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# 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 <wuertz@itp.phys.ethz.ch>
# 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
################################################################################
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
# ... - the distributional parameters
# 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)
}
# ------------------------------------------------------------------------------
.distCheck <- distCheck
# Keep for older Rmetrics Versions
################################################################################
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