covPCA {pcaPP} | R Documentation |
computes the robust covariance matrix using the PCAgrid
and
PCAproj
functions.
covPCAproj(x, control, ...) covPCAgrid(x, control, ...)
x |
a numeric matrix or data frame which provides the data. |
control |
a list whose elements must be the same as (or a subset of)
the parameters of the appropriate PCA function (PCAgrid or
PCAproj ). If the control object is supplied, other parameters
supposed to be passed to this function as part of the ... parameter
are overridden. |
... |
additional arguments passed to the function
PCAproj or PCAgrid respectively. |
The functions covPCAproj
and covPCAgrid
use the functions
PCAproj
and PCAgrid
respectively to estimate
the covariance matrix of the data matrix x
.
cov |
the actual covariance matrix estimated from x |
center |
the center of the data x that was substracted from them
before the PCA algorithms were run. |
method |
a string describing the method that was used to calculate the covariance matrix estimation |
Heinrich Fritz, Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
C. Croux, P. Filzmoser, M. Oliveira (2004) Projection-pursuit Estimators for Robust Principal Component Analysis, Technical Report TS-04-4, Vienna University of Technology, Austria
# multivariate data with outliers x <- rbind(rmvnorm(200, rep(0, 6), diag(c(5, rep(1,5)))), rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6)))) covPCAproj(x) # compare with classical covariance matrix: cov(x)