pca {pcaMethods} | R Documentation |
Can be used for computing PCA on a numeric matrix for visualisation, information extraction and missing value imputation.
pca(object, method=c("svd", "nipals", "bpca", "ppca", "svdImpute", "nlpca", "robustPca"), subset=numeric(),...)
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. |
This method is wrapper function for the following set of pca methods:
prcomp
. See documentation for svdPca
.nipalsPca
.bpca
.ppca
.svdImpute
.Extra arguments usually given to this function include:
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.
Wolfram Stacklies, 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.
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.
prcomp
, princomp
,
nipalsPca
, svdPca
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]))