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"), subset=numeric(),...)
object |
Numerical matrix with (or an object coercible to such) with
samples in rows and variables as columns. Also takes exprSet in
which case the transposed exprs slot 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" or "ppca". |
... |
Further arguments to the chosen pca method and to prep . |
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:
prep
).
A pcaRes
object.
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], scale="UV", method="nipals") pcIr <- pca(iris[,1:4], scale="UV", method="svd") ## Get a short summary on the calculated model summary(pcIr) ## Scores and loadings plot slplot(pcIr, sl=as.character(iris[,5]))