QQnorm: QQ Plot with Correlation

Description

Creates QQ plot of complete or censored data, with summaries and test.

Usage

QQnorm(X, main="", ylab="", censor=0, winsor=0,
       joinem=FALSE, ylim=c(NA,NA), isBC=FALSE, is2pBC=FALSE,
       doplot=TRUE, showP=TRUE, fitline=TRUE, showsum=FALSE)

Value

A list containing the following components:

correl: the QQ correlation coefficient.
Pval: the P value of the QQ correlation coefficient.
mean: the mean of the data.
sd: the sd of the data.
intercept: the intercept of the QQ regression line, used in place of the mean when there is censoring or winsorization.
slope: the slope of the QQ regression line, used in place of the sd when there is censorin or winsorization.

Arguments

X: the numeric data vector to be plotted. Censored values should be reported as the censoring value.
main: the header text.
ylab: the name of the variable.
censor: optional (default of 0) - the number of data censored on the left.
winsor: optional (default of 0) - the number of data winsored in each tail.
joinem: optional (default of FALSE) - if TRUE the plot is drawn as a segmented line, if FALSE the individual points are plotted as x if winsorized, else *.
ylim: optional - y limits on the plot.
isBC: optional (default of FALSE) - if true, the data set is a Box-Cox transform of the original data.
is2pBC: optional (default of FALSE) - if true, the data set is a shifted Box-Cox tranform.
doplot: optional (default of TRUE) - if true, the QQ plot is drawn.
showP: optional (default of TRUE) - if true, the QQCC P value is shown on the plot.
fitline: optional (default of TRUE) - if true, the QQ regression line is plotted.
showsum: optional (default of FALSE) - if true, the intercept and slope of the QQ regression are shown.

Author

Douglas M. Hawkins, Jessica J. Kraker krakerjj@uwec.edu

Details

Makes QQ plot of complete or censored data, as input by user:

fits regression line to complete uncensored portion of data,
calculates the QQ correlation coefficient of the fitted line, and
reports the P value of the QQ correlation coefficient as calculated by BCR_pval. Parameters isBC and is2pBC are relevant to the P value calculation.

References

Hawkins DM, Esquivel RN (2024). A Quantile-Quantile Toolbox for Reference Intervals. The Journal of Applied Laboratory Medicine, 9:2, 357-370.

Examples

Run this code

# parameters
mu    <- 40
sigma <- 10
n     <- 120
# identifying winsoring
wins  <- trunc(n/40)
# replicable randomization
set.seed(1069)
X     <- mu + sigma*rnorm(n)
# replicable randomization with heavy tails
set.seed(1069)
HT    <- mu + sigma * rt(n, 5)

# retain original visual settings
oldsettings <- par(mfrow=par()$mfrow, oma=par()$oma)
# visual settings
par(mfrow=c(2,2))
par(oma=c(0,0,2,0))
# plot to compare
base <- QQnorm(X, main="Base normal", showsum=TRUE)
title("Illustrating QQnorm with para_limits", outer=TRUE)
basew <- QQnorm(X, main="Winsorized", winsor=wins, showsum=TRUE)
ht   <- QQnorm(HT, main="Heavy tail", showsum=TRUE)
htw  <- QQnorm(HT, main="Winsorized", winsor=wins, showsum=TRUE)
# restore
par(oldsettings)

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