pcaMethods (version 1.64.0)

svdPca: Perform principal component analysis using singular value decomposition

Description

A wrapper function for prcomp to deliver the result as a pcaRes method. Supplied for compatibility with the rest of the pcaMethods package. It is not recommended to use this function directely but rather to use the pca() wrapper function.

Usage

svdPca(Matrix, nPcs = 2, varLimit = 1, verbose = interactive(), ...)

Arguments

Matrix

Pre-processed (centered and possibly scaled) numerical matrix samples in rows and variables as columns. No missing values allowed.

nPcs

Number of components that should be extracted.

varLimit

Optionally the ratio of variance that should be explained. nPcs is ignored if varLimit < 1

verbose

Verbose complaints to matrix structure

...

Only used for passing through arguments.

Value

A pcaRes object.

See Also

prcomp, princomp, pca

Examples

data(metaboliteDataComplete)
mat <- prep(t(metaboliteDataComplete))
pc <- svdPca(mat, nPcs=2)
## better use pca()
pc <- pca(t(metaboliteDataComplete), method="svd", nPcs=2)