pca {pcaMethods}R Documentation

Perform principal component analysis

Description

Can be used for computing PCA on a numeric matrix for visualisation, information extraction and missing value imputation.

Usage

pca(object, method=c("svd", "nipals", "bpca", "ppca",
"svdImpute", "nlpca", "robustPca"), subset=numeric(),...)

Arguments

object Numerical matrix with (or an object coercible to such) with samples in rows and variables as columns. Also takes ExpressionSet in which case the transposed expression matrix is used.
subset For convenience one can pass a large matrix but only use the variable specified as subset. Can be colnames or indices.
method One of "svd", "nipals", "bpca", "nlpca" or "ppca".
... Further arguments to the chosen pca method.

Details

This method is wrapper function for the following set of pca methods:

svd:
Uses classical prcomp. See documentation for svdPca.
nipals:
An iterative method capable of handling small amounts of missing values. See documentation for nipalsPca.
bpca:
An iterative method using a Bayesian model to handle missing values. See documentation for bpca.
ppca:
An iterative method using a probabilistic model to handle missing values. See documentation for ppca.
svdImpute:
Uses expectation maximation to perform SVD PCA on incomplete data. See documentation for svdImpute.

Extra arguments usually given to this function include:

nPcs:
The amount of principal components to extract

Value

A pcaRes object. Or a list containing a pcaRes object as first and an ExpressionSet object as second entry if the input was of type ExpressionSet.

Author(s)

Wolfram Stacklies, 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.

Shigeyuki Oba, Masa-aki Sato, Ichiro Takemasa, Morito Monden, Ken-ichi Matsubara and Shin Ishii. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 19(16):2088-2096, Nov 2003.

Troyanskaya O. and Cantor M. and Sherlock G. and Brown P. and Hastie T. and Tibshirani R. and Botstein D. and Altman RB. - Missing value estimation methods for DNA microarrays. Bioinformatics. 2001 Jun;17(6):520-5.

See Also

prcomp, princomp, nipalsPca, svdPca

Examples

data(iris)
## Usually some kind of scaling is appropriate
pcIr <- pca(iris[,1:4], nPcs = 2, method="nipals")
pcIr <- pca(iris[,1:4], nPcs = 2, method="svd")
## Get a short summary on the calculated model
summary(pcIr)
## Scores and loadings plot
slplot(pcIr, sl=as.character(iris[,5]))

[Package pcaMethods version 1.16.0 Index]