StatDA: RobCor.plot – R documentation

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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, ...)

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

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

Statistical Analysis for Environmental Data

v1.7.4

GPL (>= 3)

Authors

Peter Filzmoser

Initial release

2020-03-10