StatDA (version 1.7.11)

RobCor.plot: Compares the Robust Estimation with the Classical

Description

This function compares a robust covariance (correlation) estimation (MCD is used) with the classical approach. A plot with the two ellipses will be produced and the correlation coefficients are quoted.

Usage

RobCor.plot(x, y, quan = 1/2, alpha = 0.025, colC = 1, colR = 1, ltyC = 2,
ltyR = 1, ...)

Value

cor.cla

correlation of the classical estimation

cor.rob

correlation of the robust estimation

Arguments

x, y

two data vectors where the correlation should be computed

quan

fraction of tolerated outliers (at most 0.5)

alpha

quantile of chisquare distribution for outlier cutoff

colC, colR

colour for both ellipses

ltyC, ltyR

line type for both ellipses

...

other graphical parameters

Author

Peter Filzmoser <> http://cstat.tuwien.ac.at/filz/

Details

The covariance matrix is estimated in a robust (MCD) and non robust way and then both ellipses are plotted. The radi is calculated from the singular value decomposition and a breakpoint (specified quantile) for outlier cutoff.

References

C. Reimann, P. Filzmoser, R.G. Garrett, and R. Dutter: Statistical Data Analysis Explained. Applied Environmental Statistics with R. John Wiley and Sons, Chichester, 2008.

Examples

data(chorizon)
Be=chorizon[,"Be"]
Sr=chorizon[,"Sr"]
RobCor.plot(log10(Be),log10(Sr),xlab="Be in C-horizon [mg/kg]",
ylab="Sr in C-horizon [mg/kg]",cex.lab=1.2, pch=3, cex=0.7,
xaxt="n", yaxt="n",colC=1,colR=1,ltyC=2,ltyR=1)