RobCor.plot: Compares the Robust Estimation with the Classical

In StatDA: Statistical Analysis for Environmental Data

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, ...)

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

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.

Value

cor.cla	correlation of the classical estimation
cor.rob	correlation of the robust estimation

Author(s)

Peter Filzmoser <[e-mail]> http://cstat.tuwien.ac.at/filz/

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)

StatDA documentation built on March 13, 2020, 2:42 a.m.