Covariance Matrix Estimation from princomp Object
computes the covariance matrix from a princomp object. The number of components k can be given as input.
covPC(x, k, method)
x |
an object of class princomp. |
k |
number of PCs to use for covariance estimation (optional). |
method |
method how the PCs have been estimated (optional). |
There are several possibilities to estimate the principal components (PCs)
from an input data matrix, including the functions PCAproj and
PCAgrid. This function uses the estimated PCs to reconstruct
the covariance matrix. Not all PCs have to be used, the number k of
PCs (first k PCs) can be given as input to the function.
cov |
the estimated covariance matrix |
center |
the center of the data, as provided from the princomp object. |
method |
a string describing the method that was used to calculate the PCs. |
Heinrich Fritz, Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
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.
# 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))))
pc <- princomp(x)
covPC(pc, k=2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.