PerformanceAnalytics (version 1.1.0)

chart.QQPlot: Plot a QQ chart

Description

Plot the return data against any theoretical distribution.

Usage

chart.QQPlot(R, distribution = "norm", ylab = NULL,
    xlab = paste(distribution, "Quantiles"), main = NULL,
    las = par("las"), envelope = FALSE, labels = FALSE,
    col = c(1, 4), lwd = 2, pch = 1, cex = 1,
    line = c("quartiles", "robust", "none"),
    element.color = "darkgray", cex.axis = 0.8,
    cex.legend = 0.8, cex.lab = 1, cex.main = 1,
    xaxis = TRUE, yaxis = TRUE, ylim = NULL, ...)

Arguments

R
an xts, vector, matrix, data frame, timeSeries or zoo object of asset returns
distribution
root name of comparison distribution - e.g., 'norm' for the normal distribution; 't' for the t-distribution. See examples for other ideas.
xlab
set the x-axis label, as in plot
ylab
set the y-axis label, as in plot
xaxis
if true, draws the x axis
yaxis
if true, draws the y axis
ylim
set the y-axis limits, same as in plot
main
set the chart title, same as in plot
las
set the direction of axis labels, same as in plot
envelope
confidence level for point-wise confidence envelope, or FALSE for no envelope.
labels
vector of point labels for interactive point identification, or FALSE for no labels.
col
color for points and lines; the default is the second entry in the current color palette (see 'palette' and 'par').
lwd
set the line width, as in plot
pch
symbols to use, see also plot
cex
symbols to use, see also plot
line
'quartiles' to pass a line through the quartile-pairs, or 'robust' for a robust-regression line; the latter uses the 'rlm' function in the 'MASS' package. Specifying 'line = "none"' suppresses the line.
element.color
provides the color for drawing chart elements, such as the box lines, axis lines, etc. Default is "darkgray"
cex.legend
The magnification to be used for sizing the legend relative to the current setting of 'cex'
cex.axis
The magnification to be used for axis annotation relative to the current setting of 'cex'
cex.lab
The magnification to be used for x- and y-axis labels relative to the current setting of 'cex'
cex.main
The magnification to be used for the main title relative to the current setting of 'cex'.
...
any other passthru parameters to the distribution function

Details

A Quantile-Quantile (QQ) plot is a scatter plot designed to compare the data to the theoretical distributions to visually determine if the observations are likely to have come from a known population. The empirical quantiles are plotted to the y-axis, and the x-axis contains the values of the theorical model. A 45-degree reference line is also plotted. If the empirical data come from the population with the choosen distribution, the points should fall approximately along this reference line. The larger the departure from the reference line, the greater the evidence that the data set have come from a population with a different distribution.

References

main code forked/borrowed/ported from the excellent: Fox, John (2007) car: Companion to Applied Regression http://www.r-project.org, http://socserv.socsci.mcmaster.ca/jfox/

See Also

qqplot qq.plot plot

Examples

Run this code
library(MASS)
data(managers)

x = checkData(managers[,2, drop = FALSE], na.rm = TRUE, method = "vector")

#layout(rbind(c(1,2),c(3,4)))

# Panel 1, Normal distribution
chart.QQPlot(x, main = "Normal Distribution", distribution = 'norm', envelope=0.95)
# Panel 2, Log-Normal distribution
fit = fitdistr(1+x, 'lognormal')
chart.QQPlot(1+x, main = "Log-Normal Distribution", envelope=0.95, distribution='lnorm')
#other options could include
#, meanlog = fit$estimate[[1]], sdlog = fit$estimate[[2]])

# Panel 3, Skew-T distribution
library(sn)
fit = st.mle(y=x)
chart.QQPlot(x, main = "Skew T Distribution", envelope=0.95,
             distribution = 'st', location = fit$dp[[1]],
             scale = fit$dp[[2]], shape = fit$dp[[3]], df=fit$dp[[4]])

#Panel 4: Stable Parietian
library(fBasics)
fit.stable = stableFit(x,doplot=FALSE)
chart.QQPlot(x, main = "Stable Paretian Distribution", envelope=0.95,
             distribution = 'stable', alpha = fit(stable.fit)$estimate[[1]],
             beta = fit(stable.fit)$estimate[[2]], gamma = fit(stable.fit)$estimate[[3]],
             delta = fit(stable.fit)$estimate[[4]], pm = 0)
#end examples

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