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 cross-validation 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, 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
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 the variance")) +
ylab(paste("PC2", pc@R2[2] * 100, "% of the 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, basename, cbind, colMeans, colSums, colnames,
dirname, 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
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