QQplot function

QQplot with a Linear Fit

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,...)

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.

Returns

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)

References

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

Author(s)

Michael Thrun

Examples

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