pcaMethods: svdPca – R documentation

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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.

Author(s)

Henning Redestig

Examples

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

pcaMethods

A collection of PCA methods

v1.82.0

GPL (>= 3)

Authors

Wolfram Stacklies, Henning Redestig, Kevin Wright

Initial release