RnipalsPca: NIPALS PCA implemented in R
In pcaMethods: A collection of PCA methods

Description Usage Arguments Details Value Author(s) References See Also Examples

PCA by non-linear iterative partial least squares

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

`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.

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.

A pcaRes object.

Henning Redestig

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.

prcomp, princomp, pca

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)