RnipalsPca {pcaMethods} | R Documentation

## NIPALS PCA implemented in R

### Description

PCA by non-linear iterative partial least squares

### Usage

    RnipalsPca(Matrix, nPcs = 2, varLimit = 1, maxSteps = 5000,
      threshold = 1e-06, verbose = interactive(), ...)

### Arguments

`Matrix` | Pre-processed (centered, scaled) numerical matrix samples in rows and variables as columns.
`nPcs` | Number of components that should be extracted.
`varLimit` | Optionally the ratio of variance that should be explained. `nPcs` is ignored if varLimit < 1
`maxSteps` | Defines how many iterations can be done before algorithm should abort (happens almost exclusively when there were some wrong in the input data).
`threshold` | The limit condition for judging if the algorithm has converged or not, specifically if a new iteration is done if (T_{old} - T)^T(T_{old} - T) > \code{limit}.
`verbose` | Show simple progress information.
`...` | Only used for passing through arguments.

### Details

Can be used for computing PCA on a numeric matrix using either the NIPALS algorithm which is an iterative approach for estimating the principal components extracting them one at a time. NIPALS can handle a small amount of missing values. It is not recommended to use this function directely but rather to use the pca() wrapper function. There is a C++ implementation given as `nipalsPca` which is faster.

### Value

A `pcaRes` object.

### Author(s)

Henning Redestig

### References

Wold, H. (1966) Estimation of principal components and related models by iterative least squares. In Multivariate Analysis (Ed., P.R. Krishnaiah), Academic Press, NY, 391-420.

### See Also

`prcomp`, `princomp`, `pca`

### Examples

    data(metaboliteData)
    mat <- prep(t(metaboliteData))
    ## c++ version is faster
    system.time(pc <- RnipalsPca(mat, method="rnipals", nPcs=2))
    system.time(pc <- nipalsPca(mat, nPcs=2))
    ## better use pca()
    pc <- pca(t(metaboliteData), method="rnipals", nPcs=2)

[Package pcaMethods version 1.72.0 Index]