Qantile-quantile plot with a linear fit
QQplot(X,Y,Type=8,NoQuantiles=10000,xlab, ylab,col="red",main='',
lwd=3,pch=20,subplot=FALSE,...)List with
[1:NoQuantiles,1:2] quantiles in y and y
Output of the Regression with residuals.lm(line)
Output of the Regression with summaryline)
Output of the Regression with anova(line)
[1:n] numerical vector, First Feature
1:n] numerical vector, Second Feature to compare first feature with
an integer between 1 and 9 selecting one of the nine quantile algorithms detailed in quantile
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
x label, see plot
...
y label, see plot
color of line, see plot
title of plot, see plot
line width of plot, see plot
type of point, see plot
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
Michael Thrun
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.
Michael, J. R.: The stabilized probability plot, Biometrika, Vol. 70(1), pp. 11-17, 1983.
data(MTY)
NormalDistribution=rnorm(50000)
QQplot(NormalDistribution,MTY)
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