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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
################################################################################
# FUNCTION: DESCRIPTION:
# assetsHistPlot Displays a histograms of a single asset
# assetsLogDensityPlot Displays a pdf plot on logarithmic scale
################################################################################
assetsHistPlot =
function(x, col = "steelblue", skipZeros = FALSE, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Displays a histograms of a single asset
# Arguments:
# x - a timeSeries object or any other rectangular object
# which can be transformed by the function as. matrix
# into a numeric matrix.
# Example:
# x = as.timeSeries(data(LPP2005REC))
# par(mfrow = c(3,3)); assetsHistPlot(x); par(mfrow = c(1,1))
# FUNCTION:
# Settings:
n = ncol(x)
if (length(col) == 1) col = rep(col, times = n)
# Plot:
for (i in 1:n) {
X = x[, i]
if (skipZeros) X = X[series(X) != 0]
histPlot(X, ylab = "Cumulated Returns", col = col[i], ...)
}
# Return Value:
invisible()
}
# ------------------------------------------------------------------------------
assetsLogDensityPlot =
function(x, estimator = c("hubers", "sample", "both"),
labels = TRUE, ...)
{
# A function implemented by Diethelm Wuertz
# Description:
# Displays a pdf plot on logarithmic scale
# Arguments:
# x - an uni- or multivariate return series of class 'timeSeries'
# or any other object which can be transformed by the function
# 'as.timeSeries()' into an object of class 'timeSeries'.
# estimator - the type of estimator to fit the mean and variance
# of the density.
# doplot - a logical flag, by default TRUE. Should a plot be
# displayed?
# labels - a logical flag, by default TRUE. Should a default main
# title and labels addet to the plot?
# ... -
# Details:
# Returns a pdf plot on a lin-log scale in comparison to a Gaussian
# density plot Two type of fits are available: a normal density with
# fitted sample mean and sample standard deviation, or a normal
# density with Hubers robust mean and standard deviation corfrected
# by the bandwidth of the Kernel estimator.
# Example:
# x = as.timeSeries(data(LPP2005REC))
# par(mfrow=c(3,3)); assetsLogDensityPlot(x, "hubers"); par(mfrow=c(1,1))
# par(mfrow=c(3,3)); assetsLogDensityPlot(x, "sample"); par(mfrow=c(1,1))
# par(mfrow=c(3,3)); assetsLogDensityPlot(x, "both"); par(mfrow=c(1,1))
# FUNCTION:
# Settings:
if (!is.timeSeries(x)) x = as.timeSeries(x)
Units = colnames(x)
doplot = TRUE
# Select Type:
estimator = match.arg(estimator)
# Labels:
if (labels) {
main = "log PDF"
xlab = "x"
ylab = "log PDF"
} else {
main = xlab = ylab = ""
}
X = x
for (i in 1:ncol(x)) {
# Transform Data:
x = as.vector(X[, i])
if (labels) main = Units[i]
# Kernel and Histogram Estimators:
Density = density(x)
Histogram = hist(x, breaks = "FD", plot = FALSE)
result = list(density = Density, hist = Histogram)
# Plot:
if (doplot) {
# Plot Frame:
plot(Histogram$mids, log(Histogram$density), type = "n",
lwd = 5, main = Units[i], xlab = xlab, ylab = ylab,
xlim = range(Density$x), ylim = log(range(Density$y)),
col = "red", ...)
# Plot Density:
points(Density$x, log(Density$y), pch = 19, col = "darkgrey",
cex = 0.7)
# Sample Line Fit:
s = seq(min(Density$x), max(Density$x), length = 1001)
if (estimator == "sample" || estimator == "both") {
lines(s, log(dnorm(s, mean(x), sd(x))), col = "red", lwd = 2)
}
# Robust Huber Line Fit:
if (estimator == "hubers" || estimator == "both") {
h = MASS::hubers(x)
logDensity = log(dnorm(s,
mean = h[[1]],
sd = sqrt(h[[2]]^2+Density$bw^2)))
minLogDensity = log(min(Density$y))
lines(
x = s[logDensity > minLogDensity],
y = logDensity[logDensity > minLogDensity],
col = "orange", lwd = 2)
}
# Plot Histogram:
points(Histogram$mids, log(Histogram$density), pch = 19,
col = "steelblue", ...)
# Grid:
if (labels) grid()
}
}
# Return Value:
invisible(result)
}
################################################################################
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