AdaptGauss (version 1.5.4)

QQplotGMM: Quantile Quantile Plot of Data

Description

Quantile Quantile plot of data against gaussian distribution mixture model with optional best-fit-line

Usage

QQplotGMM(Data,Means,SDs,Weights,IsLogDistribution,Line,
PlotSymbol,xug,xog,LineWidth,PointWidth, ylab,main, ...)

Arguments

Data

vector (1:N) of data points

Means

vector[1:L] of Means of Gaussians (of GMM),L == Number of Gaussians

SDs

vector of standard deviations, estimated Gaussian Kernels, has to be the same length as Means

Weights

vector of relative number of points in Gaussians (prior probabilities), has to be the same length as Means

IsLogDistribution

Optional, ==1 if distribution(i) is a LogNormal, default Zeros of Length L

Line

Optional, Default: TRUE=Regression Line is drawn

xug

Optional, lower limit of the interval [xug, xog], in which a line will be interpolated

xog

Optional, upper limit of the interval [xug, xog], in which a line will be interpolated

PlotSymbol

Optional, plot symbol. Default is 20.

LineWidth

Optional, width of regression line, if Line==TRUE

PointWidth

Optional, width of points

ylab

Optional, see plot

main

Optional, see plot

...

Note: xlab cannot be changed, other parameters see qqplot

Value

List with

x

The x coordinates of the points that were plotted

y

The original data vector, i.e., the corresponding y coordinates

Details

Only verified for a Gaussian Mixture Model, usage of IsLogDistribution for LogNormal Modes is experimental!

References

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

See Also

qqplot

Examples

# NOT RUN {
data=c(rnorm(1000),rnorm(2000)+2,rnorm(1000)*2-1)
QQplotGMM(data,c(-1,0,2),c(2,1,1),c(0.25,0.25,0.5))

# }