computeCombPCA: Combined PCA for training and test sample
In CosmoPhotoz: Photometric redshift estimation using generalized linear models

Description Usage Arguments Details Value Author(s) Examples

computeCombPCA computes combined PCA projections of the training and test samples.

1	computeCombPCA(x, y, robust)

`x`	a matrix or a data.frame
`y`	a matrix or a data.frame
`robust`	a boolean indicating if robust PCA should be used or not

The program is a simple alteration of PCAgrid() that computes a desired number of robust principal components using the grid search algorithm in the plane.

PCA projections for each matrix

Rafael S. de Souza, Alberto Krone-Martins

#Multivariate data with outliers
library(mvtnorm)
x <- rbind(rmvnorm(100, rep(0, 6), diag(c(5, rep(1,5)))),
          rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6))))
y <- rbind(rmvnorm(100, rep(0, 6), diag(c(5, rep(1,5)))),
          rmvnorm( 15, c(0, rep(20, 5)), diag(rep(1, 6))))
#Here we calculate the principal components
pc <- computeCombPCA(x, y)