chart.QQPlot: Plot a QQ chart in PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis (2024)

In PerformanceAnalytics: Econometric Tools for Performance and Risk Analysis

Description Usage Arguments Details Author(s) References See Also Examples

Description

Plot the return data against any theoretical distribution.

Usage

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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, distributionParameter = NULL, ...)

Arguments

`R`	an xts, vector, matrix, data frame, timeSeries or zoo object ofasset returns
`distribution`	root name of comparison distribution - e.g., 'norm' forthe normal distribution; 't' for the t-distribution. See examples for otherideas.
`ylab`	set the y-axis label, as in `plot`
`xlab`	set the x-axis label, 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, orFALSE 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 secondentry 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' functionin the 'MASS' package. Specifying 'line = "none"' suppresses the line.
`element.color`	provides the color for drawing chart elements, such asthe box lines, axis lines, etc. Default is "darkgray"
`cex.axis`	The magnification to be used for axis annotation relative tothe current setting of 'cex'
`cex.legend`	The magnification to be used for sizing the legendrelative to the current setting of 'cex'
`cex.lab`	The magnification to be used for x- and y-axis labelsrelative to the current setting of 'cex'
`cex.main`	The magnification to be used for the main title relative tothe current setting of 'cex'.
`xaxis`	if true, draws the x axis
`yaxis`	if true, draws the y axis
`ylim`	set the y-axis limits, same as in `plot`
`distributionParameter`	a string of the parameters of the distributione.g., distributionParameter = 'location = 1, scale = 2, shape = 3, df = 4' for skew-T distribution
`...`	any other passthru parameters to the distribution function

Details

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

Author(s)

John Fox, ported by Peter Carl

References

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

Examples

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library(MASS) library(PerformanceAnalytics)data(managers)x = checkData(managers[,2, drop = FALSE], na.rm = TRUE, method = "vector")# Panel 1: Normal distributionchart.QQPlot(x, main = "Normal Distribution",line=c("quartiles"), distribution = 'norm', envelope=0.95)# Panel 2, Log-Normal distributionfit = fitdistr(1+x, 'lognormal')chart.QQPlot(1+x, main = "Log-Normal Distribution", envelope=0.95, distribution='lnorm',distributionParameter='meanlog = fit$estimate[[1]],  sdlog = fit$estimate[[2]]')## Not run: # Panel 3: Mixture Normal distributionlibrary(nor1mix)obj = norMixEM(x,m=2)chart.QQPlot(x, main = "Normal Mixture Distribution",line=c("quartiles"), distribution = 'norMix', distributionParameter='obj',envelope=0.95)# Panel 4: Symmetric t distributionlibrary(sn)n = length(x)fit.tSN = st.mple(as.matrix(rep(1,n)),x,symmetr = TRUE)names(fit.tSN$dp) = c("location","scale","dof")round(fit.tSN$dp,3)chart.QQPlot(x, main = "MO Symmetric t-Distribution QQPlot",xlab = "quantilesSymmetricTdistEst",line = c("quartiles"),envelope = .95, distribution = 't', distributionParameter='df=fit.tSN$dp[3]',pch = 20)# Panel 5: Skewed t distributionfit.st = st.mple(as.matrix(rep(1,n)),x)# fit.st = st.mple(y=x) Produces same result as line abovenames(fit.st$dp) = c("location","scale","skew","dof")round(fit.st$dp,3)chart.QQPlot(x, main = "MO Returns Skewed t-Distribution QQPlot",xlab = "quantilesSkewedTdistEst",line = c("quartiles"),envelope = .95, distribution = 'st',distributionParameter = 'xi = fit.st$dp[1],omega = fit.st$dp[2],alpha = fit.st$dp[3],nu=fit.st$dp[4]',pch = 20)# Panel 6: Stable Parietianlibrary(fBasics)fit.stable = stableFit(x,doplot=FALSE)chart.QQPlot(x, main = "Stable Paretian Distribution", envelope=0.95, distribution = 'stable', distributionParameter = 'alpha = fit(stable.fit)$estimate[[1]],  beta = fit(stable.fit)$estimate[[2]],  gamma = fit(stable.fit)$estimate[[3]],  delta = fit(stable.fit)$estimate[[4]], pm = 0')## End(Not run)#end examples

PerformanceAnalytics documentation built on Feb. 6, 2020, 5:11 p.m.