RobCor.plot {StatDA} | R Documentation

## 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>

### References

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

### 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)

[Package StatDA version 1.0 Index]