RobCor.plot: Compares the Robust Estimation with the Classical

Description Usage Arguments Details Value Author(s) References Examples

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

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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 <P.Filzmoser@tuwien.ac.at> 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

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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.

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