princomp2: Principal Component Analysis for Wide Data

In zeehio/msProcess: Protein Mass Spectra Processing

Description

This function performs principal component analysis (PCA) for wide data x, i.e. dim(x)[1] < dim(x)[2]. This kind of data can be handled by princomp in S-PLUS but not in R. The trick is to do PCA for t(x) first and then convert back to the original space. It might be more efficient than princomp for high dimensional data.

Usage

princomp2(x, ...)

Arguments

x	a matrix.
...	not used.

Value

an object of class "princomp".

See Also

princomp.

Examples

d <- matrix(rnorm(12), nrow=3)
dimnames(d) <- list(paste("S", 1:3, sep=""), paste("V", 1:4, sep=""))
{d.pc <- princomp2(d)}
summary(d.pc)
screeplot(d.pc) 
biplot(d.pc)
predict(d.pc, d)
plot(loadings(d.pc)) 

zeehio/msProcess documentation built on May 4, 2019, 10:15 p.m.