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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 - 2006, 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: DESCRIPTION:
# acfPlot Displays autocorrelations function plot
# pacfPlot Displays partial autocorrelation function plot
# teffectPlot Estimates and plots the Taylor effect
# lmacfPlot Estimates and plots the long memory ACF
# lacfPlot Plots lagged autocorrelations
# .logpdfPlot Returns a pdf plot on logarithmic scale(s)
# .qqgaussPlot Returns a Gaussian quantile-quantile plot
# scalinglawPlot Evaluates and plots scaling law behavior
################################################################################
test.acfPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# acfPlot -
acfPlot(x = msft.ret)
# acfPlot -
acfPlot(x = msft.ret, labels = FALSE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.pacfPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# pacfPlot -
pacfPlot(x = msft.ret)
# pacfPlot -
pacfPlot(x = msft.ret, labels = FALSE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.teffectPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# teffectPlot -
teffectPlot(x = msft.ret)
# teffectPlot -
teffectPlot(x = msft.ret, labels = FALSE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.lmacfPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# lmacfPlot -
## lmacfPlot(x = abs(msft.ret), type = "acf")
## lmacfPlot(x = abs(msft.ret), type = "hurst")
# ... CHECK ACF OF RETURNS
# lmacfPlot -
## lacfPlot(x = msft, n = 4, type = "values") ## CHECK !!!
# lmacfPlot -
## lmacfPlot(x = abs(msft.ret), type = "acf", labels = FALSE)
## lmacfPlot(x = abs(msft.ret), type = "hurst", labels = FALSE)
# ... CHECK ACF OF RETURNS
# lmacfPlot -
## lacfPlot(x = msft, n = 4, labels = FALSE, type = "values") ## CHECK !!!
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.lacfPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.logpdfPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# logpdfPlot -
fBasics:::.logpdfPlot(x = msft.ret, labels = FALSE)
fBasics:::.logpdfPlot(x = msft.ret, type = "log-log")
# ... CHECK WARNINGS
# ... CHECK COLORS
# logpdfPlot -
fBasics:::.logpdfPlot(x = msft.ret, labels = FALSE)
fBasics:::.logpdfPlot(x = msft.ret, type = "log-log", labels = FALSE)
# ... CHECK WARNINGS
# ... CHECK COLORS
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.qqgausPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# qqgaussPlot -
fBasics:::.qqgaussPlot(x = msft.ret)
# qqgaussPlot -
fBasics:::.qqgaussPlot(x = msft.ret, labels = FALSE)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.scalinglawPlot =
function()
{
# MSFT Data:
data("MSFT", package = "timeSeries", envir = environment())
msft.dat = MSFT
msft = msft.dat[, 1]
msft.vol = msft.dat[ , 5]/10^6
msft.ret = timeSeries::returns(msft)
# Graph Frame:
par(mfrow = c(1, 1))
# scalinglawPlot -
scalinglawPlot(x = msft.ret, span = 4)
# ... CHECK COLORS
# scalinglawPlot -
scalinglawPlot(x = msft.ret, span = 4, labels = FALSE)
# ... CHECK COLORS
# Return Value:
return()
}
################################################################################
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