a covariance matrix (from cov, covMcd, covPC,
covPCAgrid, covPCAproj, etc.
cov2
a covariance matrix (from cov, covMcd, covPC,
covPCAgrid, covPCAproj, etc.
method1
legend for ellipses of estimation method1
method2
legend for ellipses of estimation method2
labels1
legend for numbers of estimation method1
labels2
legend for numbers of estimation method2
ndigits
number of digits to use for printing covariances, by default
ndigits=4
...
additional arguments for text or plot
Details
Since (robust) PCA can be used to re-compute the (robust) covariance matrix,
one might be interested to compare two different methods of covariance
estimation visually. This routine takes as input objects for the covariances
to compare the output of cov, but also the return objects
from covPCAgrid, covPCAproj, covPC,
and covMcd. The comparison of the two covariance matrices
is done by numbers (the covariances) and by ellipses.
C. Croux, P. Filzmoser, M. Oliveira, (2007).
Algorithms for Projection-Pursuit Robust Principal Component Analysis,
Chemometrics and Intelligent Laboratory Systems, Vol. 87, pp. 218-225.
See Also
PCAgrid, PCAproj, princomp
Examples
# multivariate data with outliers
library(mvtnorm)
x <- rbind(rmvnorm(200, rep(0, 6), diag(c(5, rep(1,5)))),
rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6))))
plotcov(covPCAproj(x),covPCAgrid(x))