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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: ADDITIONAL PLOTS:
# gpdTailPlot Plots Tail Estimate From GPD Model
# gpdQuantPlot Plots of GPD Tail Estimate of a High Quantile
# gpdShapePlot Plots for GPD Shape Parameter
# gpdQPlot Adds Quantile Estimates to plot.gpd
# gpdSfallPlot Adds Expected Shortfall Estimates to a GPD Plot
# gpdRiskMeasures Calculates Quantiles and Expected Shortfalls
# FUNCTION: NEW STYLE FUNCTIONS:
# tailPlot Plots GPD VaR and Expected Shortfall risk
# tailSlider Interactive view to find proper threshold value
# tailRiskMeasures Calculates VaR and Expected Shortfall risks
################################################################################
test.gpdTailPlot =
function()
{
# Artificial Data Set:
x = gpdSim(seed = 1985)
fit = gpdFit(x)
par(mfrow = c(1, 1))
par(ask = FALSE)
gpdTailPlot(fit)
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
fit = gpdFit(x)
par(mfrow = c(1, 1))
par(ask = FALSE)
gpdTailPlot(fit)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.gpdQuantPlot =
function()
{
# Artificial Data Set:
x = gpdSim(seed = 1985)
par(mfrow = c(1, 1))
par(ask = FALSE)
gpdQuantPlot(x)
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
fit = gpdFit(x)
par(mfrow = c(1, 1))
par(ask = FALSE)
gpdQuantPlot(x)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.gpdShapePlot =
function()
{
# Artificial Data Set:
x = gpdSim(seed = 1985)
par(mfrow = c(1, 1))
par(ask = FALSE)
gpdShapePlot(x)
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
par(mfrow = c(1, 1))
par(ask = FALSE)
gpdShapePlot(x)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.gpdQPlot =
function()
{
# Artificial Data Set:
x = gpdSim(seed = 1985)
fit = gpdFit(x)
tp = gpdTailPlot(fit)
gpdQPlot(tp)
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
fit = gpdFit(x, u =10)
tp = gpdTailPlot(fit)
gpdQPlot(tp)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.gpdSfallPlot =
function()
{
# Artificial Data Set:
x = gpdSim(seed = 1985)
fit = gpdFit(x)
### tp = gpdTailPlot(fit) # CHECK
### gpdSfallPlot(tp) # CHECK
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
fit = gpdFit(as.vector(x), u =10)
### tp = gpdTailPlot(fit) # CHECK
### gpdSfallPlot(tp) # CHECK
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.tailPlot =
function()
{
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
fit = gpdFit(x, u = 10)
### tailPlot(fit) # CHECK
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.tailSlider =
function()
{
# Danish Fire Claims:
# x = as.timeSeries(data(danishClaims))
# tailSlider(x)
NA
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.tailRisk =
function()
{
# Danish Fire Claims:
x = as.timeSeries(data(danishClaims))
fit = gpdFit(x, u = 10)
tailRisk(fit)
# Return Value:
return()
}
################################################################################
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