# 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

 ```1 2``` ```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

 ```1 2 3 4 5 6``` ```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.