Perform principal component analysis
Perform PCA on a numeric matrix for visualisation, information extraction and missing value imputation.
pca(object, method, nPcs = 2, scale = c("none", "pareto", "vector",
"uv"), center = TRUE, completeObs = TRUE, subset = NULL,
cv = c("none", "q2"), ...)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 cross-validation to be performed. |
... |
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, 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.
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 the variance")) +
ylab(paste("PC2", pc@R2[2] * 100, "% of the variance"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.