PlotQQ {DescTools} | R Documentation |
Create a QQ-plot for a variable of any distribution. The assumed underlying distribution can be defined as a function of f(p), including all required parameters. Confidence bands are provided by default.
PlotQQ(x, qdist=qnorm, main = NULL, xlab = NULL, ylab = NULL, datax = FALSE, add = FALSE,
args.qqline = NULL, conf.level = 0.95, args.cband = NULL, ...)
x |
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. Default is |
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 |
datax |
logical. Should data values be on the x-axis? Default is |
add |
logical specifying if the points should be added to an already existing plot; defaults to |
args.qqline |
arguments for the qqline. This will be estimated
as a line through the 25% and 75% quantiles by default, which is the same procedure as |
conf.level |
confidence level for the confidence interval. Set this to |
args.cband |
list of arguments for the confidence band, such as color or border (see |
... |
the dots are passed to the plot function. |
The function generates a sequence of points between 0 and 1 and transforms those into quantiles by means of the defined assumed distribution.
The code is inspired by the tip 10.22 "Creating other Quantile-Quantile plots" from R Cookbook and based on R-Core code from the function qqline
. The calculation of confidence bands are rewritten based on an algorithm published in the package BoutrosLab.plotting.general
.
Andri Signorell <andri@signorell.net>, Ying Wu <Ying.Wu@stevens.edu>
Teetor, P. (2011) R Cookbook. O'Reilly, pp. 254-255.
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 5 random sets
for(i in 1:5){
z <- rchisq(100, df=5)
PlotQQ(z, function(p) qchisq(p, df=5), add=TRUE, args.qqline = NA,
col="grey", lty="dotted")
}