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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 EXPLORATIVE DATA ANALYSIS:
# emdPlot Creates an empirical distribution plot
# qqparetoPlot Creates exploratory QQ plot for EV analysis
# mePlot Creates a sample mean excess function plot
# mxfPlot Creates another view of a sample mean excess plot
# mrlPlot Returns a mean residual life plot with confidence levels
# recordsPlot Plots records development
# ssrecordsPlot Plots records development of data subsamples
# msratioPlot Plots ratio of maximums and sums
# sllnPlot Verifies Kolmogorov's Strong Law of large numbers
# lilPlot Verifies Hartman-Wintner's Law of the iterated logarithm
# xacfPlot Plots autocorrelations of exceedances
################################################################################
test.emd =
function()
{
# emdPlot - Creates an empirical distribution plot
# Artificial Data Set:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
x = rgpd(1000)
# Empirical distribution plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
emdPlot(x, plottype = "xy")
emdPlot(x, plottype = "x")
emdPlot(x, plottype = "y")
# emdPlot(x, plottype = " ") # CHECK !!!
# Artificial Data Set:
x = rt(1000, df = 3)
# Empirical distribution plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
emdPlot(x, plottype = "xy")
emdPlot(x, plottype = "x")
emdPlot(x, plottype = "y")
# emdPlot(x, plottype = " ") # CHECK !!!
# Artificial Data Set:
x = rnorm(1000)
# Empirical distribution plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
emdPlot(x, plottype = "xy")
emdPlot(x, plottype = "x")
emdPlot(x, plottype = "y")
# emdPlot(x, plottype = " ") # CHECK !!!
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.qqpareto =
function()
{
# qqparetoPlot - Creates exploratory QQ plot for EV analysis
# Artificial Data Set -
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
r0 = rgpd(n = 1000, xi = 0)
r1 = rgpd(n = 1000, xi = 1)
# Graph Frame:
par(ask = FALSE)
par(mfrow = c(2, 2))
# Empirical Pareto Distribution Plot:
qqparetoPlot(x = r0, xi = 0)
qqparetoPlot(x = r1, xi = 1)
# Empirical Normal Distribution Plot:
qqnormPlot(x = r0)
qqnormPlot(x = r1)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.me =
function()
{
# mePlot - Creates a sample mean excess function plot
# mxfPlot - Creates another view of a sample mean excess plot
# mrlPlot - Returns a mean residual life plot with confidence levels
# Artificial Data Set -
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
r = rgpd(n = 1000)
# Mean Excess Function Plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
mePlot(x = r) # Check, the largest point is missing ...
mxfPlot(x = r)
mrlPlot(x = r)
# No Labels:
par(mfrow = c(2, 2))
par(ask = FALSE)
mePlot(x = r, labels = FALSE)
mxfPlot(x = r, labels = FALSE)
mrlPlot(x = r, labels = FALSE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.records =
function()
{
# recordsPlot - Plots records development
# ssrecordsPlot - Plots records development of data subsamples
# Artificial Data Set -
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
r = rgpd(n = 1000)
# Records Plot:
par(mfrow = c(2, 2))
par(ask = FALSE)
recordsPlot(x = r)
recordsPlot(x = r, ci = 0.99)
ans = recordsPlot(x = r, labels = FALSE)
print(ans)
# Subrecords Plot:
set.seed(1985)
r = rgpd(n = 10000)
par(mfrow = c(2, 2))
par(ask = FALSE)
recordsPlot(r)
ssrecordsPlot(r, subsamples = 1)
ssrecordsPlot(r, subsamples = 1, plottype = "log")
ans = ssrecordsPlot(r, subsamples = 1, plottype = "lin")
print(ans)
# Subrecords Plot:
set.seed(1985)
r = rgpd(n = 10000)
par(mfrow = c(2, 2))
par(ask = FALSE)
ssrecordsPlot(r, subsamples = 10)
ssrecordsPlot(r, subsamples = 50)
ssrecordsPlot(r, subsamples = 10, plottype = "log")
ans = ssrecordsPlot(r, subsamples = 50, plottype = "log", labels = FALSE)
print(ans)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.msratio =
function()
{
# msratioPlot - Plots ratio of maximums and sums
# Artificial Data Set -
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
r = rgpd(n = 1000)
# Mean Excess Function Plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
msratioPlot(x = r, p = 1:4)
ans = msratioPlot(x = r, p = 1:4, labels = FALSE)
print(head(ans))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.laws =
function()
{
# sllnPlot - Verifies Kolmogorov's Strong Law of large numbers
# lilPlot - Verifies Hartman-Wintner's Law of the iterated logarithm
# Artificial Data Set -
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
r = rgpd(n = 1000)
# Strong Law of Large Numbers:
par(ask = FALSE)
par(mfrow = c(2, 2))
sllnPlot(x = r)
ans = sllnPlot(x = r, labels = FALSE)
print(ans)
# Law of the Iterated Logarithm:
lilPlot(x = r)
ans = lilPlot(x = r, labels = FALSE)
print(ans)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.xacf =
function()
{
# xacfPlot - Plots autocorrelations of exceedances
# Create an Artificial Data Set:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
r = rgpd(n = 1000)
# ACF of Exceedances Plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
ans = xacfPlot(x = r)
print(ans)
# ACF of Exceedances Plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
xacfPlot(x = r, labels = FALSE)
# ACF of Exceedances Plot:
par(ask = FALSE)
par(mfrow = c(2, 2))
xacfPlot(x = r, labels = FALSE, which = 1); title(main = "1")
xacfPlot(x = r, labels = FALSE, which = 2); title(main = "2")
xacfPlot(x = r, labels = FALSE, which = "3"); title(main = "3")
xacfPlot(x = r, labels = FALSE, which = "4"); title(main = "4")
# Return Value:
return()
}
################################################################################
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