| 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> 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)