PlotQQ: QQ-Plot for Any Distribution

Description

Create a QQ-plot for a variable of any distribution. The assumed underlying distribution can be defined as a function including all required parameters.

Usage

PlotQQ(x, qdist, main = NULL, xlab = NULL, ylab = NULL, add = FALSE,
       args.qqline = NULL, ...)

Arguments

the data sample

qdist

the quantile function of the assumed distribution. Can either be given as simple function name or defined as own function using the required arguments. See examples.

main

the main title for the plot. This will be "Q-Q-Plot" by default

xlab

the xlab for the plot

ylab

the ylab for the plot

add

logical specifying if the points should be added to an already existing plot; defaults to FALSE.

args.qqline

arguments for the qqline. This will be estimated as a line through the 25% and 75% quantiles, which is the same procedure as qqline does for normal distribution (instead of set it to abline(a = 0, b = 1)). The line defaults are set to col = par("f

...

the dots are passed to the plot function.

Details

The function generates a sequence of points between 0 and 1 and transforms those into quantiles by means of the defined assumed distribution.

References

Teetor, P. (2011) R Cookbook. O'Reilly, pp. 254-255.

Examples

Run this code

y <- rexp(100, 1/10)
PlotQQ(y, function(p) qexp(p, rate=1/10))

w <- rweibull(100, shape=2)
PlotQQ(w, qdist = function(p) qweibull(p, shape=4))

z <- rchisq(100, df=5)
PlotQQ(z, function(p) qchisq(p, df=5), args.qqline=list(col=2, probs=c(0.1,0.6)),
       main=expression("Q-Q plot for" ~~ {chi^2}[nu == 3]))
abline(0,1)

# add 20 random sets
for(i in 1:20){
  z <- rchisq(100, df=5)
  PlotQQ(z, function(p) qchisq(p, df=5), add=TRUE, args.qqline = NA,
         col="grey", lty="dotted")
}

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