QQplot: QQplot with a Linear Fit

In Mthrun/DataVisualizations: Visualizations of High-Dimensional Data

QQplot with a Linear Fit

Description

Qantile-quantile plot with a linear fit

Usage

QQplot(X,Y,Type=8,NoQuantiles=10000,xlab, ylab,col="red",main='',
lwd=3,pch=20,subplot=FALSE,...)

Arguments

X	[1:n] numerical vector, First Feature
Y	1:n] numerical vector, Second Feature to compare first feature with
Type	an integer between 1 and 9 selecting one of the nine quantile algorithms detailed in quantile
NoQuantiles	number of quantiles used in QQ-plot, if number is low and the data has outliers, there may be empty space visible in the plot
xlab	x label, see plot ...
ylab	y label, see plot
col	color of line, see plot
main	title of plot, see plot
lwd	line width of plot, see plot
pch	type of point, see plot
subplot	FALSE: par is set specifically, TRUE: assumption is the usage as a subfigure, par has to be set by the user, no checks are performed, labels have to be set by the user
...	other parameters for qqplot

Details

Output is the evaluation of a linear (regression) fit of lm called 'line' and a quantile quantile plot (QQplot). Per default 10.000 quantiles are chosen, but in the case of very large data vectors one can reduce the quantiles for faster computation. The 100 percentiles used for the regression line are of darker blue than the quantiles chosen by the user.

Value

List with

Quantiles	[1:NoQuantiles,1:2] quantiles in y and y
Residuals	Output of the Regression with residuals.lm(line)
Summary	Output of the Regression with summaryline)
Anova	Output of the Regression with anova(line)

Author(s)

Michael Thrun

References

Michael, J. R.: The stabilized probability plot, Biometrika, Vol. 70(1), pp. 11-17, 1983.

Examples

data(MTY)
NormalDistribution=rnorm(50000)
QQplot(NormalDistribution,MTY)

Mthrun/DataVisualizations documentation built on Jan. 16, 2024, 1:01 a.m.