, green blue mono palettes } \emph{Graphics Utilities:} % utils-symbolTable.R | utils-characterTable.R | utils-decor.R | % utils-interactivePlot.R \preformatted{ symbolTable Shows a table of plot symbols characterTable Shows a table of character codes decor Adds horizontal grid and L shaped box hgrid Adds horizontal grid lines vgrid Adds vertical grid lines boxL Adds L-shaped box box Adds unterlined box .xrug Adds rugs on x axis .yrug Adds rugs on y axis copyright Adds copyright notice interactivePlot Plots several graphs interactively } \emph{Special Function Utilities:} % utils-heaviside.R | utils-hessian.R \preformatted{ Heaviside Computes Heaviside unit step function Sign Another signum function Delta Computes delta function Boxcar Computes boxcar function Ramp Computes ramp function tsHessian Computes Two Sided Hessian matrix } \emph{Other Utilities:} % utils-unitrootNA.R | utils-getS4.R \preformatted{ .unirootNA Computes zero of a function without error exit getModel Extracts the model slot from a S4 object getTitle Extracts the title slot from a S4 object getDescription Extracts the description slot getSlot Extracts a specified slot from a S4 object } } \section{About Builtin Functions}{ Builtin functions are borrowed from contributed R packages and other sources. There are several reasons why we have modified and copied code from other sources and included in this package. * The builtin code is not available on Debian, so that Linux users have no easy acces to this code. * The original code conflicts with other code from this package or conflicts with Rmetrics design objectives. * We only need a very small piece of functionality from the original package which may depend on other packages which are not needed. * The package from which we builtin the code is under current development, so that the functions often change and thus leads to unexpectect behavior in the Rmetrics packages. * The package may be incompatible since it uses other time date and time series classes than the 'timeDate' and 'timeSeries' objects and methods from Rmetrics. We put the code in script files named \emph{builtin-funPackage.R} where "fun" denotes the (optional) major function name, and "Package" the name of the contributed package from which we copied the original code. Builtin functions include: \preformatted{ gelGmm gll function from gmm package gmmGMM gmm function from gmm package kweightsSandwhich kweights from sandwhich package glGld gl functions from gld package ssdenGss ssden from the gss package hypHyperbolicDist hyp from HyperbolicDist package } } \section{Compiled Fortran and C Code:}{ \preformatted{ gld.c source code from gld package nig.c source code from Kersti Aas gss.f source code fromsandwhich package } } \section{About Rmetrics:}{ The \code{fBasics} Rmetrics package is written for educational support in teaching "Computational Finance and Financial Engineering" and licensed under the GPL. } \keyword{package} fBasics/man/plot-qqPlot.Rd0000644000176200001440000000617114660430457015140 0ustar liggesusers\name{QuantileQuantilePlots} \alias{qqnormPlot} \alias{qqnigPlot} \alias{qqghtPlot} \alias{qqgldPlot} \title{Quantile-Quantile plots} \description{ Returns quantile-quantile plots for the normal, the normal inverse Gaussian, the generalized hyperbolic Student-t and the generalized lambda distribution. } \usage{ qqnormPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) qqnigPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) qqghtPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) qqgldPlot(x, labels = TRUE, col = "steelblue", pch = 19, title = TRUE, mtext = TRUE, grid = FALSE, rug = TRUE, scale = TRUE, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by\code{as.timeSeries} into an object of class \code{"timeSeries"}. The latter case, other then \code{"timeSeries"} objects, is more or less untested. } \item{labels}{ a logical flag, should the plot be returned with default labels and decorated in an automated way? By default \code{TRUE}. } \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{pch}{ an integer value, by default 19. Which plot character should be used in the plot? } \item{title}{ a logical flag, by default \code{TRUE}. Should a default title added to the plot? } \item{mtext}{ a logical flag, by default \code{TRUE}. Should a marginal text be printed on the third site of the graph? } \item{grid}{ a logical flag, should a grid be added to the plot? By default \code{TRUE}. To plot a horizontal lines only use \code{grid="h"} and for vertical lines use \code{grid="h"}, respectively. } \item{rug}{ a logical flag, by default \code{TRUE} . Should a rug representation of the data added to the plot? } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled for the investigation? } \item{\dots}{ optional arguments to be passed. } } \details{ List of Functions: \tabular{ll}{ \code{qqnormPlot} Produces a tailored Normal quantile-quantile plot, \cr \code{qqnigPlot} Produces a tailored NIG quantile-quantile plot, \cr \code{qqghtPlot} Produces a tailored GHT quantile-quantile plot, \cr \code{qqgldPlot} Produces a tailored GLD quantile-quantile plot.} } \value{ displays a quantile-quantile plot. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## data data(LPP2005REC, package = "timeSeries") SPI <- LPP2005REC[, "SPI"] plot(SPI, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") qqnormPlot(SPI) } \keyword{hplot} fBasics/man/data-examples.Rd0000644000176200001440000001431114660347607015430 0ustar liggesusers\name{fBasicsData} \alias{fBasicsData} \alias{Capitalization} \alias{cars2} \alias{DowJones30} \alias{HedgeFund} \alias{msft.dat} \alias{nyse} \alias{PensionFund} \alias{swissEconomy} \alias{SWXLP} \alias{usdthb} %-------------------------------------------------------------- \title{fBasics data sets} \description{ The following data sets are part of this package: \tabular{ll}{ \code{Capitalization} \tab Market capitalization of domestic companies, \cr \code{cars2} \tab Data for various car models, \cr \code{DowJones30} \tab Down Jones 30 stocks, \cr \code{HedgeFund} \tab Hennessee Hedge Fund Indices, \cr \code{msft.dat} \tab Daily Microsoft OHLC prices and volume, \cr \code{nyse} \tab NYSE composite Index, \cr \code{PensionFund} \tab Swiss Pension Fund LPP-2005, \cr \code{swissEconomy} \tab Swiss Economic Data, \cr \code{SWXLP} \tab Swiss Pension Fund LPP-2000, \cr \code{usdthb} \tab Tick data of USD to THB. } } %-------------------------------------------------------------- \details{ All datasets are data frames. A brief description is given below. \bold{Capitalization:}\cr \code{Capitalization} contains market capitalization of 13 domestic companies for 6 years (from 2003 to 2008) in USD millions. Each row contains the data for one company/stock exchange. \bold{cars2:}\cr \code{cars2} contains columns \code{rowNames} (model), \code{Price}, \code{Country}, \code{Reliability}, \code{Mileage}, (\code{Type}), (\code{Weight}), \code{Disp.} (engine displacement) and \code{HP} (\code{net horsepower}) reprsenting the indicated properties of 60 car models. \bold{DowJones30:}\cr \code{DowJones30} contains 2529 daily observations from the \sQuote{Dow Jones 30} Index series. The first row contains the dates (from 1990-12-31 to 2001-01-02). Each of the remaining thirty columns represents the closing price of a stock in the Index. \bold{HedgeFund:}\cr \code{HedgeFund} contains monthly percentage returns of 16 hedge fund strategies from Hennessee Group LLC for year 2005. \bold{msft.dat:}\cr \code{msft.dat} contains daily prices (open, high, low and close) and volumes for the Microsoft stocks. It is a data frame with column names \code{"\%Y-\%m-\%d"}, \code{"Open"}, \code{"High"}, \code{"Low"}, \code{"Close"}, \code{"Volume"}. \emph{Note:} there is a dataset, \code{MSFT}, in package \pkg{timeSeries} which contains the same data but is of class \code{"timeSeries"}. \bold{nyse:}\cr \code{nyse} contains daily records of the NYSE Composite Index from 1966-01-04 to 2002-12-31 (9311 observations). The data is in column \code{"NYSE"} (second column). The first column contains the dates. \bold{PensionFund:}\cr \code{PensionFund} is a daily data set of the Swiss pension fund benchmark LPP-2005. The data set ranges from 2005-11-01 to 2007-04-11. The columns are named: SBI, SPI, SII, LMI, MPI, ALT, LPP25, LPP40, LPP60. \bold{swissEconomy:}\cr \code{swissEconomy} contains the GDP per capita (\code{GDPR}), exports (\code{EXPO}), imports (\code{IMPO}), interest rates (\code{INTR}), inflation (\code{INFL}), unemployment (\code{UNEM}) and population (\code{POPU}) foryears 1964 to 1999 for Switzerland. \bold{SWXLP:}\cr \code{SWXLP} is a daily data set of the Swiss pension fund benchmark LPP-2000. The data set ranges from 2000-01-03 to 2007-05-08 (1917 observations). The first column contains the dates. The remaining columns are named: SBI, SPI, SII, LP25, LP40, LP60. \bold{usdthb:}\cr \code{usdthb} Tick data of US Dollar (USD) in Thailand Bhat (THB) collected from Reuters. The date is in the first column in YYYYMMDDhhmm format. The remaining columns contain: delay time (\code{DELAY}), contributor (\code{CONTRIBUTOR}), bid (\code{BID}) and ask (\code{ASK}) prices, and quality flag (\code{FLAG}). It covers the Asia FX crisis in June 1997. } %-------------------------------------------------------------- \references{ \bold{Capitalization:}\cr \emph{World Federation of Stock Exchanges}, http://www.world-exchanges.org/statistics. \bold{cars2:}\cr Derived from the car90 dataset within the rpart package. The car90 dataset is based on the car.all dataset in S-PLUS. Original data comes from: April 1990, \emph{Consumer Reports Magazine}, pages 235-255, 281-285 and 287-288. % Package rpart (dataset: car90) % This is derived (with permission) from the data set car.all % in S-PLUS, but with some further clean up of variable names and definitions. % http://ftp.yzu.edu.tw/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf % http://www.uni-muenster.de/ZIV.BennoSueselbeck/s-html/helpfiles/car.all.html \bold{DowJones30}\cr https://www.yahoo.com. \bold{HedgeFund:}\cr http://www.hennesseegroup.com/indices/returns/year/2005.html. \bold{msft.dat:}\cr https://www.yahoo.com. \bold{nyse:}\cr https://www.nyse.com. \bold{PensionFund:}\cr SBI, SPI, SII: SIX (Swiss Exchange Zurich); LPP25, LPP40, LPP60: Banque Pictet Geneva; LMI, MPI, ALT: Recalculated from the indices and benchmarks. \bold{swissEconomy:}\cr https://www.oecd.org/ and https://www.imf.org/. \bold{SWXLP:}\cr SBI, SPI, SII: SIX (Swiss Exchange Zurich); LPP25, LPP40, LPP60: Banque Pictet Geneva. \bold{usdthb:}\cr Reuters Select Feed Terminal (1997). % \bold{Example:}\cr % McCullough B.D., Renfro C.G. (1998); % \emph{Benchmarks and Software Standards: A Case Study of GARCH Procedures}, % Journal of Economic and Social Measurement 25, 59--71. } %-------------------------------------------------------------- \examples{ ## Plot DowJones30 Example Data Set series <- timeSeries::as.timeSeries(DowJones30) head(series) plot(series[,1:6], type = "l") ## msft.dat contains (almost?) the same data as MSFT in package timeSeries data(MSFT, package = "timeSeries") m1 <- as.matrix(msft.dat[, -1]) # drop date stamps in column 1 m2 <- as.matrix(MSFT) all.equal(m1, m2, check.attributes = FALSE) # TRUE ## compare the dates: all.equal(format(msft.dat[ , 1]), format(time(MSFT))) # TRUE } \keyword{datasets} fBasics/man/volatility.Rd0000644000176200001440000000115714660437032015077 0ustar liggesusers\name{volatility} \alias{volatility} \alias{volatility.default} \title{Compute volatility} \description{ Generic function for volatility computations. } \usage{ volatility(object, \dots) \method{volatility}{default}(object, \dots) } \arguments{ \item{object}{ an object from which to extract or compute the volatility. } \item{\dots}{ arguments for methods. Ignored by the default method. } } \details{ \code{volatility} is a generic function, whose default method centers and squares the values in \code{object}. Other packages can (and do) define methods for it. } \keyword{stats} fBasics/man/dist-hypSlider.Rd0000644000176200001440000000072714650724110015600 0ustar liggesusers\name{hypSlider} \alias{hypSlider} \title{Hyperbolic distribution slider} \description{ Displays interactively the dependence of the hyperbolic distribution on its parameters. } \usage{ hypSlider() } \value{ a tcl/tk based graphical user interface. This is a nice display for educational purposes to investigate the densities and probabilities of the hyperbolic distribution. } \examples{ ## hypSlider - # } \keyword{distribution} fBasics/man/dist-ghtRobMoments.Rd0000644000176200001440000000273714650724110016430 0ustar liggesusers\name{ghtRobMoments} \alias{ghtRobMoments} \alias{ghtMED} \alias{ghtIQR} \alias{ghtSKEW} \alias{ghtKURT} \title{Robust Moments for the GHT} \description{ Computes the first four robust moments for the generalized hyperbolic Student-t. } \usage{ ghtMED(beta = 0.1, delta = 1, mu = 0, nu = 10) ghtIQR(beta = 0.1, delta = 1, mu = 0, nu = 10) ghtSKEW(beta = 0.1, delta = 1, mu = 0, nu = 10) ghtKURT(beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{beta}{skewness parameter in the range \code{(0, alpha)}.} \item{delta}{scale parameter, must be zero or positive.} \item{mu}{location parameter, by default 0.} \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } } \details{ The parameters are those of the first parameterization. } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{ght} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz. } \examples{ ## ghtMED - # Median: ghtMED(beta = 0.1, delta = 1, mu = 0, nu = 10) ## ghtIQR - # Inter-quartile Range: ghtIQR(beta = 0.1, delta = 1, mu = 0, nu = 10) ## ghtSKEW - # Robust Skewness: ghtSKEW(beta = 0.1, delta = 1, mu = 0, nu = 10) ## ghtKURT - # Robust Kurtosis: ghtKURT(beta = 0.1, delta = 1, mu = 0, nu = 10) } \keyword{distribution} fBasics/man/matrix-triang.Rd0000644000176200001440000000400514660350355015462 0ustar liggesusers\name{triang} \alias{triang} \alias{Triang} \title{Upper and lower triangular matrices} \description{ Extracs the upper or lower triangular part from a matrix. } \usage{ triang(x) Triang(x) } \arguments{ \item{x}{ a numeric matrix. } } \details{ \code{triang} and \code{Triang} transform a square matrix to a lower or upper triangular form. The functions just replace the remaining values with zeroes and work with non-square matrices, as well. A triangular matrix is either an upper triangular matrix or lower triangular matrix. For the first case all matrix elements \code{a[i,j]} of matrix \code{A} are zero for \code{i>j}, whereas in the second case we have just the opposite situation. A lower triangular matrix is sometimes also called left triangular. In fact, triangular matrices are so useful that much of computational linear algebra begins with factoring or decomposing a general matrix or matrices into triangular form. Some matrix factorization methods are the Cholesky factorization and the LU-factorization. Even including the factorization step, enough later operations are typically avoided to yield an overall time savings. Triangular matrices have the following properties: the inverse of a triangular matrix is a triangular matrix, the product of two triangular matrices is a triangular matrix, the determinant of a triangular matrix is the product of the diagonal elements, the eigenvalues of a triangular matrix are the diagonal elements. } \value{ a matrix of the same dimensions as \code{x} with the elements above or below the main diagonal set to zeroes } \references{ Higham, N.J., (2002); \emph{Accuracy and Stability of Numerical Algorithms}, 2nd ed., SIAM. Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(3) P ## Create lower triangle matrix L = triang(P) L } \keyword{math} fBasics/man/plot-seriesPlot.Rd0000644000176200001440000000760514660431750016011 0ustar liggesusers\name{TimeSeriesPlots} \alias{seriesPlot} \alias{returnPlot} \alias{cumulatedPlot} \alias{drawdownPlot} \title{Financial time series plots} \description{ Produces an index/price, a cumulated return, a return, or a drawdown plot. } \usage{ seriesPlot(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) cumulatedPlot(x, index = 100, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) returnPlot(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) drawdownPlot(x, labels = TRUE, type = "l", col = "steelblue", title = TRUE, grid = TRUE, box = TRUE, rug = TRUE, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other then \code{timeSeries} objects, is more or less untested. } \item{index}{ a numeric value, by default 100. The function cumulates column by columN the returns and multiplies the result with the index value: \code{index * exp(colCumsums(x))}. } \item{labels}{ a logical flag, should the plot be returned with default labels and decorated in an automated way? By default \code{TRUE}. } \item{type}{ what type of plot should be drawn? By default we use a line plot, \code{type="l"}. An alternative plot style which produces nice figures is for example \code{type="h"}. } \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{title}{ a logical flag, by default TRUE. Should a default title added to the plot? } \item{grid}{ a logical flag, should a grid be added to the plot? By default \code{TRUE}. } \item{box}{ a logical flag, should a box be added to the plot? By default \code{TRUE}. } \item{rug}{ a logical flag, by default TRUE. Should a rug representation of the data added to the plot? } \item{\dots}{ optional arguments to be passed. } } \details{ List of Functions: \tabular{ll}{ \code{seriesPlot} \tab Returns a tailored return series plot, \cr \code{cumulatedPlot} \tab Displays a cumulated series given the returns, \cr \code{returnPlot} \tab Displays returns given the cumulated series, \cr \code{drawdownPlot} \tab Displays drawdowns given the return series.} The plot functions can be used to plot univariate and multivariate time series of class \code{timeSeries}. The graphical parameters \code{type} and \code{col} can be set by the values specified through the argument list. In the case of multivariate time series \code{col} can be specified by the values returned by a color palette. Automated titles including main title, x- and y-lables, grid lines, box style and rug represenatations cann be selected by setting these arguments to \code{TRUE} which is the default. If the title flag is unset, then the main title, x-, and y-labels are empty strings. This allows to set user defined labels with the function \code{title} after the plot is drawn. Beside \code{type}, \code{col}, \code{main}, \code{xlab} and \code{ylab}, all other \code{par} arguments can be passed to the \code{plot} function. If the \code{labels} flag is unset to \code{FALSE}, then no decorations will be added tothe plot, and the plot can be fully decorated by the user. } \value{ displays a time series plot } \examples{ data(LPP2005REC, package = "timeSeries") tS <- as.timeSeries(LPP2005REC) seriesPlot(tS) } \keyword{hplot} fBasics/man/dist-nigFit.Rd0000644000176200001440000000717014657144173015070 0ustar liggesusers\name{nigFit} \alias{nigFit} \title{Fit of a Normal Inverse Gaussian Distribution} \description{ Estimates the parameters of a normal inverse Gaussian distribution. } \usage{ nigFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, method = c("mle", "gmm", "mps", "vmps"), scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{alpha, beta, delta, mu}{ The parameters are \code{alpha}, \code{beta}, \code{delta}, and \code{mu}:\cr shape parameter \code{alpha}; skewness parameter \code{beta}, \code{abs(beta)} is in the range (0, alpha); scale parameter \code{delta}, \code{delta} must be zero or positive; location parameter \code{mu}, by default 0. These is the meaning of the parameters in the first parameterization \code{pm=1} which is the default parameterization selection. In the second parameterization, \code{pm=2} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4} \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. } \item{description}{ a character string which allows for a brief description. } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{method}{ a character string. Either \code{"mle"}, Maximum Likelihood Estimation, the default, \code{"gmm"} Gemeralized Method of Moments Estimation, \code{"mps"} Maximum Product Spacings Estimation, or \code{"vmps"} Minimum Variance Product Spacings Estimation. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{title}{ a character string which allows for a project title. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{x}{ a numeric vector. } \item{\dots}{ parameters to be parsed. } } \value{ an object from class \code{"\linkS4class{fDISTFIT}"}. Slot \code{fit} is a list, whose components depend on the method. See \code{"\linkS4class{fDISTFIT}"} for the meaning of the most common ones. Here is an \strong{informal} list of components for the various methods: for mle: \code{par}, \code{scale}, \code{estimate}, \code{minimum}, \code{code} plus components from \code{nlminb()} plus additions from \code{.distStandardErrors()}; for gmm: only \code{estimate}; for mps and vmps: \code{estimate}, \code{minimum}, \code{error} (s.e.'s), \code{code}. } \examples{ ## Simulate Random Variates set.seed(1953) s <- rnig(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0) nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE, trace = FALSE) } \keyword{distribution} fBasics/man/utils-printControl.Rd0000644000176200001440000000076214660436622016537 0ustar liggesusers\name{print} \alias{print.control} \title{Print control} \description{ Unlists and prints a control object. } \usage{ \method{print}{control}(x, \dots) } \arguments{ \item{x}{ the object to be printed. } \item{\dots}{ arguments to be passed. } } \value{ prints control } \examples{ control <- list(n = 211, seed = 54, name = "generator") print(control) class(control) <- "control" print(control) } \keyword{programming} fBasics/man/matrix-rk.Rd0000644000176200001440000000200714660350336014611 0ustar liggesusers\name{rk} \alias{rk} \title{The rank of a matrix} \description{ Computes the rank of a matrix. } \usage{ rk(x, method = c("qr", "chol")) } \arguments{ \item{x}{ a numeric matrix. } \item{method}{ a character string. For \code{method = "qr"} the rank is computed as \code{qr(x)\$rank}, or alternatively for \code{method = "chol"} the rank is computed as \code{attr(chol(x, pivot = TRUE), "rank")}. } } \details{ The function \code{rk} computes the rank of a matrix which is the dimension of the range of the matrix corresponding to the number of linearly independent rows or columns of the matrix, or to the number of nonzero singular values. The rank of a matrix is also named linear map. } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Compute the Rank: rk(P) rk(P, "chol") } \keyword{math} fBasics/man/dist-sght.Rd0000644000176200001440000000457214650724110014604 0ustar liggesusers\name{sght} \alias{sght} \alias{dsght} \alias{psght} \alias{qsght} \alias{rsght} \concept{standardized generalized hyperbolic Student-t distribution} \title{Standardized generalized hyperbolic Student-t Distribution} \description{ Density, distribution function, quantile function and random generation for the standardized generalized hyperbolic Student-t distribution. } \usage{ dsght(x, beta = 0.1, delta = 1, mu = 0, nu = 10, log = FALSE) psght(q, beta = 0.1, delta = 1, mu = 0, nu = 10) qsght(p, beta = 0.1, delta = 1, mu = 0, nu = 10) rsght(n, beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{beta}{ numeric value, \code{beta} is the skewness parameter in the range \code{(0, alpha)}. } \item{delta}{ numeric value, the scale parameter, must be zero or positive. } \item{mu}{ numeric value, the location parameter, by default 0. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } } \details{ \code{dsght} gives the density, \code{psght} gives the distribution function, \code{qsght} gives the quantile function, and \code{rsght} generates random deviates. These are the parameters in the first parameterization. } \value{ numeric vector } \author{ Diethelm Wuertz } \examples{ ## rsght - set.seed(1953) r = rsght(5000, beta = 0.1, delta = 1, mu = 0, nu = 10) plot(r, type = "l", col = "steelblue", main = "gh: zeta=1 rho=0.5 lambda=1") ## dsght - # Plot empirical density and compare with true density: hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, length = 501) lines(x, dsght(x, beta = 0.1, delta = 1, mu = 0, nu = 10)) ## psght - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, psght(x, beta = 0.1, delta = 1, mu = 0, nu = 10)) ## qsght - # Compute Quantiles: round(qsght(psght(seq(-5, 5, 1), beta = 0.1, delta = 1, mu = 0, nu =10), beta = 0.1, delta = 1, mu = 0, nu = 10), 4) } \keyword{distribution} fBasics/man/utils-getS4.Rd0000644000176200001440000000506614660435163015031 0ustar liggesusers\name{getS4} \alias{getS4} \alias{getCall} \alias{getCall,ANY-method} \alias{getModel} \alias{getModel.default} \alias{getTitle} \alias{getDescription} \alias{getSlot} \alias{getArgs} \title{General S4 Class Extractor Functions} \description{ A collection and description of functions to extract slots from S4 class objects. \cr The extractor functions are: \tabular{ll}{ % \code{getCall} \tab Extracts the call slot from a S4 object, \cr \code{getModel} \tab Extracts the model slot from a S4 object, \cr \code{getTitle} \tab Extracts the title slot from a S4 object, \cr \code{getDescription} \tab Extracts the description slot from a S4 object, \cr \code{getSlot} \tab Extracts a specified slot from a S4 object, \cr \code{getArgs} \tab Shows the arguments of a S4 function. } Since \R version 2.14.0, a generic \code{getCall()} is part of \R; for earlier versions, we had provided a simple version for S4 objects. } \usage{ % getCall(x, \dots) getModel(object) getTitle(object) getDescription(object) getSlot(object, slotName) getArgs(f, signature) } \arguments{ \item{f}{ a generic function or the character-string name of one. } \item{object}{ an object of class S4. % (For \code{getCall()} and \R 2.14.0 and newer: \dQuote{any} object.) } \item{signature}{ the signature of classes to match to the arguments of \code{f} } \item{slotName}{ a character string, the name of the slot to be extracted from the S4 object. } } \value{ for \code{getModel}, \code{getTitle}, \code{getDescription}, and \code{getSlot} - the content of the corresponding slot. for \code{getArgs} the names of the arguments. } \examples{ ## Example S4 Representation: ## Hyothesis Testing with Control Settings setClass("hypTest", representation( call = "call", data = "numeric", test = "list", description = "character") ) ## Shapiro Wilk Normaility Test swTest = function(x, description = "") { ans = shapiro.test(x) class(ans) = "list" new("hypTest", call = match.call(), data = x, test = ans, description = description) } test = swTest(x = rnorm(500), description = "500 RVs") ## Extractor Functions: isS4(test) getCall(test) getDescription(test) ## get arguments args(returns) getArgs(returns) getArgs("returns") getArgs(returns, "timeSeries") getArgs("returns", "timeSeries") } \keyword{programming} fBasics/man/utils-symbolTable.Rd0000644000176200001440000000266714660437001016315 0ustar liggesusers\name{symbolTable} \alias{symbolTable} \title{Table of symbols} \description{ Displays a table of plot characters and symbols. } \usage{ symbolTable(font = par('font'), cex = 0.7) } \arguments{ \item{cex}{ a numeric value, determines the character size, the default size is 0.7. } \item{font}{ an integer value, the number of the \code{font}, by default font number 1. } } \value{ displays a table with the plot characters and symbols numbered from 0 to 255 and returns invisibly the name of the font. } \note{ Symbols with codes on the range 128-255 are not legitimate in some locales, most notably UTF-8. Moreover, what happens with non-ASCII characters in plots is system dependent and depends on the graphics device, as well. Use of such characters is not recommended for portable code. From version 4031.95 of package \code{fBasics}, the characters are always defined as Latin1. In particular, in UTF8 locales the system converts them internally to UTF8. Still some symbols are not usable and non-ASCII symbols are not recommended, as pointed out above. For details, see the help page of \code{points()}, in particular the discussion of its argument \code{pch}. } \seealso{ \code{\link{characterTable}}, \code{\link{colorTable}} \code{\link[grDevices]{pdf}} for discussion of encodings for the pdf device } \examples{ # symbolTable() } \keyword{programming} fBasics/man/stats-sampleRobMoments.Rd0000644000176200001440000000111614660433431017314 0ustar liggesusers\name{sampleLMoments} \alias{sampleLMoments} \alias{sampleLmoments} \title{Sample L-moments} \description{ Computes L-moments from an empirical sample data set. } \usage{ sampleLmoments(x, rmax = 4) } \arguments{ \item{x}{ numeric vector, the sample values. } \item{rmax}{ an integer value, the number of L-moments to be returned. } } \value{ a named numeric vector of length \code{rmax} with names \code{c("L1", "L2", ..., "L")} } \author{ Diethelm Wuertz } \examples{ x <- rt(100, 4) sampleLmoments(x) } \keyword{distribution} fBasics/man/plot-returnSeriesGUI.Rd0000644000176200001440000000140214660430571016704 0ustar liggesusers\name{ReturnSeriesGUI} \alias{returnSeriesGUI} \title{Return series plots} \description{ A graphical user interface to display finanical time series plots. \code{returnSeriesGUI} opens a GUI for return series plots. } \usage{ returnSeriesGUI(x) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{timeSeries}. The latter case, other than \code{timeSeries} objects, is more or less untested. } } \value{ For \code{returnSeriesGUI} function, besides the graphical user interface no values are returned. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \keyword{hplot} fBasics/man/dist-hyp.Rd0000644000176200001440000000771214650724110014436 0ustar liggesusers\name{hyp} \alias{hyp} \alias{dhyp} \alias{phyp} \alias{qhyp} \alias{rhyp} \concept{hyperbolic distribution} \title{Hyperbolic distribution} \description{ Density, distribution function, quantile function and random generation for the hyperbolic distribution. } \usage{ dhyp(x, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1, log = FALSE) phyp(q, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1, \dots) qhyp(p, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1, \dots) rhyp(n, alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1) } \arguments{ \item{x, q}{ numeric vector of quantiles. } \item{p}{ numeric vector of probabilities. } \item{n}{ number of observations. } \item{alpha}{ shape parameter, a positive number. \code{alpha} can also be a vector of length four, containing \code{alpha}, \code{beta}, \code{delta} and \code{mu} (in that order). } \item{beta}{ skewness parameter, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{pm}{ integer number specifying the parameterisation, one of \code{1}, \code{2}, \code{3}, or \code{4}. The default is the first parameterization. } \item{log}{ a logical value, if \code{TRUE}, probabilities \code{p} are given as \code{log(p)}. } \item{\dots}{ arguments to be passed to the function \code{integrate}. } } \details{ \code{dhyp} gives the density, \code{phyp} gives the distribution function, \code{qhyp} gives the quantile function, and \code{rhyp} generates random deviates. The meaning of the parameters given above corresponds to the first parameterization, \code{pm = 1}, which is the default. In the second parameterization, \code{pm=2}, \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3}, \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4}, \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. The generator \code{rhyp} is based on the HYP algorithm given by Atkinson (1982). } \value{ numeric vector } \author{ David Scott for code implemented from \R's contributed package \pkg{HyperbolicDist}. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## hyp - set.seed(1953) r = rhyp(5000, alpha = 1, beta = 0.3, delta = 1) plot(r, type = "l", col = "steelblue", main = "hyp: alpha=1 beta=0.3 delta=1") ## hyp - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dhyp(x, alpha = 1, beta = 0.3, delta = 1)) ## hyp - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, phyp(x, alpha = 1, beta = 0.3, delta = 1)) ## hyp - # Compute Quantiles: qhyp(phyp(seq(-5, 5, 1), alpha = 1, beta = 0.3, delta = 1), alpha = 1, beta = 0.3, delta = 1) } \keyword{distribution} fBasics/man/matrix-pdl.Rd0000644000176200001440000000132014650724110014743 0ustar liggesusers\name{pdl} \alias{pdl} \title{Polynomial distributed lags} \description{ Creates a regressor matrix for polynomial distributed lags. } \usage{ pdl(x, d = 2, q = 3, trim = FALSE) } \arguments{ \item{x}{ a numeric vector. } \item{d}{ an integer specifying the order of the polynomial. } \item{q}{ an integer specifying the number of lags to use in creating polynomial distributed lags. This must be greater than \code{d}. } \item{trim}{ a logical flag; if \code{TRUE}, the missing values at the beginning of the returned matrix will be trimmed. } } \seealso{ \code{\link{tslag}} } \examples{ ## pdl - # } \keyword{math} fBasics/man/matrix-pascal.Rd0000644000176200001440000000214014660350264015436 0ustar liggesusers\name{pascal} \alias{pascal} \title{Pascal matrix} \description{ Creates a Pascal matrix. } \usage{ pascal(n) } \arguments{ \item{n}{ an integer value, the dimension of the square matrix. } } \details{ The function \code{pascal} generates a Pascal matrix of order \code{n} which is a symmetric positive definite matrix with integer entries made up from Pascal's triangle. The determinant of a Pascal matrix is 1. The inverse of a Pascal matrix has integer entries. If \code{lambda} is an eigenvalue of a Pascal matrix, then \code{1/lambda} is also an eigenvalue of the matrix. Pascal matrices are ill-conditioned. } \references{ Call G.S., Velleman D.J., (1993); \emph{Pascal's matrices}, American Mathematical Monthly 100, 372--376. Edelman A., Strang G., (2004); \emph{Pascal Matrices}, American Mathematical Monthly 111, 361--385. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Determinant det(pascal(5)) det(pascal(10)) det(pascal(15)) det(pascal(20)) } \keyword{math} fBasics/man/matrix-posDefinite.Rd0000644000176200001440000000134314660350317016447 0ustar liggesusers\name{positiveDefinite} \alias{positiveDefinite} \alias{isPositiveDefinite} \alias{makePositiveDefinite} \title{Positive definite matrices} \description{ Checks if a matrix is positive definite and/or forces a matrix to be positive definite. } \usage{ isPositiveDefinite(x) makePositiveDefinite(x) } \arguments{ \item{x}{ a square numeric matrix. } } \details{ The function \code{isPositiveDefinite} checks if a square matrix is positive definite. The function \code{makePositiveDefinite} forces a matrix to be positive definite. } \author{ Korbinian Strimmer. } \examples{ # the 3x3 Pascal Matrix is positive define isPositiveDefinite(pascal(3)) } \keyword{math} fBasics/man/plot-scalinglawPlot.Rd0000644000176200001440000000502114660430707016632 0ustar liggesusers\name{ScalingLawPlot} \alias{scalinglawPlot} \title{Scaling law behaviour} \description{ Evaluates the scaling exponent of a financial return series and plots the scaling law. } \usage{ scalinglawPlot(x, span = ceiling(log(length(x)/252)/log(2)), doplot = TRUE, labels = TRUE, trace = TRUE, \dots) } \arguments{ \item{x}{ an uni- or multivariate return series of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries()} into an object of class \code{"timeSeries"}. } \item{span}{ an integer value, determines for the \code{qqgaussPlot} the plot range, by default 5, and for the \code{scalingPlot} a reasonable number of points for the scaling range, by default daily data with 252 business days per year are assumed. } \item{doplot}{ a logical value. Should a plot be displayed? } \item{labels}{ a logical value. Whether or not x- and y-axes should be automatically labeled and a default main title should be added to the plot. By default \code{TRUE}. } \item{trace}{ a logical value. Should the computation be traced? } \item{\dots}{ arguments to be passed. } } \value{ a list with the following components: \item{Intercept}{intercept,} \item{Exponent}{the scaling exponent,} \item{InverseExponent}{the inverse of the scaling component.} } \details{ \bold{Scaling Behavior:} \cr\cr The function \code{scalingPlot} plots the scaling law of financial time series under aggregation and returns an estimate for the scaling exponent. The scaling behavior is a very striking effect of the foreign exchange market and also other markets expressing a regular structure for the volatility. Considering the average absolute return over individual data periods one finds a scaling power law which relates the mean volatility over given time intervals to the size of these intervals. The power law is in many cases valid over several orders of magnitude in time. Its exponent usually deviates significantly from a Gaussian random walk model which implies 1/2. } \references{ Taylor S.J. (1986); \emph{Modeling Financial Time Series}, John Wiley and Sons, Chichester. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \examples{ ## data data(LPP2005REC, package = "timeSeries") SPI <- LPP2005REC[, "SPI"] plot(SPI, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") ## Scaling Law Effect scalinglawPlot(SPI) } \keyword{hplot} fBasics/man/utils-colIds.Rd0000644000176200001440000000145514660434611015253 0ustar liggesusers\name{Ids} \alias{Ids} \alias{colIds} \alias{rowIds} \alias{colIds<-} \alias{rowIds<-} \title{Set and retrieve column/row names} \description{ Sets and retrieves column and row names. The functions are for compatibility with SPlus. } \usage{ colIds(x, \dots) rowIds(x, \dots) } \arguments{ \item{x}{ a numeric matrix. } \item{\dots}{ passed on to \code{colnames} or \code{rownames}. } } \details{ Usually in R the functions \code{colnames} and \code{rownames} are used to retrieve and set the names of matrices. The functions \code{rowIds} and \code{colIds}, are S-Plus like synonyms. } \examples{ ## Create Pascal Matrix: P <- pascal(3) P rownames(P) <- letters[1:3] P colIds(P) <- as.character(1:3) P } \keyword{math} fBasics/man/matrix-colVec.Rd0000644000176200001440000000104714660350043015406 0ustar liggesusers\name{colVec} \alias{colVec} \alias{rowVec} \title{Column and row vectors} \description{ Creates a column or row vector from a numeric vector. } \usage{ colVec(x) rowVec(x) } \arguments{ \item{x}{ a numeric vector. } } \details{ \code{colVec} and \code{rowVec} transform a vector into a column and row vector, respectively. A column vector is a matrix object with one column, and a row vector is a matrix object with one row. } \examples{ x = rnorm(5) colVec(x) rowVec(x) } \keyword{math} fBasics/man/dist-ghtMoments.Rd0000644000176200001440000000402514650724110015755 0ustar liggesusers\name{ghtMoments} \alias{ghtMoments} \alias{ghtMean} \alias{ghtVar} \alias{ghtSkew} \alias{ghtKurt} \alias{ghtMoments} \title{Generalized Hyperbolic Student-t Moments} \description{ Calculates moments of the generalized hyperbolic Student-t distribution. } \usage{ ghtMean(beta=0.1, delta=1, mu=0, nu=10) ghtVar(beta=0.1, delta=1, mu=0, nu=10) ghtSkew(beta=0.1, delta=1, mu=0, nu=10) ghtKurt(beta=0.1, delta=1, mu=0, nu=10) ghtMoments(order, type = c("raw", "central", "mu"), beta=0.1, delta=1, mu=0, nu=10) } \arguments{ \item{beta}{ numeric value, the skewness parameter in the range \code{(0, alpha)}. } \item{delta}{ numeric value, the scale parameter, must be zero or positive. } \item{mu}{ numeric value, the location parameter, by default 0. } \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{order}{ an integer value, the order of the moment. } \item{type}{ a character string, \code{"raw"} returns the moments about zero, \code{"central"} returns the central moments about the mean, and \code{"mu"} returns the moments about the location parameter \code{mu}. } } \value{ a named numerical value. The name is one of \code{mean}, \code{var}, \code{skew}, or \code{kurt}, obtained by dropping the \code{nig} prefix from the name of the corresponding function and lowercasing it. for \code{ghtMoments}, the name is obtained by \code{paste0("m", order, type)}. } \references{ Scott, D.J., Wuertz, D. and Tran, T.T. (2008) \emph{Moments of the Generalized Hyperbolic Distribution}. Preprint. } \author{ Diethelm Wuertz } \examples{ ## ghtMean - ghtMean(beta=0.2, delta=1.2, mu=-0.5, nu=4) ## ghtKurt - ghtKurt(beta=0.2, delta=1.2, mu=-0.5, nu=4) ## ghtMoments - ghtMoments(4, beta=0.2, delta=1.2, mu=-0.5, nu=4) ghtMoments(4, "central", beta=0.2, delta=1.2, mu=-0.5, nu=4) } \keyword{distribution} fBasics/man/dist-hypRobMoments.Rd0000644000176200001440000000300414650724110016432 0ustar liggesusers\name{hypRobMoments} \alias{hypRobMoments} \alias{hypMED} \alias{hypIQR} \alias{hypSKEW} \alias{hypKURT} \concept{hyperbolic distribution} \title{Robust moments for the HYP} \description{ Computes the first four robust moments for the hyperbolic distribution. } \usage{ hypMED(alpha = 1, beta = 0, delta = 1, mu = 0) hypIQR(alpha = 1, beta = 0, delta = 1, mu = 0) hypSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) hypKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha}{ shape parameter, a positive number. \code{alpha} can also be a vector of length four, containing \code{alpha}, \code{beta}, \code{delta} and \code{mu} (in that order). } \item{beta}{ skewness parameter, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{hyp} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz } \examples{ ## hypMED - # Median: hypMED(alpha = 1, beta = 0, delta = 1, mu = 0) ## hypIQR - # Inter-quartile Range: hypIQR(alpha = 1, beta = 0, delta = 1, mu = 0) ## hypSKEW - # Robust Skewness: hypSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) ## hypKURT - # Robust Kurtosis: hypKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \keyword{distribution} fBasics/man/matrix-norm.Rd0000644000176200001440000000316414660350235015153 0ustar liggesusers\name{norm} \alias{norm2} \title{Matrix norm} \description{ Computes the norm of a matrix. } \usage{ norm2(x, p = 2) } \arguments{ \item{x}{ a numeric matrix. } \item{p}{ an integer value, \code{1}, \code{2} or \code{Inf}, see section \sQuote{Details}. } } \details{ The function \code{norm2} computes the norm of a matrix. Three choices are possible: \describe{ \item{\code{p=1}}{ The maximum absolute column sum norm which is defined as the maximum of the sum of the absolute valued elements of columns of the matrix. } \item{\code{p=2}}{ The spectral norm is "the norm" of a matrix \code{X}. This value is computed as the square root of the maximum eigenvalue of \code{CX}, where \code{C} is the conjugate transpose. } \item{\code{p=Inf}}{ The maximum absolute row sum norm is defined as the maximum of the sum of the absolute valued elements of rows of the matrix. } } } \note{ Since \code{base::norm()} has become available in the R base environment, the function \code{fBasics::norm()} has become obsolete. To avoid conflicts with \code{norm()} we have renamed the fBasics' one to \code{norm2}. } \value{ the requested norm of the matrix, a non-negative number } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P <- pascal(5) P ## Return the Norm of the Matrix: norm2(P) } \keyword{math} fBasics/man/plot-acfPlot.Rd0000644000176200001440000001042114660350525015236 0ustar liggesusers\name{acfPlot} \alias{acfPlot} \alias{pacfPlot} \alias{teffectPlot} \alias{lacfPlot} \title{Autocorrelation function plots} \description{ Returns plots of autocorrelations including the autocorrelation function ACF, the partial ACF, the lagged ACF, and the Taylor effect plot. \cr The functions to display stylized facts are: \tabular{ll}{ \code{acfPlot} \tab autocorrelation function plot, \cr \code{pacfPlot} \tab partial autocorrelation function plot, \cr \code{lacfPlot} \tab lagged autocorrelation function plot, \cr \code{teffectPlot} \tab Taylor effect plot.} } \usage{ acfPlot(x, labels = TRUE, \dots) pacfPlot(x, labels = TRUE, \dots) lacfPlot(x, n = 12, lag.max = 20, type = c("returns", "values"), labels = TRUE, \dots) teffectPlot(x, deltas = seq(from = 0.2, to = 3, by = 0.2), lag.max = 10, ymax = NA, standardize = TRUE, labels = TRUE, \dots) } \arguments{ \item{x}{ an uni- or multivariate return series of class \code{timeSeries} or any other object which can be transformed by the function \code{as.timeSeries()} into an object of class \code{timeSeries}. } \item{labels}{ a logical value. Whether or not x- and y-axes should be automatically labeled and a default main title should be added to the plot. By default \code{TRUE}. } \item{n}{ an integer value, the number of lags. } \item{lag.max}{ maximum lag for which the autocorrelation should be calculated, an integer. } \item{type}{ a character string which specifies the type of the input series, either "returns" or series "values". In the case of a return series as input, the required value series is computed by cumulating the financial returns: \code{exp(colCumsums(x))} } \item{deltas}{ the exponents, a numeric vector, by default ranging from 0.2 to 3.0 in steps of 0.2. } \item{ymax}{ maximum y-axis value on plot. If \code{NA}, then the value is selected automatically. } \item{standardize}{ a logical value. Should the vector \code{x} be standardized? } \item{\dots}{ arguments to be passed. } } \details{ \bold{Autocorrelation Functions:} \cr\cr The functions \code{acfPlot} and \code{pacfPlot}, plot and estimate autocorrelation and partial autocorrelation function. The functions allow to get a first view on correlations within the time series. The functions are synonym function calls for R's \code{acf} and \code{pacf} from the the \code{ts} package. \cr \bold{Taylor Effect:} \cr\cr The "Taylor Effect" describes the fact that absolute returns of speculative assets have significant serial correlation over long lags. Even more, autocorrelations of absolute returns are typically greater than those of squared returns. From these observations the Taylor effect states, that that the autocorrelations of absolute returns to the the power of \code{delta}, \code{abs(x-mean(x))^delta} reach their maximum at \code{delta=1}. The function \code{teffect} explores this behaviour. A plot is created which shows for each lag (from 1 to \code{max.lag}) the autocorrelations as a function of the exponent \code{delta}. In the case that the above formulated hypothesis is supported, all the curves should peak at the same value around \code{delta=1}. } \value{ for \code{acfPlot} and \code{pacfplot}, an object of class \code{"acf"}, see \code{\link{acf}}; for \code{teffectPlot}, a numeric matrix of order \code{deltas} by \code{max.lag} with the values of the autocorrelations; for \code{lacfPlot}, a list with the following two elements: \item{Rho}{the autocorrelation function,} \item{lagged}{the lagged correlations.} } \references{ Taylor S.J. (1986); \emph{Modeling Financial Time Series}, John Wiley and Sons, Chichester. Ding Z., Granger C.W.J., Engle R.F. (1993); \emph{A long memory property of stock market returns and a new model}, Journal of Empirical Finance 1, 83. } \examples{ ## data data(LPP2005REC, package = "timeSeries") SPI <- LPP2005REC[, "SPI"] plot(SPI, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") ## Taylor Effect: teffectPlot(SPI) } \keyword{hplot} fBasics/man/dist-normRobMoments.Rd0000644000176200001440000000175514650724110016620 0ustar liggesusers\name{normRobMoments} \alias{normRobMoments} \alias{normMED} \alias{normIQR} \alias{normSKEW} \alias{normKURT} \title{Robust moments for the Normal distribution} \description{ Computes the first four robust moments for the Normal distribution. } \usage{ normMED(mean = 0, sd = 1) normIQR(mean = 0, sd = 1) normSKEW(mean = 0, sd = 1) normKURT(mean = 0, sd = 1) } \arguments{ \item{mean}{ locaiton parameter. } \item{sd}{ scale parameter. } } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{gh} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz } \examples{ ## normMED - # Median: normMED(mean = 0, sd = 1) ## normIQR - # Inter-quartile Range: normIQR(mean = 0, sd = 1) ## normSKEW - # Robust Skewness: normSKEW(mean = 0, sd = 1) ## normKURT - # Robust Kurtosis: normKURT(mean = 0, sd = 1) } \keyword{distribution} fBasics/man/stats-rowStats.Rd0000644000176200001440000000376114657044436015674 0ustar liggesusers\name{rowStats} \alias{rowStats} % \alias{rowSums} % \alias{rowMeans} \alias{rowSds} \alias{rowVars} \alias{rowSkewness} \alias{rowKurtosis} \alias{rowMaxs} \alias{rowMins} \alias{rowProds} \alias{rowQuantiles} \alias{rowAvgs} \alias{rowStdevs} \title{Row statistics} \description{ Functions to compute row statistical properties of financial and economic time series data. \cr The functions are: \tabular{ll}{ \code{rowStats}\tab calculates row statistics, \cr % \code{rowSums} \tab calculates row sums, \cr % \code{rowMeans} \tab calculates row means, \cr \code{rowSds} \tab calculates row standard deviations, \cr \code{rowVars} \tab calculates row variances, \cr \code{rowSkewness} \tab calculates row skewness, \cr \code{rowKurtosis} \tab calculates row kurtosis, \cr \code{rowMaxs} \tab calculates maximum values in each row, \cr \code{rowMins} \tab calculates minimum values in each row, \cr \code{rowProds} \tab computes product of all values in each row, \cr \code{rowQuantiles} \tab computes quantiles of each row. } } \usage{ rowStats(x, FUN, \dots) % rowSums(x, \dots) % rowMeans(x, \dots) rowSds(x, \dots) rowVars(x, \dots) rowSkewness(x, \dots) rowKurtosis(x, \dots) rowMaxs(x, \dots) rowMins(x, \dots) rowProds(x, \dots) rowQuantiles(x, prob = 0.05, \dots) rowStdevs(x, \dots) rowAvgs(x, \dots) } \arguments{ \item{FUN}{ a function name, the statistical function to be applied. } \item{prob}{ a numeric value, the probability with value in [0,1]. } \item{x}{ a rectangular object which can be transformed into a matrix by the function \code{as.matrix}. } \item{\dots}{ arguments to be passed. } } \value{ each function returns a numeric vector of the statistics } \seealso{ \code{\link{apply}} } \examples{ ## Simulated Return Data in Matrix Form: x <- matrix(rnorm(10*10), nrow = 10) rowStats(x, FUN = mean) rowMaxs(x) } \keyword{univar} fBasics/man/stats-basicStats.Rd0000644000176200001440000000416414660432117016133 0ustar liggesusers\name{BasicStatistics} \alias{basicStats} \title{Basic time series statistics} \description{ Computes basic financial time series statistics. } \usage{ basicStats(x, ci = 0.95) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other object which can be transformed by the function \code{as.timeSeries} into an object of class \code{"timeSeries"}. The latter case, other than \code{"timeSeries"} objects, is more or less untested. } \item{ci}{ confidence interval, a numeric value, by default 0.95, i.e. 95\%. } } \details{ Computes a number of sample statistics for each column of \code{x}. The statistics should be clear from the row names of the returned data frame. \code{"LCL"} and \code{"UCL"} stand for lower/upper confidence limits, computed under the null hypothesis of i.i.d. \code{"Kurtosis"} represents the \emph{excess kurtosis}, so its theoretical value for the normal distribution is zero, not 3. These statistics are often computed as a first step in the study of returns on financial assets. In that case any inference on these statistics (including the confidence intervals for the mean) should be considered exploratory, since returns are virtually never i.i.d. } \value{ a data frame with one column for each column of \code{x} and the following rows: \item{"nobs"}{number of observations,} \item{"NAs"}{number of \code{NA}s} \item{"Minimum"}{minimum,} \item{"Maximum "}{maximum,} \item{"1. Quartile"}{lower quartile,} \item{"3. Quartile"}{upper quartile,} \item{"Mean"}{mean, } \item{"Median"}{median,} \item{"Sum"}{sum of the values,} \item{"SE Mean"}{standard error of the mean,} \item{"LCL Mean"}{lower limit of the CI for the mean,} \item{"UCL Mean"}{upper limit of the CI for the mean,} \item{"Variance"}{variance, } \item{"Stdev"}{standard deviation,} \item{"Skewness"}{skewness coefficient,} \item{"Kurtosis"}{excess kurtosis.} } \examples{ \dontshow{set.seed(1234)} ## Simulated Monthly Return Data tS <- timeSeries(matrix(rnorm(12)), timeDate::timeCalendar()) basicStats(tS) } \keyword{stats} fBasics/man/dist-distCheck.Rd0000644000176200001440000000174614323567450015551 0ustar liggesusers\name{distCheck} \alias{distCheck} \title{Distribution check} \description{ Tests properties of an \R implementation of a distribution, i.e., of all four of its \dQuote{dpqr} functions. } \usage{ distCheck(fun = "norm", n = 1000, robust = TRUE, subdivisions = 100, \dots) } \arguments{ \item{fun}{ a character string, the name of the distribution. } \item{n}{ an integer specifying the number of random variates to be generated. } \item{robust}{ logical flag, should robust estimates be used? By default \code{TRUE}. } \item{subdivisions}{ integer specifying the numbers of subdivisions in integration. } \item{\dots}{ the distributional parameters. } } \examples{ distCheck("norm", mean = 1, sd = 1) distCheck("lnorm", meanlog = 0.5, sdlog = 2, robust=FALSE) ## here, true E(X) = exp(mu + 1/2 sigma^2) = exp(.5 + 2) = exp(2.5) = 12.182 ## and Var(X) = exp(2*mu + sigma^2)*(exp(sigma^2) - 1) = 7954.67 } \keyword{distribution} fBasics/man/stats-interpKrige.Rd0000644000176200001440000000363414650724110016313 0ustar liggesusers\name{krigeInterp} \alias{krigeInterp} \concept{interpolation} \concept{bivariate interpolation} \concept{Bivariate Krige Interpolation} \title{Bivariate Krige Interpolation} \description{ Bivariate Krige Interpolation. } \usage{ krigeInterp(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE, polDegree = 6) } \arguments{ \item{x, y, z}{ the arguments \code{x} and \code{y} are two numeric vectors of grid pounts, and \code{z} is a numeric matrix or any other rectangular object which can be transformed by the function \code{as.matrix} into a matrix object. } \item{gridPoints}{ an integer value specifying the number of grid points in \code{x} and \code{y} direction. } \item{xo, yo}{ two numeric vectors of data points spanning the grid. } \item{extrap}{ a logical, if \code{TRUE} then the data points are extrapolated. } \item{polDegree}{ the polynomial krige degree, an integer ranging between 1 and 6. } } \note{ \code{krigeInterp()} requires package \pkg{spatial}. } \value{ a list with at least three entries, \code{x}, \code{y} and \code{z}. The returned values can be used directly in \code{\link{persp}} and \code{\link{contour}} 3D plotting methods. } \seealso{ \code{\link{akimaInterp}}, \code{\link{linearInterp}}. } \examples{\donttest{ ## The akima library is not auto-installed because of a different licence. ## krigeInterp - Kriging: set.seed(1953) x = runif(999) - 0.5 y = runif(999) - 0.5 z = cos(2*pi*(x^2+y^2)) ans = krigeInterp(x, y, z, extrap = FALSE) persp(ans, theta = -40, phi = 30, col = "steelblue", xlab = "x", ylab = "y", zlab = "z") contour(ans) } } \keyword{math} fBasics/man/stats-interpAkima.Rd0000644000176200001440000000776614650724110016306 0ustar liggesusers\name{akimaInterp} \alias{akimaInterp} \alias{akimaInterpp} \concept{interpolation} \concept{bivariate interpolation} \concept{bivariate spline interpolation} \concept{Akima method} \title{Bivariate Spline Interpolation} \description{ Interpolates bivariate data sets using Akima spline interpolation. } \usage{ akimaInterp(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints), extrap = FALSE) akimaInterpp(x, y = NULL, z = NULL, xo, yo, extrap = FALSE) } \arguments{ \item{x, y, z}{ for \code{akimaInterp} the arguments \code{x} and \code{y} are two numeric vectors of grid pounts, and \code{z} is a numeric matrix or any other rectangular object which can be transformed by the function \code{as.matrix} into a matrix object. For \code{akimaInterpp} we consider either three numeric vectors of equal length or if \code{y} and \code{z} are NULL, a list with entries \code{x}, \code{y}, \code{z}, or named data frame with \code{x} in the first, \code{y} in the second, and \code{z} in the third column. } \item{gridPoints}{ an integer value specifying the number of grid points in \code{x} and \code{y} direction. } \item{xo, yo}{ for \code{akimaInterp} two numeric vectors of data points spanning the grid, and for \code{akimaInterpp} two numeric vectors of data points building pairs for pointwise interpolation. } \item{extrap}{ a logical, if \code{TRUE} then the data points are extrapolated. } } \details{ Two options are available: gridded and pointwise interpolation. \code{akimaInterp} is a wrapper to \code{interp} provided by the contributed R package \code{akima}. The Fortran code of the Akima spline interpolation routine was written by H. Akima. Linear surface fitting and krige surface fitting are provided by the functions \code{\link{linearInterp}} and \code{\link{krigeInterp}}. } \note{ Package \code{akima} is no longer needed. Equivalent functions from package \code{interp} are now called instead. } \value{ \describe{ \item{akimaInterp}{ returns a list with at least three entries, \code{x}, \code{y} and \code{z}. Note, that the returned values, can be directly used by the \code{persp} and \code{contour} 3D plotting methods. } \item{akimaInterpp}{ returns a data.frame with columns \code{"x"}, \code{"y"}, and \code{"z"}. } } } \seealso{ \code{\link{linearInterp}}, \code{\link{krigeInterp}}. } \references{ Akima H., 1978, \emph{A Method of Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points}, ACM Transactions on Mathematical Software 4, 149-164. Akima H., 1996, \emph{Algorithm 761: Scattered-Data Surface Fitting that has the Accuracy of a Cubic Polynomial}, ACM Transactions on Mathematical Software 22, 362-371. } \examples{\donttest{ ## Does not run for r-solaris-x86 ## akimaInterp -- Akima Interpolation: if (requireNamespace("interp")) { set.seed(1953) x <- runif(999) - 0.5 y <- runif(999) - 0.5 z <- cos(2*pi*(x^2+y^2)) ans <- akimaInterp(x, y, z, gridPoints = 41, extrap = FALSE) persp(ans, theta = -40, phi = 30, col = "steelblue", xlab = "x", ylab = "y", zlab = "z") contour(ans) } ## Use spatial as alternative on r-solaris-x86 ## spatialInterp - Generate Kriged Grid Data: if (requireNamespace("spatial")) { RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x <- runif(999)-0.5 y <- runif(999)-0.5 z <- cos(2*pi*(x^2+y^2)) ans <- krigeInterp(x, y, z, extrap = FALSE) persp(ans) title(main = "Kriging") contour(ans) title(main = "Kriging") } } } \keyword{math} fBasics/man/matrix-inv.Rd0000644000176200001440000000107214660350156014772 0ustar liggesusers\name{inv} \alias{inv} \title{The inverse of a matrix} \description{ Computes the inverse of a matrix. } \usage{ inv(x) } \arguments{ \item{x}{ a numeric matrix. } } \value{ a matrix } \references{ Golub, van Loan, (1996); \emph{Matrix Computations}, 3rd edition. Johns Hopkins University Press. } \examples{ ## Create Pascal Matrix: P = pascal(5) P ## Compute the Inverse Matrix: inv(P) ## Check: inv(P) \%*\% P ## Alternatives: chol2inv(chol(P)) solve(P) } \keyword{math} fBasics/man/test-normalityTests.Rd0000644000176200001440000002202014660346737016720 0ustar liggesusers\name{NormalityTests} \alias{NormalityTests} \alias{normalTest} \alias{ksnormTest} \alias{shapiroTest} \alias{jarqueberaTest} \alias{dagoTest} \alias{jbTest} % from nortest: \alias{adTest} \alias{cvmTest} \alias{lillieTest} \alias{pchiTest} \alias{sfTest} \concept{normality test} \concept{test for normality} \concept{Kolmogorov-Smirnov test for normality} \concept{Shapiro-Wilk's test for normality} \concept{Jarque-Bera test for normality} \concept{D'Agostino test for normality} \concept{finite sample adjusted JB test} \concept{finite sample adjusted LM test} \concept{finite sample adjusted ALM test} \concept{Anderson-Darling test for normality} \concept{Cramer-von Mises test for normality} \concept{Lilliefors test for normality} \concept{Pearson chi-square test for normality} \concept{Shapiro-Francia test for normality} \title{Tests for normality} \description{ A collection of functions of one sample tests for testing normality of financial return series. \cr The functions for testing normality are: \tabular{ll}{ \code{ksnormTest} \tab Kolmogorov-Smirnov normality test, \cr \code{shapiroTest} \tab Shapiro-Wilk's test for normality, \cr \code{jarqueberaTest} \tab Jarque-Bera test for normality, \cr \code{dagoTest} \tab D'Agostino normality test. } Functions for high precision Jarque Bera LM and ALM tests: \tabular{ll}{ \code{jbTest} \tab Performs finite sample adjusted JB, LM and ALM test. } Additional functions for testing normality from the 'nortest' package: \tabular{ll}{ \code{adTest} \tab Anderson--Darling normality test, \cr \code{cvmTest} \tab Cramer--von Mises normality test, \cr \code{lillieTest} \tab Lilliefors (Kolmogorov-Smirnov) normality test, \cr \code{pchiTest} \tab Pearson chi-square normality test, \cr \code{sfTest} \tab Shapiro-Francia normality test. } For SPlus/Finmetrics Compatibility: \tabular{ll}{ \code{normalTest} \tab test suite for some normality tests. } } \usage{ ksnormTest(x, title = NULL, description = NULL) jbTest(x, title = NULL, description = NULL) shapiroTest(x, title = NULL, description = NULL) normalTest(x, method = c("sw", "jb"), na.rm = FALSE) jarqueberaTest(x, title = NULL, description = NULL) dagoTest(x, title = NULL, description = NULL) adTest(x, title = NULL, description = NULL) cvmTest(x, title = NULL, description = NULL) lillieTest(x, title = NULL, description = NULL) pchiTest(x, title = NULL, description = NULL) sfTest(x, title = NULL, description = NULL) } \arguments{ \item{x}{ a numeric vector of data values or an S4 object of class \code{"timeSeries"}. } \item{title}{ an optional character string, if not specified the inputs data name is deparsed. } \item{description}{ optional description string, or a vector of character strings. } \item{method}{ for \code{normalTest} only, indicates one of four different methods for the normality test, one of \code{"ks"} (Kolmogorov-Smirnov one-sample test, the the default), \code{"sw"} (Shapiro-Wilk test), \code{"jb"} (Jarque-Bera Test), and \code{"da"} (D'Agostino Test). } \item{na.rm}{ for \code{normalTest} only, a logical value. Should missing values removed before computing the tests? The default value is \code{FALSE}. } } \details{ The hypothesis tests may be of interest for many financial and economic applications, especially for the investigation of univariate time series returns. Several tests for testing if the records from a data set are normally distributed are available. The input to all these functions may be just a vector \code{x} or a univariate time series object \code{x} of class \code{timeSeries}. First, there exists a wrapper function which allows to call one from two normal tests either the Shapiro--Wilks test or the Jarque--Bera test. This wrapper was introduced for compatibility with S-Plus' FinMetrics package. Also available are the Kolmogorov--Smirnov one sample test and the D'Agostino normality test. The remaining five normal tests are the Anderson--Darling test, the Cramer--von Mises test, the Lilliefors (Kolmogorov--Smirnov) test, the Pearson chi--square test, and the Shapiro--Francia test. They are calling functions from R's contributed package \code{nortest}. The difference to the original test functions implemented in R and from contributed R packages is that the Rmetrics functions accept time series objects as input and give a more detailed output report. The Anderson-Darling test is used to test if a sample of data came from a population with a specific distribution, here the normal distribution. The \code{adTest} goodness-of-fit test can be considered as a modification of the Kolmogorov--Smirnov test which gives more weight to the tails than does the \code{ksnormTest}. Note that \code{jarqueBeraTest} computes the asymptotic statistic and p-value, while \code{jbTesT} gives final sample approximations. } \value{ an object from class \code{\link{fHTEST}}. Slot \code{test} is a list containing the following (optionally empty) elements (in addition to those described in \code{\link{fHTEST}}): \describe{ \item{ksnormTest}{ the 'D' statistic and p-values for the three alternatives 'two-sided, 'less' and 'greater'. } \item{shapiroTest}{ the 'W' statistic and the p-value. } \item{jarqueberaTest}{ no additional elements. } \item{jbTest}{ the 'Chi-squared' statistic with 2 degrees of freedom and the asymptotic p-value. \code{jbTest} is the finite sample version of the Jarque Bera Lagrange multiplier, LM, and adjusted Lagrange multiplier test, ALM. } \item{dagoTest}{ the 'Chi-squared', the 'Z3' (Skewness) and 'Z4' (Kurtosis) statistic together with the corresponding p values. } \item{adTest}{ the 'A' statistic and the p-value. } \item{cvmTest}{ the 'W' statistic and the p-value. } \item{lillieTest}{ the 'D' statistic and the p-value. } \item{pchiTest}{ the value for the 'P' statistic and the p-values for the adjusted and not adjusted test cases. In addition the number of classes is printed, taking the default value due to Moore (1986) computed from the expression \code{n.classes = ceiling(2 * (n^(2/5)))}, where \code{n} is the number of observations. } \item{sfTest}{ the 'W' statistic and the p-value. } } } \note{ Some of the test implementations are selected from R's \code{ctest} and \code{nortest} packages. } \references{ Anderson T.W., Darling D.A. (1954); \emph{A Test of Goodness of Fit}, JASA 49:765--69. Conover, W. J. (1971); \emph{Practical nonparametric statistics}, New York: John Wiley & Sons. D'Agostino R.B., Pearson E.S. (1973); \emph{Tests for Departure from Normality}, Biometrika 60, 613--22. D'Agostino R.B., Rosman B. (1974); \emph{The Power of Geary's Test of Normality}, Biometrika 61, 181--84. Durbin J. (1961); \emph{Some Methods of Constructing Exact Tests}, Biometrika 48, 41--55. Durbin,J. (1973); \emph{Distribution Theory Based on the Sample Distribution Function}, SIAM, Philadelphia. Geary R.C. (1947); \emph{Testing for Normality}; Biometrika 36, 68--97. Lehmann E.L. (1986); \emph{Testing Statistical Hypotheses}, John Wiley and Sons, New York. Linnet K. (1988); \emph{Testing Normality of Transformed Data}, Applied Statistics 32, 180--186. Moore, D.S. (1986); \emph{Tests of the chi-squared type}, In: D'Agostino, R.B. and Stephens, M.A., eds., Goodness-of-Fit Techniques, Marcel Dekker, New York. Shapiro S.S., Francia R.S. (1972); \emph{An Approximate Analysis of Variance Test for Normality}, JASA 67, 215--216. Shapiro S.S., Wilk M.B., Chen V. (1968); \emph{A Comparative Study of Various Tests for Normality}, JASA 63, 1343--72. Thode H.C. (2002); \emph{Testing for Normality}, Marcel Dekker, New York. Weiss M.S. (1978); \emph{Modification of the Kolmogorov-Smirnov Statistic for Use with Correlated Data}, JASA 73, 872--75. Wuertz D., Katzgraber H.G. (2005); \emph{Precise finite-sample quantiles of the Jarque-Bera adjusted Lagrange multiplier test}, ETHZ Preprint. } \author{ R-core team for the tests from \R's ctest package,\cr Adrian Trapletti for the runs test from \R's tseries package,\cr Juergen Gross for the normal tests from \R's nortest package,\cr James Filliben for the Fortran program producing the runs report,\cr Diethelm Wuertz and Helmut Katzgraber for the finite sample JB tests, \cr Diethelm Wuertz for the Rmetrics \R-port. \cr Earlier versions of theses functions were based on Fortran code of Paul Johnson. } \examples{ \dontshow{set.seed(1234)} x <- rnorm(100) ## Kolmogorov-Smirnov one-sampe test ksnormTest(x) ## Shapiro-Wilk test shapiroTest(x) ## Jarque-Bera Test jarqueberaTest(x) jbTest(x) } \keyword{htest} fBasics/man/utils-decor.Rd0000644000176200001440000000241714660434773015142 0ustar liggesusers\name{decor} \alias{decor} \alias{hgrid} \alias{vgrid} \alias{boxL} \alias{box_} \alias{copyright} \title{Functions for decorating plots} \description{ Functions for decorating plots. } \usage{ decor() hgrid(ny = NULL, ...) vgrid(nx = NULL, ...) boxL(col = "white") box_(col = c("white", "black")) copyright() } \arguments{ \item{col}{ the color of the background, \code{"black"} and foreground \code{"white"} lines of the box. } \item{nx, ny}{ number of cells of the grid in \code{x} or \code{y} direction. When \code{NULL}, as per default, the grid aligns with the tick marks on the corresponding default axis (i.e., tick marks as computed by axTicks). } \item{\dots}{ additional arguments passed to the \code{grid()} function. } } \details{ \code{decor} is equivalent to \code{hgrid()} followed by \code{boxL()}. \code{hgrid} creates horizontal grid lines. \code{vgrid} creates vertical grid lines. \code{boxL} creates an L-shaped box \code{box_} creates a bottom line box. \code{copyright} adds Rmetrics copyright to a plot. } \examples{ plot(x = rnorm(100), type = "l", col = "red", xlab = "", ylab = "Variates", las = 1) title("Normal Deviates", adj = 0) hgrid() boxL() copyright() } \keyword{hplot} fBasics/man/dist-ghSlider.Rd0000644000176200001440000000102114650724110015362 0ustar liggesusers\name{ghSlider} \alias{ghSlider} \title{Generalized Hyperbolic Distribution Slider} \description{ Displays interactively the dependence of the generalized hyperbolic distribution on its parameters. } \usage{ ghSlider() } \value{ a tcl/tk based graphical user interface. This is a nice display for educational purposes to investigate the densities and probabilities of the generalized hyperbolic distribution. } \examples{ ## ghSlider - # ghSlider() } \keyword{distribution} fBasics/man/utils-colorPalette.Rd0000644000176200001440000002133314660434676016503 0ustar liggesusers\name{colorPalette} \alias{colorPalette} \alias{rainbowPalette} \alias{heatPalette} \alias{terrainPalette} \alias{topoPalette} \alias{cmPalette} \alias{greyPalette} \alias{timPalette} \alias{rampPalette} \alias{seqPalette} \alias{divPalette} \alias{qualiPalette} \alias{focusPalette} \alias{monoPalette} \title{Color palettes} \description{ Functions to create color palettes. \cr The functions are: \tabular{ll}{ \code{rainbowPalette} \tab Contiguous rainbow color palette, \cr \code{heatPalette} \tab Contiguous heat color palette, \cr \code{terrainPalette} \tab Contiguous terrain color palette, \cr \code{topoPalette} \tab Contiguous topo color palette, \cr \code{cmPalette} \tab Contiguous cm color palette, \cr \code{greyPalette} \tab R's gamma-corrected gray palette, \cr \code{timPalette} \tab Tim's Matlab like color palette, \cr \code{rampPalette} \tab Color ramp palettes, \cr \code{seqPalette} \tab Sequential color brewer palettes, \cr \code{divPalette} \tab Diverging color brewer palettes, \cr \code{qualiPalette} \tab Qualified color brewer palettes, \cr \code{focusPalette} \tab Red, green blue focus palettes, \cr \code{monoPalette} \tab Red, green blue mono palettes. } } \usage{ rainbowPalette(n = 64, \dots) heatPalette(n = 64, \dots) terrainPalette(n = 64, \dots) topoPalette(n = 64, \dots) cmPalette(n = 64, \dots) greyPalette(n = 64, \dots) timPalette(n = 64) rampPalette(n, name = c("blue2red", "green2red", "blue2green", "purple2green", "blue2yellow", "cyan2magenta")) seqPalette(n, name = c( "Blues", "BuGn", "BuPu", "GnBu", "Greens", "Greys", "Oranges", "OrRd", "PuBu", "PuBuGn", "PuRd", "Purples", "RdPu", "Reds", "YlGn", "YlGnBu", "YlOrBr", "YlOrRd")) divPalette(n, name = c( "BrBG", "PiYG", "PRGn", "PuOr", "RdBu", "RdGy", "RdYlBu", "RdYlGn", "Spectral")) qualiPalette(n, name = c( "Accent", "Dark2", "Paired", "Pastel1", "Pastel2", "Set1", "Set2", "Set3")) focusPalette(n, name = c("redfocus", "greenfocus", "bluefocus")) monoPalette(n, name = c("redmono", "greenmono", "bluemono")) } \arguments{ \item{n}{ an integer, giving the number of greys or colors to be constructed. } \item{name}{ a character string, the name of the color set. } \item{\dots}{ arguments to be passed, see the details section } } \details{ All Rmetrics' color sets are named as \code{fooPalette}, where the prefix \code{foo} denotes the name of the underlying color set. \subsection{R's Contiguous Color Palettes:}{ Palettes for \code{n} contiguous colors are implemented in the \code{grDevices} package. To conform with Rmetrics' naming convention for color palettes we have build wrappers around the underlying functions. These are the \code{rainbowPalette}, \code{heatPalette}, \code{terrainPalette}, \code{topoPalette}, and the \code{cmPalette}. Conceptually, all of these functions actually use (parts of) a line cut out of the 3-dimensional color space, parametrized by the function \code{hsv(h,s,v,gamma)}, where \code{gamma=1} for the \code{fooPalette} function, and hence, equispaced hues in RGB space tend to cluster at the red, green and blue primaries. Some applications, such as contouring, require a palette of colors which do not wrap around to give a final color close to the starting one. To pass additional arguments to the underlying functions see \code{help(rainbow)}. With rainbow, the parameters \code{start} and \code{end} can be used to specify particular subranges of hues. Synonym function calls are \code{rainbow}, \code{heat.colors}, \code{terrain.colors}, \code{topo.colors}, and \code{cm.colors}. } \subsection{R's Gamma-Corrected Gray Palette:}{ \code{grayPalette} chooses a series of \code{n} gamma-corrected gray levels. The range of the gray levels can be optionally monitored through the \code{\dots} arguments, for details see \code{help(gray.colors)}, which is a synonym function call in the \code{grDevices} package. } \subsection{Tim's Matlab like Color Palette:}{ \code{timPalette} creates a color set ranging from blue to red, and passes through the colors cyan, yellow, and orange. It comes from the Matlab software, originally used in fluid dynamics simulations. The function here is a copy from R's contributed package \code{fields} doing a spline interpolation on \code{n=64} color points. } \subsection{Color Ramp Palettes:}{ \code{rampPalette} creates several color ramps. The function is implemented from Tim Keitt's contributed R package \code{colorRamps}. Supported through the argument \code{name} are the following color ramps: \code{"blue2red"}, \code{"green2red"}, \code{"blue2green"}, \code{"purple2green"}, \code{"blue2yellow"}, \code{"cyan2magenta"}. } \subsection{Color Brewer Palettes:}{ The functions \code{seqPalette}, \code{divPalette}, and \code{qualiPalette} create color sets according to R's contributed \code{RColorBrewer} package. The first letter in the function name denotes the type of the color set: "s" for sequential palettes, "d" for diverging palettes, and "q" for qualitative palettes. \emph{Sequential palettes} are suited to ordered data that progress from low to high. Lightness steps dominate the look of these schemes, with light colors for low data values to dark colors for high data values. The sequential palettes names are: Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd. \emph{Diverging palettes} put equal emphasis on mid-range critical values and extremes at both ends of the data range. The critical class or break in the middle of the legend is emphasized with light colors and low and high extremes are emphasized with dark colors that have contrasting hues. The diverging palettes names are: BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral. \emph{Qualitative palettes} do not imply magnitude differences between legend classes, and hues are used to create the primary visual differences between classes. Qualitative schemes are best suited to representing nominal or categorical data. The qualitative palettes names are: Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3. In contrast to the original color brewer palettes, the palettes here are created by spline interpolation from the color variation with the most different values, i.e for the sequential palettes these are 9 values, for the diverging palettes these are 11 values, and for the qualitative palettes these are between 8 and 12 values dependeing on the color set. } \subsection{Graph Color Palettes}{ The function \code{perfanPalette} creates color sets inspired by R's contributed package \code{Performance Analytics}. These color palettes have been designed to create readable, comparable line and bar graphs with specific objectives. \describe{ \item{Focused Color Palettes}{ Color sets designed to provide focus to the data graphed as the first element. This palette is best used when there is clearly an important data set for the viewer to focus on, with the remaining data being secondary, tertiary, etc. Later elements graphed in diminishing values of gray. } \item{Monochrome Color Palettes}{ These include color sets for monochrome color displays. } } } } \value{ a character string of color strings } \note{ The palettes are wrapper functions provided in several contributed R packages. These include: Cynthia Brewer and Mark Harrower for the brewer palettes, \cr Peter Carl and Brian G. Peterson for the "PerformanceAnalytics" package, \cr Tim Keitt for the "colorRamps" package, \cr Ross Ihaka for the "colorspace" package, \cr Tomas Aragon for the "epitools" package, \cr Doug Nychka for the "fields" package, \cr Erich Neuwirth for the "RColorBrewer" package. \cr Additional undocumented hidden functions: \tabular{ll}{ % RGB CONVERTER: \code{.asRGB} \tab Converts any R color to RGB (red/green/blue), \cr % CONVERSION HEXIMAL/DECIMAL: \code{.chcode} \tab Changes from one to another number system, \cr \code{.hex.to.dec} \tab Converts heximal numbers do decimal numbers, \cr \code{.dec.to.hex} \tab Converts decimal numbers do heximal numbers.} } \examples{ greyPalette() } \keyword{programming} fBasics/man/dist-nig.Rd0000644000176200001440000000602114650724110014403 0ustar liggesusers\name{nig} \alias{nig} \alias{dnig} \alias{pnig} \alias{qnig} \alias{rnig} \concept{normal inverse Gaussian distribution} \title{Normal Inverse Gaussian Distribution} \description{ Density, distribution function, quantile function and random generation for the normal inverse Gaussian distribution. } \usage{ dnig(x, alpha = 1, beta = 0, delta = 1, mu = 0, log = FALSE) pnig(q, alpha = 1, beta = 0, delta = 1, mu = 0) qnig(p, alpha = 1, beta = 0, delta = 1, mu = 0) rnig(n, alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{x,q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{alpha}{ shape parameter. } \item{beta}{ skewness parameter \code{beta}, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{log}{ a logical flag by default \code{FALSE}. Should labels and a main title be drawn to the plot? } } \details{ \code{dnig} gives the density. \code{pnig} gives the distribution function. \code{qnig} gives the quantile function, and \code{rnig} generates random deviates. The parameters \code{alpha, beta, delta, mu} are in the first parameterization of the distribution. The random deviates are calculated with the method described by Raible (2000). } \value{ numeric vector } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## nig - set.seed(1953) r = rnig(5000, alpha = 1, beta = 0.3, delta = 1) plot(r, type = "l", col = "steelblue", main = "nig: alpha=1 beta=0.3 delta=1") ## nig - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dnig(x, alpha = 1, beta = 0.3, delta = 1)) ## nig - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, pnig(x, alpha = 1, beta = 0.3, delta = 1)) ## nig - # Compute Quantiles: qnig(pnig(seq(-5, 5, 1), alpha = 1, beta = 0.3, delta = 1), alpha = 1, beta = 0.3, delta = 1) } \keyword{distribution} fBasics/man/dist-hypMode.Rd0000644000176200001440000000352514650724110015241 0ustar liggesusers\name{hypMode} \alias{hypMode} \concept{hyperbolic distribution} \concept{mode} \title{Hyperbolic mode} \description{ Computes the mode of the hyperbolic distribution. } \usage{ hypMode(alpha = 1, beta = 0, delta = 1, mu = 0, pm = 1) } \arguments{ \item{alpha}{ shape parameter, a positive number. \code{alpha} can also be a vector of length four, containing \code{alpha}, \code{beta}, \code{delta} and \code{mu} (in that order). } \item{beta}{ skewness parameter, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{pm}{ an integer value between \code{1} and \code{4} for the selection of the parameterization. The default takes the first parameterization. } } \value{ a numeric value, the mode in the appropriate parameterization for the hyperbolic distribution. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \examples{ ## hypMode - hypMode() } \keyword{distribution} fBasics/man/dist-ghRobMoments.Rd0000644000176200001440000000315314650724110016235 0ustar liggesusers\name{ghRobMoments} \alias{ghRobMoments} \alias{ghMED} \alias{ghIQR} \alias{ghSKEW} \alias{ghKURT} \title{Robust Moments for the GH} \description{ Computes the first four robust moments for the generalized hyperbolic distribution. } \usage{ ghMED(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ghIQR(alpha= 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ghSKEW(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ghKURT(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \arguments{ \item{alpha}{ first shape parameter. } \item{beta}{ second shape parameter, should in the range \code{(0, alpha).} } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{lambda}{ defines the sublclass, by default \eqn{-1/2}. } } \details{ The meanings of the parameters correspond to the first parameterization, see \code{\link{gh}} for further details. } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{gh} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz. } \examples{ ## ghMED - # Median: ghMED(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ## ghIQR - # Inter-quartile Range: ghIQR(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ## ghSKEW - # Robust Skewness: ghSKEW(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) ## ghKURT - # Robust Kurtosis: ghKURT(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \keyword{distribution} fBasics/man/fBasics-deprecated.Rd0000644000176200001440000000311714650724110016340 0ustar liggesusers% Part of the Rmetrics project, http://R-metrics.org/ % Copyright 2005-2011 Rmetrics Core Team % Distributed under GPL 2 or later \name{fBasics-deprecated} %------ NOTE: ../R/fBasics-deprecated.R must be synchronized with this! \alias{fBasics-deprecated} %% \alias{dstable} %% \alias{pstable} %% \alias{qstable} %% \alias{rstable} %% \alias{stableMode} %% \alias{listDescription} % functions with names starting with '.' still used by other packages; % (see file NAMESPACE) \alias{.acfPlot} \alias{.contourPlot} \alias{.distCheck} \alias{.firePlot} \alias{.mrlPlot} \alias{.pacfPlot} \alias{.perspPlot} \alias{.plot} \alias{.predict} \alias{.qStableFit} \alias{.residualsPlot} \alias{.responsesPlot} \alias{.sliderMenu} \alias{.unirootNA} \title{Deprecated functions in package fBasics} \description{ These functions are provided for compatibility with older versions of the package only, and may be defunct as soon as of the next release. } %\usage{ %} %\arguments{ %} \details{ %% \describe{ %% %% \item{listDescription}{use \code{\link{packageDescription}} instead.} %% %% } There are none currently. \code{\link[stabledist]{dstable}}, etc., now are defunct, as they have been available from \pkg{stabledist} since early 2011. %% dstable(x, alpha, beta, gamma = 1, delta = 0, pm = 0) %% pstable(q, alpha, beta, gamma = 1, delta = 0, pm = 0) %% qstable(p, alpha, beta, gamma = 1, delta = 0, pm = 0) %% rstable(n, alpha, beta, gamma = 1, delta = 0, pm = 0) %% stableMode(alpha, beta) } \seealso{ \code{\link{Deprecated}}, \code{\link{Defunct}} } \keyword{misc} fBasics/man/utils-listFunctions.Rd0000644000176200001440000000256314660436547016715 0ustar liggesusers\name{listFunctions} \alias{listFunctions} \alias{countFunctions} \alias{listIndex} \title{List exported functions in a package} \description{ Utilities to list and count exported functions in a package, list the contents of the description file of a package, and Prints the content of an index file for a package (a list of the objects exported by a package). } \usage{ listFunctions(package, character.only = FALSE) countFunctions(package, character.only = FALSE) listIndex(package, character.only = FALSE) } \arguments{ \item{package}{ a literal character string or a character string denoting the name of a package. } \item{character.only}{ a logical indicating whether 'package' can be assumed to be a character string. } } \value{ for \code{listFunctions}, a character vector containing the names of the exported functions in a package, for \code{countFunctions}, a named numeric value giving the number of the exported functions in a package. \code{listIndex} doesn't return a useful value. It is used for the side effect of printing the description or index. } \note{ Be aware that \code{listFunctions} and \code{countFunctions} attach the package to the search path. } \seealso{ \code{\link{packageDescription}} } \examples{ listFunctions("fBasics") countFunctions("fBasics") } \keyword{package} fBasics/man/dist-nigRobMoments.Rd0000644000176200001440000000255214650724110016416 0ustar liggesusers\name{nigRobMoments} \alias{nigRobMoments} \alias{nigMED} \alias{nigIQR} \alias{nigSKEW} \alias{nigKURT} \title{Robust Moments for the NIG} \description{ Computes the first four robust moments for the Normal Inverse Gaussian Distribution. } \usage{ nigMED(alpha = 1, beta = 0, delta = 1, mu = 0) nigIQR(alpha = 1, beta = 0, delta = 1, mu = 0) nigSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) nigKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha}{ shape parameter. } \item{beta}{ skewness parameter \code{beta}, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{nig} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz. } \examples{ ## nigMED - # Median: nigMED(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigIQR - # Inter-quartile Range: nigIQR(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigSKEW - # Robust Skewness: nigSKEW(alpha = 1, beta = 0, delta = 1, mu = 0) ## nigKURT - # Robust Kurtosis: nigKURT(alpha = 1, beta = 0, delta = 1, mu = 0) } \keyword{distribution} fBasics/man/plot-histPlot.Rd0000644000176200001440000000513214660351417015460 0ustar liggesusers\name{HistogramPlot} \alias{HistogramPlot} \alias{histPlot} \alias{densityPlot} \alias{logDensityPlot} \title{Histogram and density plots} \description{ Produce tailored histogram plots and kernel density/log-density estimate plots. } \usage{ histPlot(x, labels = TRUE, col = "steelblue", fit = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, \dots) densityPlot(x, labels = TRUE, col = "steelblue", fit = TRUE, hist = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, \dots) logDensityPlot(x, labels = TRUE, col = "steelblue", robust = TRUE, title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{labels}{ a logical flag, should the plot be returned with default labels and decorated in an automated way? By default \code{TRUE}. } \item{col}{ the color for the series. In the univariate case use just a color name like the default, \code{col="steelblue"}, in the multivariate case we recommend to select the colors from a color palette, e.g. \code{col=heat.colors(ncol(x))}. } \item{fit}{ a logical flag, should a fit be added to the plot? } \item{hist}{ a logical flag, by default \code{TRUE}. Should a histogram be laid under the plot? } \item{title}{ a logical flag, by default \code{TRUE}. Should a default title be added to the plot? } \item{grid}{ a logical flag, should a grid be added to the plot? By default \code{TRUE}. To plot a horizontal lines only use \code{grid="h"} and for vertical lines use \code{grid="h"}, respectively. } \item{rug}{ a logical flag, by default TRUE. Should a rug representation of the data be added to the plot? } \item{skip}{ a logical flag, should zeros be skipped in the return Series? } \item{robust}{ a logical flag, by default \code{TRUE}. Should a robust fit be added to the plot? } \item{\dots}{ optional arguments to be passed on. } } \details{ \code{histPlot} produces a tailored histogram plot. \code{densityPlot} produces a tailored kernel density estimate plot. \code{logDensityPlot} produces a tailored log kernel density estimate plot. } \value{ \code{NULL}, invisibly. The functions are used for the side effect of producing a plot. } \examples{ ## data data(LPP2005REC, package = "timeSeries") SPI <- LPP2005REC[, "SPI"] plot(SPI, type = "l", col = "steelblue", main = "SP500") abline(h = 0, col = "grey") histPlot(SPI) densityPlot(SPI) } \keyword{hplot} fBasics/man/dist-ghFit.Rd0000644000176200001440000000454414657145035014711 0ustar liggesusers\name{ghFit} \alias{ghFit} \title{GH Distribution Fit} \description{ Estimates the distrinbutional parameters for a generalized hyperbolic distribution. } \usage{ ghFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{alpha}{ first shape parameter. } \item{beta}{ second shape parameter, should in the range \code{(0, alpha).} } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{lambda}{ defines the sublclass, by default \eqn{-1/2}. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \value{ an object from class \code{"fDISTFIT"}. Slot \code{fit} is a list, currently with components \code{estimate}, \code{minimum} and \code{code}. } \details{ The meanings of the parameters correspond to the first parameterization, see \code{\link{gh}} for further details. The function \code{\link{nlm}} is used to minimize the "negative" maximum log-likelihood function. \code{nlm} carries out a minimization using a Newton-type algorithm. } \examples{ set.seed(1953) s <- rgh(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0) ghFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE, trace = FALSE) } \keyword{distribution} fBasics/man/dist-gldMode.Rd0000644000176200001440000000074314650724110015206 0ustar liggesusers\name{gldMode} \alias{gldMode} \title{Generalized Lambda Distribution Mode} \description{ Computes the mode of the generalized lambda distribution. } \usage{ gldMode(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \arguments{ \item{lambda1}{ location parameter. } \item{lambda2}{ scale parameter. } \item{lambda3}{ first shape parameter. } \item{lambda4}{ second shape parameter. } } \author{ Implemented by Diethelm Wuertz } fBasics/man/dist-gh.Rd0000644000176200001440000000723114650724110014230 0ustar liggesusers\name{gh} \alias{gh} \alias{dgh} \alias{pgh} \alias{qgh} \alias{rgh} \concept{generalized hyperbolic distribution} \title{Generalized Hyperbolic Distribution} \description{ Density, distribution function, quantile function and random generation for the generalized hyperbolic distribution. } \usage{ dgh(x, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2, log = FALSE) pgh(q, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) qgh(p, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) rgh(n, alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{alpha}{ first shape parameter. } \item{beta}{ second shape parameter, should in the range \code{(0, alpha).} } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{lambda}{ defines the sublclass, by default \eqn{-1/2}. } \item{log}{ a logical flag by default \code{FALSE}. Should labels and a main title drawn to the plot? } } \details{ \code{dgh} gives the density, \code{pgh} gives the distribution function, \code{qgh} gives the quantile function, and \code{rgh} generates random deviates. The meanings of the parameters correspond to the first parameterization, \code{pm=1}, which is the default parameterization for this distribution. In the second parameterization, \code{pm=2}, \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{zeta} and \code{rho}. In the third parameterization, \code{pm=3}, \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{xi} and \code{chi}. In the fourth parameterization, \code{pm=4}, \code{alpha} and \code{beta} take the meaning of the shape parameters (usually named) \code{a.bar} and \code{b.bar}. The generator \code{rgh} is based on the GH algorithm given by Scott (2004). } \value{ numeric vector } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \author{ David Scott for code implemented from \R's contributed package \code{HyperbolicDist}. } \examples{ ## rgh - set.seed(1953) r = rgh(5000, alpha = 1, beta = 0.3, delta = 1) plot(r, type = "l", col = "steelblue", main = "gh: alpha=1 beta=0.3 delta=1") ## dgh - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dgh(x, alpha = 1, beta = 0.3, delta = 1)) ## pgh - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, pgh(x, alpha = 1, beta = 0.3, delta = 1)) ## qgh - # Compute Quantiles: qgh(pgh(seq(-5, 5, 1), alpha = 1, beta = 0.3, delta = 1), alpha = 1, beta = 0.3, delta = 1) } \keyword{distribution} fBasics/man/dist-nigMode.Rd0000644000176200001440000000261114650724110015211 0ustar liggesusers\name{nigMode} \alias{nigMode} \title{Normal Inverse Gaussian Mode} \description{ Computes the mode of the norm inverse Gaussian distribution. } \usage{ nigMode(alpha = 1, beta = 0, delta = 1, mu = 0) } \arguments{ \item{alpha}{ shape parameter. } \item{beta}{ skewness parameter \code{beta}, \code{abs(beta)} is in the range \code{(0, alpha)}. } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } } \value{ a numeric value, the mode of the normal inverse Gaussian distribution } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## nigMode - nigMode() } \keyword{distribution} fBasics/man/dist-sghFit.Rd0000644000176200001440000000450414657144312015065 0ustar liggesusers\name{sghFit} \alias{sghFit} \concept{Standardized generalized hyperbolic distribution} \title{Standardized GH distribution fit} \description{ Estimates the distributional parameters for a standardized generalized hyperbolic distribution. } \usage{ sghFit(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{zeta, rho, lambda}{ shape parameter \code{zeta} is positive, skewness parameter \code{rho} is in the range (-1, 1). and index parameter \code{lambda}, by default 1. } \item{include.lambda}{ a logical flag, by default \code{TRUE}. Should the index parameter \code{lambda} included in the parameter estimate? } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \value{ an object from class \code{"fDISTFIT"}. Slot \code{fit} is a list, currently with components \code{estimate}, \code{minimum}, \code{code}, \code{param}, \code{mean} (mean of the original data), \code{var} (variance of original data). } \examples{ set.seed(1953) s <- rsgh(n = 2000, zeta = 0.7, rho = 0.5, lambda = 0) sghFit(s, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, doplot = TRUE, trace = FALSE) } \keyword{distribution} fBasics/man/dist-ghtMode.Rd0000644000176200001440000000304014650724110015213 0ustar liggesusers\name{ghtMode} \alias{ghtMode} \title{Generalized Hyperbolic Student-t Mode} \description{ Computes the mode of the generalized hyperbolic Student-t distribution. } \usage{ ghtMode(beta = 0.1, delta = 1, mu = 0, nu = 10) } \arguments{ \item{beta}{the skewness parameter in the range \code{(0, alpha).}} \item{delta}{the scale parameter, must be zero or positive.} \item{mu}{the location parameter, by default 0.} \item{nu}{ a numeric value, the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } } \details{ These are the parameters in the first parameterization. } \value{ a numeric value, the mode for the generalized hyperbolic Student-t distribution. } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## ghtMode - ghtMode() } \keyword{distribution} fBasics/man/fHTEST-class.Rd0000644000176200001440000000442014657060765015046 0ustar liggesusers\name{fHTEST-class} \Rdversion{1.1} \docType{class} \alias{fHTEST-class} \alias{fHTEST} \alias{show,fHTEST-method} \title{Class \code{"fHTEST"}} \description{ An S4 class representing the outcome of a statistical test. } \section{Objects from the Class}{ % Objects can be created by calls of the form \code{new("fHTEST", ...)}. Objects from this class are created by some statistical test functions. %% ~~ describe objects here ~~ } \section{Slots}{ \describe{ \item{\code{call}:}{ the function call. } \item{\code{data}:}{ the data as specified by the input argument(s). } \item{\code{test}:}{ a list whose elements contain the results from the statistical test. The information provided is similar to a list object of class \code{"htest"}. } \item{\code{title}:}{ a character string with the name of the test. This can be overwritten specifying a user defined input argument. } \item{\code{description}:}{ a character string with an optional user defined description. By default just the current date when the test was applied will be returned. } } Slot \code{@test} is an object of class \code{"list"} containing at least the following elements: \describe{ \item{statistic}{ the value(s) of the test statistic. } \item{p.value}{ the p-value(s) of the test. } \item{parameters}{ a numeric value or vector of parameters. } \item{estimate}{ a numeric value or vector of sample estimates. } \item{conf.int}{ a numeric two row vector or matrix of 95\% confidence levels. } \item{method}{ a character string indicating what type of test was performed. } \item{data.name}{ a character string giving the name(s) of the data. } } } \section{Methods}{ \describe{ \item{show}{\code{signature(object = "fHTEST")}: ... } } } \seealso{ for functions returning objects from class \code{"fHTEST"}, see \code{\link{scaleTest}}, \code{\link{correlationTest}}, \code{\link{ks2Test}}, \code{\link{locationTest}}, \code{\link{NormalityTests}}, \code{\link{varianceTest}} \code{\link{scaleTest}} } \examples{ showClass("fHTEST") } \keyword{classes} \keyword{htest} fBasics/man/dist-nigSlider.Rd0000644000176200001440000000076614650724110015560 0ustar liggesusers\name{nigSlider} \alias{nigSlider} \title{nigerbolic Distribution Slider} \description{ Displays interactively the dependence of the nigerbolic distribution on its parameters. } \usage{ nigSlider() } \value{ a tcl/tk based graphical user interface. This is a nice display for educational purposes to investigate the densities and probabilities of the invetrse Gaussian distribution. } \examples{ ## nigSlider - # nigSlider() } \keyword{distribution} fBasics/man/dist-gldRobMoments.Rd0000644000176200001440000000257514650724110016414 0ustar liggesusers\name{gldRobMoments} \alias{gldRobMoments} \alias{gldMED} \alias{gldIQR} \alias{gldSKEW} \alias{gldKURT} \title{Robust Moments for the GLD} \description{ Computes the first four robust moments for the Generalized Lambda Distribution. } \usage{ gldMED(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) gldIQR(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) gldSKEW(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) gldKURT(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \arguments{ \item{lambda1}{location parameter} \item{lambda2}{scale parameter} \item{lambda3}{first shape parameter} \item{lambda4}{second shape parameter} } \value{ a named numerical value. The name is one of \code{MED}, \code{IQR}, \code{SKEW}, or \code{KURT}, obtained by dropping the \code{gld} prefix from the name of the corresponding function. } \author{ Diethelm Wuertz. } \examples{ ## gldMED - # Median: gldMED(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) ## gldIQR - # Inter-quartile Range: gldIQR(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) ## gldSKEW - # Robust Skewness: gldSKEW(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) ## gldKURT - # Robust Kurtosis: gldKURT(lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \keyword{distribution} fBasics/man/dist-ssd.Rd0000644000176200001440000000315714650724110014426 0ustar liggesusers\name{ssd} \alias{ssd} \alias{dssd} \alias{pssd} \alias{qssd} \alias{rssd} \concept{Spline Smoothed Distribution} \title{Spline Smoothed Distribution} \description{ Density, distribution function, quantile function and random generation from smoothing spline estimates. } \usage{ dssd(x, param, log = FALSE) pssd(q, param) qssd(p, param) rssd(n, param) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{param}{ an object as returned by the function \code{ssdFit}. } \item{log}{ a logical flag by default \code{FALSE}. Should labels and a main title drawn to the plot? } } \details{ \code{dssd} gives the density, \code{pssd} gives the distribution function, \code{qssd} gives the quantile function, and \code{rssd} generates random deviates. } \value{ numeric vector } \author{ Diethelm Wuertz, Chong Gu for the underlying \code{gss} package. } \references{ Gu, C. (2002), \emph{Smoothing Spline ANOVA Models}, New York Springer--Verlag. Gu, C. and Wang, J. (2003), \emph{Penalized likelihood density estimation: Direct cross-validation and scalable approximation}, Statistica Sinica, 13, 811--826. } \examples{ ## ssdFit - set.seed(1953) r = rnorm(500) hist(r, breaks = "FD", probability = TRUE, col = "steelblue", border = "white") ## ssdFit - param = ssdFit(r) ## dssd - u = seq(min(r), max(r), len = 301) v = dssd(u, param) lines(u, v, col = "orange", lwd = 2) } \keyword{distribution} fBasics/man/stats-interpLinear.Rd0000644000176200001440000000452514660432334016471 0ustar liggesusers\name{linearInterp} \alias{linearInterp} \alias{linearInterpp} \concept{interpolation} \concept{bivariate interpolation} \concept{bivariate linear interpolation} \title{Bivariate Linear Interpolation} \description{ Bivariate Linear Interpolation. Options are available for gridded and pointwise interpolation. } \usage{ linearInterp(x, y = NULL, z = NULL, gridPoints = 21, xo = seq(min(x), max(x), length = gridPoints), yo = seq(min(y), max(y), length = gridPoints)) linearInterpp(x, y = NULL, z = NULL, xo, yo) } \arguments{ \item{x, y, z}{ for \code{linearInterp} the arguments \code{x} and \code{y} are two numeric vectors of grid pounts, and \code{z} is a numeric matrix or any other rectangular object which can be transformed by the function \code{as.matrix} into a matrix object. For \code{linearInterpp} we consider either three numeric vectors of equal length or if \code{y} and \code{z} are NULL, a list with entries \code{x}, \code{y}, \code{z}, or named data frame with \code{x} in the first, \code{y} in the second, and \code{z} in the third column. } \item{gridPoints}{ an integer value specifying the number of grid points in \code{x} and \code{y} direction. } \item{xo, yo}{ for \code{linearInterp} two numeric vectors of data points spanning the grid, and for \code{linearInterpp} two numeric vectors of data points building pairs for pointwise interpolation. } } \value{ for \code{linearInterp}, a list with at least three entries, \code{x}, \code{y} and \code{z}. The returned values, can be used directly in \code{persp} and \code{contour} 3D plotting methods. for \code{linearInterpp}, a \code{data.frame} with columns \code{"x"}, \code{"y"}, and \code{"z"}. } \seealso{ \code{\link{akimaInterp}} and \code{\link{krigeInterp}} } \examples{ ## Linear Interpolation: if (requireNamespace("interp")) { set.seed(1953) x <- runif(999) - 0.5 y <- runif(999) - 0.5 z <- cos(2 * pi * (x^2 + y^2)) ans = linearInterp(x, y, z, gridPoints = 41) persp(ans, theta = -40, phi = 30, col = "steelblue", xlab = "x", ylab = "y", zlab = "z") contour(ans) } } \keyword{math} fBasics/man/dist-ghMode.Rd0000644000176200001440000000305014650724110015030 0ustar liggesusers\name{ghMode} \alias{ghMode} \title{Generalized Hyperbolic Mode} \description{ Computes the mode of the generalized hyperbolic function. } \usage{ ghMode(alpha = 1, beta = 0, delta = 1, mu = 0, lambda = -1/2) } \arguments{ \item{alpha}{ first shape parameter. } \item{beta}{ second shape parameter, should in the range \code{(0, alpha).} } \item{delta}{ scale parameter, must be zero or positive. } \item{mu}{ location parameter, by default 0. } \item{lambda}{ defines the sublclass, by default \eqn{-1/2}. } } \details{ The meanings of the parameters correspond to the first parameterization, see \code{\link{gh}} for further details. } \value{ a numeric value, the mode of the generalized hyperbolic distribution } \references{ Atkinson, A.C. (1982); \emph{The simulation of generalized inverse Gaussian and hyperbolic random variables}, SIAM J. Sci. Stat. Comput. 3, 502--515. Barndorff-Nielsen O. (1977); \emph{Exponentially decreasing distributions for the logarithm of particle size}, Proc. Roy. Soc. Lond., A353, 401--419. Barndorff-Nielsen O., Blaesild, P. (1983); \emph{Hyperbolic distributions. In Encyclopedia of Statistical Sciences}, Eds., Johnson N.L., Kotz S. and Read C.B., Vol. 3, pp. 700--707. New York: Wiley. Raible S. (2000); \emph{Levy Processes in Finance: Theory, Numerics and Empirical Facts}, PhD Thesis, University of Freiburg, Germany, 161 pages. } \examples{ ## ghMode - ghMode() } \keyword{distribution} fBasics/man/dist-gldFit.Rd0000644000176200001440000000571014657145364015062 0ustar liggesusers\name{gldFit} \alias{gldFit} \title{GH Distribution Fit} \description{ Estimates the distrinbutional parameters for a generalized lambda distribution. } \usage{ gldFit(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, method = c("mle", "mps", "gof", "hist", "rob"), scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{x}{ a numeric vector. } \item{lambda1, lambda2, lambda3, lambda4}{ are numeric values where \code{lambda1} is the location parameter, \code{lambda2} is the location parameter, \code{lambda3} is the first shape parameter, and \code{lambda4} is the second shape parameter. } \item{method}{ a character string, the estimation approach to fit the distributional parameters, see details. } \item{scale}{ not used. %a logical flag, by default \code{TRUE}. Should the time series %be scaled by its standard deviation to achieve a more stable %optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{add}{ a logical flag. Should a new fit added to an existing plot? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and rigldt endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \details{ The function \code{\link{nlminb}} is used to minimize the objective function. The following approaches have been implemented: \code{"mle"}, maximimum log likelihoo estimation. \code{"mps"}, maximum product spacing estimation. \code{"gof"}, goodness of fit approaches, \code{type="ad"} Anderson-Darling, \code{type="cvm"} Cramer-vonMise, \code{type="ks"} Kolmogorov-Smirnov. \code{"hist"}, histogram binning approaches, \code{"fd"} Freedman-Diaconis binning, \code{"scott"}, Scott histogram binning, \code{"sturges"}, Sturges histogram binning. \code{"rob"}, robust moment matching. } \value{ an object from class \code{"fDISTFIT"}. Slot \code{fit} is a list, currently with components \code{estimate}, \code{minimum} and \code{code}. } \examples{ set.seed(1953) s <- rgld(n = 1000, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) gldFit(s, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8, doplot = TRUE, trace = FALSE) } \keyword{distribution} fBasics/man/dist-ssdFit.Rd0000644000176200001440000000211514650724110015062 0ustar liggesusers\name{ssdFit} \alias{ssdFit} \title{Fit density using smoothing splines} \description{ Estimates the parameters of a density function using smoothing splines. } \usage{ ssdFit(x) } \arguments{ \item{x}{ a numeric vector. } } \value{ for \code{ssdFit}, an object of class \code{ssden}. The returned object can be used to evaluate density, probabilities and quantiles. } \author{ Diethelm Wuertz, Chong Gu for the underlying \code{gss} package. } \references{ Gu, C. (2002), \emph{Smoothing Spline ANOVA Models}, New York Springer--Verlag. Gu, C. and Wang, J. (2003), \emph{Penalized likelihood density estimation: Direct cross-validation and scalable approximation}, Statistica Sinica, 13, 811--826. } \examples{ ## ssdFit - set.seed(1953) r = rnorm(500) hist(r, breaks = "FD", probability = TRUE, col = "steelblue", border = "white") ## ssdFit - param = ssdFit(r) ## dssd - u = seq(min(r), max(r), len = 301) v = dssd(u, param) lines(u, v, col = "orange", lwd = 2) } \keyword{distribution} fBasics/man/utils-colorLocator.Rd0000644000176200001440000000243614650724110016473 0ustar liggesusers\name{colorLocator} \alias{colorLocator} \alias{colorMatrix} \title{Named colors in R} \description{ Displays \R's 657 named colors for selection and returns optionally \R's color names. } \usage{ colorLocator(locator = FALSE, cex.axis = 0.7) colorMatrix() } \arguments{ \item{locator}{ logical, if true, \code{\link{locator}} is used for interactive selection of color names, default is \code{FALSE}. } \item{cex.axis}{ size of axis labels. } } \value{ Color Locator: \code{colorsLocator()} generates a plot with R colors and, when \code{locator} is true, returns matrix with graph coordinates and names of colors selected. \code{colorsMatrix()} quietly returns the matrix of names. } \details{ Color Locator: The \code{colorLocator} function plots R's 657 named colors. If \code{locator=TRUE} then you can interactively point and click to select the colors for which you want names. To end selection, right click on the mouse and select 'Stop', then R returns the selected color names. The functions used here are wrappers to the functions provided by Tomas Aragon in the contributed R package \code{epitools}. } \seealso{ \code{\link{colorPalette}}, \code{\link{colorTable}}. } \examples{ colorLocator() } \keyword{programming} fBasics/man/utils-interactivePlot.Rd0000644000176200001440000000617014660436512017213 0ustar liggesusers\name{interactivePlot} \alias{interactivePlot} \title{Interactive Plot Utility} \description{ Plots with emphasis on interactive plots. } \usage{ interactivePlot(x, choices = paste("Plot", 1:9), plotFUN = paste("plot.", 1:9, sep = ""), which = "all", \dots) } \arguments{ \item{x}{ an object to be plotted. } \item{choices}{ a character vector of length at most 9, giving descriptive names of the plots for the menu presented to the user. } \item{plotFUN}{ a vector of the same length as \code{choices}, containing functions and/or names of functions. \code{plotFUN[[i]]} is called to produce the plot corresponding to \code{choice[i]}. } \item{which}{ Which graph(s) should be displayed? One of the character strings \code{"ask"} (ask the user) or \code{"all"} (produce all plots), or a logical vector in which the positions of the \code{TRUE} values designating the plots to produce. } \item{\dots}{ additional arguments passed to the FUN or plot function. (2023-10-21 GNB: currently the \code{"..."} arguments are not really passed on to the plotting functions.) } } \details{ If \code{"which"} is the character string \code{"ask"}, then the user is presented with a menu to interactively choose which plot(s) to show. Argument \code{choices} is used for the choices in the menu, so they should be informative. If \code{"which"} is equal to \code{"all"} all plots are drawn. If \code{"which"} is a logical vector, the indicate plots are displayed. Note that if more plots are to be shown in one window, the arrangement should be made in advance (and cleaned up afterwards), see the examples. } \examples{ ## Test Plot Function testPlot <- function(x, which = "all", ...) { ## Plot Function and Addons plot.1 <<- function(x, ...) plot(x, ...) plot.2 <<- function(x, ...) acf(x, ...) plot.3 <<- function(x, ...) hist(x, ...) plot.4 <<- function(x, ...) qqnorm(x, ...) ## Plot interactivePlot( x, choices = c("Series Plot", "ACF", "Histogram", "QQ Plot"), plotFUN = c("plot.1", "plot.2", "plot.3", "plot.4"), which = which, ...) ## Return Value invisible() } ## Plot ## prepare the window and store its previous state op <- par(mfrow = c(2, 2), cex = 0.7) ## produce the plot testPlot(rnorm(500)) ## restore the previous state par(op) ## Try: ## par(mfrow = c(1,1)) ## testPlot(rnorm(500), which = "ask") ## similar to above but using functions for plotFUN testPlot_2 <- function(x, which = "all", ...) { interactivePlot( x, choices = c("Series Plot", "ACF", "Histogram", "QQ Plot"), plotFUN = c(plot.1 = function(x, ...) plot(x, ...), plot.2 = function(x, ...) acf(x, ...), plot.3 = function(x, ...) hist(x, ...), plot.4 = function(x, ...) qqnorm(x, ...) ), which = which, ...) ## Return Value: invisible() } ## produce the plot op <- par(mfrow = c(2, 2), cex = 0.7) testPlot_2(rnorm(500)) par(op) } \keyword{hplot} fBasics/man/dist-gld.Rd0000644000176200001440000000430614650724110014400 0ustar liggesusers\name{gld} \alias{gld} \alias{dgld} \alias{pgld} \alias{qgld} \alias{rgld} \concept{generalized lambda distribution} \title{Generalized Lambda Distribution} \description{ Density, distribution function, quantile function and random generation for the generalized lambda distribution. } \usage{ dgld(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, log = FALSE) pgld(q, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) qgld(p, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) rgld(n, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8) } \arguments{ \item{lambda1}{ location parameter. } \item{lambda2}{ scale parameter. } \item{lambda3}{ first shape parameter. } \item{lambda4}{ second shape parameter. } \item{n}{ number of observations. } \item{p}{ a numeric vector of probabilities. } \item{x, q}{ a numeric vector of quantiles. } \item{log}{ a logical, if TRUE, probabilities \code{p} are given as \code{log(p)}. } } \details{ \code{dgld} gives the density, \code{pgld} gives the distribution function, \code{qgld} gives the quantile function, and \code{rgld} generates random deviates. } \value{ numeric vector } \author{ Chong Gu for code implemented from \R's contributed package \code{gld}. } \examples{ ## rgld - set.seed(1953) r = rgld(500, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) plot(r, type = "l", col = "steelblue", main = "gld: lambda1=0 lambda2=-1 lambda3/4=-1/8") ## dgld - # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue") x = seq(-5, 5, 0.25) lines(x, dgld(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)) ## pgld - # Plot df and compare with true df: plot(sort(r), ((1:500)-0.5)/500, main = "Probability", col = "steelblue") lines(x, pgld(x, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8)) ## qgld - # Compute Quantiles: qgld(pgld(seq(-5, 5, 1), lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8), lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) } \keyword{distribution} fBasics/man/fBasics-reexports.Rd0000644000176200001440000000114114323431044016264 0ustar liggesusers% see Repeated descriptions of re-exported functions #408 at % https://github.com/klutometis/roxygen/issues/408 \docType{import} \name{fBasics_reexports} %\alias{fBasics_reexports} \alias{as.timeSeries} \alias{kurtosis} \alias{skewness} \title{Objects exported from other packages} \description{ These objects are imported from other packages. Follow the links below to see their documentation. \describe{ \item{lagged}{\code{\link[timeSeries]{as.timeSeries}}} \item{kurtosis}{\code{\link[timeDate]{kurtosis}}} \item{skewness}{\code{\link[timeDate]{skewness}}} } } \keyword{internal} fBasics/man/dist-ghtFit.Rd0000644000176200001440000000472414657145136015077 0ustar liggesusers\name{ghtFit} \alias{ghtFit} \title{GHT distribution fit} \description{ Estimates the distributional parameters for a generalized hyperbolic Student-t distribution. } \usage{ ghtFit(x, beta = 0.1, delta = 1, mu = 0, nu = 10, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, \dots) } \arguments{ \item{beta, delta, mu}{ numeric values. \code{beta} is the skewness parameter in the range \code{(0, alpha)}; \code{delta} is the scale parameter, must be zero or positive; \code{mu} is the location parameter, by default 0. These are the parameters in the first parameterization. } \item{nu}{ defines the number of degrees of freedom. Note, \code{alpha} takes the limit of \code{abs(beta)}, and \code{lambda=-nu/2}. } \item{x}{ a numeric vector. } \item{scale}{ a logical flag, by default \code{TRUE}. Should the time series be scaled by its standard deviation to achieve a more stable optimization? } \item{doplot}{ a logical flag. Should a plot be displayed? } \item{span}{ x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like \code{span=seq(min, max, times = n)}, where, \code{min} and \code{max} are the left and right endpoints of the range, and \code{n} gives the number of the intermediate points. } \item{trace}{ a logical flag. Should the parameter estimation process be traced? } \item{title}{ a character string which allows for a project title. } \item{description}{ a character string which allows for a brief description. } \item{\dots}{ parameters to be parsed. } } \details{ The function \code{\link{nlm}} is used to minimize the "negative" log-likelihood function. \code{nlm} carries out a minimization using a Newton-type algorithm. } \value{ an object from class \code{"fDISTFIT"}. Slot \code{fit} is a list, currently with components \code{estimate}, \code{minimum} and \code{code}. } \examples{ ## ghtFit - # Simulate Random Variates: set.seed(1953) ## ghtFit - # Fit Parameters: } \keyword{distribution} fBasics/man/dist-sgh.Rd0000644000176200001440000000400114650724110014403 0ustar liggesusers\name{sgh} \alias{sgh} \alias{dsgh} \alias{psgh} \alias{qsgh} \alias{rsgh} \concept{standardized generalized hyperbolic distribution} \title{Standardized Generalized Hyperbolic Distribution} \description{ Density, distribution function, quantile function and random generation for the standardized generalized hyperbolic distribution. } \usage{ dsgh(x, zeta = 1, rho = 0, lambda = 1, log = FALSE) psgh(q, zeta = 1, rho = 0, lambda = 1) qsgh(p, zeta = 1, rho = 0, lambda = 1) rsgh(n, zeta = 1, rho = 0, lambda = 1) } \arguments{ \item{x, q}{ a numeric vector of quantiles. } \item{p}{ a numeric vector of probabilities. } \item{n}{ number of observations. } \item{zeta}{shape parameter, a positive number.} \item{rho}{skewness parameter, a number in the range \eqn{(-1, 1)}.} \item{lambda}{ ?? } \item{log}{ a logical flag by default \code{FALSE}. If \code{TRUE}, log values are returned. } } \value{ numeric vector } \details{ \code{dsgh} gives the density, \code{psgh} gives the distribution function, \code{qsgh} gives the quantile function, and \code{rsgh} generates random deviates. The generator \code{rsgh} is based on the GH algorithm given by Scott (2004). } \author{ Diethelm Wuertz } \examples{ ## rsgh - set.seed(1953) r = rsgh(5000, zeta = 1, rho = 0.5, lambda = 1) plot(r, type = "l", col = "steelblue", main = "gh: zeta=1 rho=0.5 lambda=1") ## dsgh - # Plot empirical density and compare with true density: hist(r, n = 50, probability = TRUE, border = "white", col = "steelblue", ylim = c(0, 0.6)) x = seq(-5, 5, length = 501) lines(x, dsgh(x, zeta = 1, rho = 0.5, lambda = 1)) ## psgh - # Plot df and compare with true df: plot(sort(r), (1:5000/5000), main = "Probability", col = "steelblue") lines(x, psgh(x, zeta = 1, rho = 0.5, lambda = 1)) ## qsgh - # Compute Quantiles: round(qsgh(psgh(seq(-5, 5, 1), zeta = 1, rho = 0.5), zeta = 1, rho = 0.5), 4) } \keyword{distribution} fBasics/man/fDISTFIT-class.Rd0000644000176200001440000000426414657137467015277 0ustar liggesusers\name{fDISTFIT-class} \Rdversion{1.1} \docType{class} \alias{fDISTFIT-class} \alias{show,fDISTFIT-method} \title{Class \code{"fDISTFIT"}} \description{ S4 class representing fitted distributions. } \section{Objects from the Class}{ Objects can be created by calls of the form \code{new("fDISTFIT", ...)} but are typically created by functions fitting distributions. %% ~~ describe objects here ~~ } \section{Slots}{ \describe{ \item{\code{call}:}{Object of class \code{"call"} ~~ } \item{\code{model}:}{Object of class \code{"character"} ~~ } \item{\code{data}:}{Object of class \code{"data.frame"} ~~ } \item{\code{fit}:}{Object of class \code{"list"} ~~ } \item{\code{title}:}{Object of class \code{"character"} ~~ } \item{\code{description}:}{Object of class \code{"character"} ~~ } } Slot \code{fit} is a list. Its components depend on the fitting functions. Here is the meaning of some common ones: \describe{ \item{estimate}{ the point at which the maximum value of the log liklihood function is obtained. } \item{minimum}{ the value of the estimated maximum, i.e. the value of the log liklihood function. } \item{code}{ an integer indicating why the optimization process terminated.\cr 1: relative gradient is close to zero, current iterate is probably solution; \cr 2: successive iterates within tolerance, current iterate is probably solution; \cr 3: last global step failed to locate a point lower than \code{estimate}. Either \code{estimate} is an approximate local minimum of the function or \code{steptol} is too small; \cr 4: iteration limit exceeded; \cr 5: maximum step size \code{stepmax} exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or \code{stepmax} is too small. } \item{gradient}{ the gradient at the estimated maximum. } } } \section{Methods}{ \describe{ \item{show}{\code{signature(object = "fDISTFIT")}: ... } } } \examples{ showClass("fDISTFIT") } \keyword{classes} fBasics/man/test-correlationTest.Rd0000644000176200001440000000450414660433560017036 0ustar liggesusers\name{correlationTest} \alias{correlationTest} \alias{pearsonTest} \alias{kendallTest} \alias{spearmanTest} \concept{Pearson's product moment correlation test} \concept{Kendall's tau correlation test} \concept{Spearman's rho correlation test} \concept{Pearson's correlation} \concept{Kendall's correlation} \concept{Spearman's correlation} \title{Correlation tests} \description{ Tests if two series are correlated. } \usage{ correlationTest(x, y, method = c("pearson", "kendall", "spearman"), title = NULL, description = NULL) pearsonTest(x, y, title = NULL, description = NULL) kendallTest(x, y, title = NULL, description = NULL) spearmanTest(x, y, title = NULL, description = NULL) } \arguments{ \item{x,y}{ numeric vectors of data values. } \item{method}{ a character string naming which test should be applied. } \item{title}{ an optional title string, if not specified the input's data name is deparsed. } \item{description}{ optional description string, or a vector of character strings. } } \details{ These functions test for association/correlation between paired samples based on the Pearson's product moment correlation coefficient (a.k.a. sample correlation), Kendall's tau, and Spearman's rho coefficients. \code{pearsonTest}, \code{kendallTest}, and \code{spearmanTest} are wrappers of base R's \code{\link[stats]{cor.test}} with simplified interface. They provide 'exact' and approximate p-values for all three alternatives (two-sided, less, and greater), as well as 95\% confidence intervals. This is particularly convenient in interactive use. Instead of calling the individual functions, one can use \code{correlationTest} and specify the required test with argument \code{method}. } \value{ an object from class \code{\link{fHTEST}} } \references{ Conover, W. J. (1971); \emph{Practical nonparametric statistics}, New York: John Wiley & Sons. Lehmann E.L. (1986); \emph{Testing Statistical Hypotheses}, John Wiley and Sons, New York. } \seealso{ \code{\link{locationTest}}, \code{\link{scaleTest}}, \code{\link{varianceTest}} } \examples{ \dontshow{ set.seed(1234) } x <- rnorm(50) y <- rnorm(50) correlationTest(x, y, "pearson") correlationTest(x, y, "kendall") spearmanTest(x, y) } \keyword{htest} fBasics/man/stableSlider.Rd0000644000176200001440000000143114650724110015302 0ustar liggesusers\name{StableSlider} \alias{stableSlider} \title{Slider GUI for Stable Distribution} \description{ The \code{stableSlider()} function provides interactive displays of density and probabilities of stable distributions. } \usage{ stableSlider(col= "steelblue", col.med = "gray30") } \arguments{ \item{col}{colour for the density and distributions functions.} \item{col.med}{colour for the median.} } \value{ The \code{stableSlider()} function displays densities and probabilities of the skew stable distribution, for educational purposes. } \author{ Diethelm Wuertz for the Rmetrics \R-port. } \references{ see those in \code{\link[stabledist]{dstable}}, in package \pkg{stabledist}. } \examples{ if(dev.interactive()) stableSlider() } \keyword{distribution} fBasics/DESCRIPTION0000644000176200001440000000354614660613642013352 0ustar liggesusersPackage: fBasics Title: Rmetrics - Markets and Basic Statistics Version: 4041.97 Authors@R: c(person("Diethelm", "Wuertz", role="aut", comment = "original code") , person("Tobias", "Setz", role = c("aut"), email = "tobias.setz@live.com") , person("Yohan", "Chalabi", role = "aut") , person("Martin","Maechler", role="ctb", email="maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910")) , person("CRAN Team", role = "ctb") , person(given = c("Georgi", "N."), family = "Boshnakov", role = c("cre", "aut"), email = "georgi.boshnakov@manchester.ac.uk") ) Description: Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management. Depends: R (>= 2.15.1) Imports: timeDate, timeSeries (>= 4021.105), stats, grDevices, graphics, methods, utils, MASS, spatial, gss, stabledist Suggests: interp, RUnit, tcltk LazyData: yes License: GPL (>= 2) Encoding: UTF-8 URL: https://geobosh.github.io/fBasicsDoc/ (doc), https://r-forge.r-project.org/scm/viewvc.php/pkg/fBasics/?root=rmetrics (devel), https://www.rmetrics.org BugReports: https://r-forge.r-project.org/projects/rmetrics NeedsCompilation: yes Packaged: 2024-08-18 18:37:43 UTC; georgi Author: Diethelm Wuertz [aut] (original code), Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] (), CRAN Team [ctb], Georgi N. Boshnakov [cre, aut] Maintainer: Georgi N. Boshnakov Repository: CRAN Date/Publication: 2024-08-19 10:00:02 UTC