svdPca {pcaMethods} | R Documentation

## 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

### 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)

[Package pcaMethods version 1.76.0 Index]