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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 DATA PREPROCESSING:
# blockMaxima Returns block maxima from a time series
# findThreshold Upper threshold for a given number of extremes
# pointProcess Returns peaks over a threshold from a time series
# deCluster Declusters a point process
################################################################################
test.blockMaxima =
function()
{
# blockMaxima - Returns block maxima from a time series
# blockMaxima(x, block = c("monthly", "quarterly"), doplot = FALSE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Monthly Block Maxima:
ans = blockMaxima(x.ret, block = "monthly", doplot = TRUE)
print(ans)
# Quarterly Block Maxima:
ans = blockMaxima(x.ret, block = "q", doplot = TRUE)
print(ans)
# 20-Days Block Maxima:
ans = blockMaxima(x.ret, block = 20, doplot = TRUE)
print(ans)
# Numerical Data Vector:
x.ret = as.vector(x.ret)
head(x.ret)
ans = blockMaxima(x.ret, block = 20, doplot = TRUE)
print(ans)
# Stops by stopifnot() - Check:
# blockMaxima(x.ret, block = "month", doplot = TRUE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.findThreshold =
function()
{
# findThreshold - Upper threshold for a given number of extremes
# findThreshold(x, n = floor(0.05*length(as.vector(x))), doplot = FALSE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Find 99% Threshold:
par(mfrow = c(2, 2), cex = 0.7)
par(ask = FALSE)
findThreshold(x.ret, n = floor(0.01*length(as.vector(x))), doplot = TRUE)
# Remark - Alternative use ...
quantile(x.ret, probs = 1 - 0.01)
quantile(x.ret, probs = 1 - 0.01, type = 1)
# Find 95% Threshold:
findThreshold(x.ret, doplot = TRUE)
# Find 90% Threshold:
findThreshold(x.ret, n = floor(0.1*length(as.vector(x))), doplot = TRUE)
# Try if x is a numeric vector:
findThreshold(as.vector(x.ret), doplot = TRUE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.pointProcess =
function()
{
# pointProcess - Returns peaks over a threshold from a time series
# pointProcess(x, u = quantile(x, 0.95), doplot = FALSE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Plot Series:
par(mfrow = c(2, 1), cex = 0.7)
par(ask = FALSE)
# plot(x.ret, type = "l", main = "Series")
# abline(h = 0, col = "red", lty = 3)
# or use ...
seriesPlot(x.ret)
# Point Process:
pp = pointProcess(x.ret, u = quantile(x.ret, 0.8))
pp
plot(pp, type = "b", main = "Point Process")
abline(h = 0, col = "red", lty = 3)
# Try seriesPlot(pp)
# ... add points in graph
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.deCluster =
function()
{
# deCluster - Declusters a point process
# deCluster(x, run = 20, doplot = TRUE)
# Time Series Data:
x = MSFT[, "Close"]
x.ret = 100*returns(x)
head(x.ret)
class(x.ret)
# Decluster Time Series:
tS = deCluster(x = x.ret, run = 3)
print(tS)
# Return Value:
return()
}
################################################################################
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