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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 - 2007, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# 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: GPD SIMULATION:
# gpdSim Simulates a GPD distributed process
# FUNCTION: GPD PARAMETER ESTIMATION:
# 'fGPDFIT' S4 class representation
# gpdFit Fits Parameters of GPD distribution
# METHODS: PRINT, PLOT, AND SUMMARY:
# show.fGPDFIT S4 Print Method for object of class "fGPDFIT"
# plot.fGPDFIT S3 Plot Method for object of class "fGPDFIT"
# summary.fGPDFIT S3 Summary Method for object of class "fGPDFIT"
################################################################################
test.gpdSim =
function()
{
# Generate Artificial Data Set:
x = gpdSim(model = list(xi = 0.25, mu = 0, beta = 1), n = 1000, seed = 4711)
class(x)
# Plot Series:
par(mfrow = c(2, 1), cex = 0.7)
par(ask = FALSE)
seriesPlot(as.timeSeries(x))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.fGPDFIT =
function()
{
# Slot names:
slotNames("fGPDFIT")
# [1] "call" "method" "parameter" "data" "fit"
# [6] "residuals" "title" "description"
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.gpdFit =
function()
{
# Generate Artificial Data Set:
model = list(xi = -0.25, mu = 0, beta = 1)
ts = gpdSim(model = model, n = 5000, seed = 4711)
class(ts)
# Transform As timeSeries:
tS = as.timeSeries(ts)
class(tS)
# As numeric vector:
x = as.vector(ts)
class(x)
# GPD Fit:
# gpdFit(x, u = quantile(x, 0.95), type = c("mle", "pwm"),
# information = c("observed", "expected"), title = NULL,
# description = NULL, ...)
# PWM Fit:
fit = gpdFit(tS, u = min(series(tS)), "pwm")
print(fit)
fit = gpdFit(ts, u = min(ts), "pwm")
print(fit)
fit = gpdFit(x, u = min(x), "pwm")
print(fit)
# MLE Fit:
fit = gpdFit(tS, u = min(series(tS)), "mle")
print(fit)
fit = gpdFit(ts, u = min(ts), "mle")
print(fit)
fit = gpdFit(x, u = min(x), "mle")
print(fit)
# Information:
fit = gpdFit(tS, u = min(series(tS)), type = "mle", information = "observed")
print(fit)
fit = gpdFit(tS, u = min(series(tS)), type = "mle", information = "expected")
print(fit)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.plot =
function()
{
# Artificial Data Set:
model = list(xi = -0.25, mu = 0, beta = 1)
ts = gpdSim(model = model, n = 5000, seed = 4711)
class(ts)
# Fit:
fit = gpdFit(ts, u = min(ts), type = "mle")
print(fit)
par(mfrow = c(2, 2), cex = 0.7)
par(ask = FALSE)
plot(fit, which = "all")
# Try:
# plot(fit, which = "ask")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.summary =
function()
{
# Artificial Data Set:
model = list(xi = -0.25, mu = 0, beta = 1)
ts = gpdSim(model = model, n = 5000, seed = 4711)
class(ts)
# Fit:
fit = gpdFit(ts, u = min(ts), type = "mle")
summary(fit, doplot = FALSE)
# Return Value:
return()
}
################################################################################
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