Description Usage Arguments Details Value Author(s) References See Also Examples
Perform PCA on a numeric matrix for visualisation, information extraction and missing value imputation.
1 2 3 
object 
Numerical matrix with (or an object coercible to
such) with samples in rows and variables as columns. Also takes

method 
One of the methods reported by

nPcs 
Number of principal components to calculate. 
scale 
Scaling, see 
center 
Centering, see 
completeObs 
Sets the 
subset 
A subset of variables to use for calculating the model. Can be column names or indices. 
cv 
character naming a the type of crossvalidation to be performed. 
... 
Arguments to 
This method is wrapper function for the following set of pca methods:
Uses classical prcomp
. See
documentation for svdPca
.
An iterative method capable of handling small
amounts of missing values. See documentation for
nipalsPca
.
Same as nipals but implemented in R.
An iterative method using a Bayesian model to handle
missing values. See documentation for bpca
.
An iterative method using a probabilistic model to
handle missing values. See documentation for ppca
.
Uses expectation maximation to perform SVD PCA
on incomplete data. See documentation for
svdImpute
.
Scaling and centering is part of the PCA model and handled by
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, 391420.
Shigeyuki Oba, Masaaki Sato, Ichiro Takemasa, Morito Monden, Kenichi Matsubara and Shin Ishii. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 19(16):20882096, 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):5205.
prcomp
, princomp
,
nipalsPca
, svdPca
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  data(iris)
## Usually some kind of scaling is appropriate
pcIr < pca(iris, method="svd", nPcs=2)
pcIr < pca(iris, method="nipals", nPcs=3, cv="q2")
## Get a short summary on the calculated model
summary(pcIr)
plot(pcIr)
## Scores and loadings plot
slplot(pcIr, sl=as.character(iris[,5]))
## use an expressionset and ggplot
data(sample.ExpressionSet)
pc < pca(sample.ExpressionSet)
df < merge(scores(pc), pData(sample.ExpressionSet), by=0)
library(ggplot2)
ggplot(df, aes(PC1, PC2, shape=sex, color=type)) +
geom_point() +
xlab(paste("PC1", pc@R2[1] * 100, "% of variance")) +
ylab(paste("PC2", pc@R2[2] * 100, "% of variance"))

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colMeans, colSums, colnames, do.call,
duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: 'pcaMethods'
The following object is masked from 'package:stats':
loadings
nipals calculated PCA
Importance of component(s):
PC1 PC2 PC3
R2 0.9246 0.05307 0.0171
Cumulative R2 0.9246 0.97769 0.9948
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.