To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("pcaMethods")

In most cases, you don't need to download the package archive at all.

pcaMethods

   

This package is for version 3.2 of Bioconductor; for the stable, up-to-date release version, see pcaMethods.

A collection of PCA methods

Bioconductor version: 3.2

Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany.

Author: Wolfram Stacklies, Henning Redestig, Kevin Wright

Maintainer: Henning Redestig <henning.red at gmail.com>

Citation (from within R, enter citation("pcaMethods")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("pcaMethods")

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("pcaMethods")

 

PDF Data with outliers
PDF Introduction
PDF Missing value imputation
PDF   Reference Manual
Text   NEWS

Details

biocViews Bayesian, Software
Version 1.60.0
In Bioconductor since BioC 1.9 (R-2.4) (9.5 years)
License GPL (>= 3)
Depends Biobase, methods
Imports BiocGenerics, Rcpp (>= 0.11.3), MASS
LinkingTo Rcpp
Suggests matrixStats, lattice
SystemRequirements Rcpp
Enhances
URL
Depends On Me DeconRNASeq
Imports Me CompGO, DAPAR, metaX, MSnbase, SomaticSignatures
Suggests Me mtbls2
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source pcaMethods_1.60.0.tar.gz
Windows Binary pcaMethods_1.60.0.zip (32- & 64-bit)
Mac OS X 10.6 (Snow Leopard) pcaMethods_1.60.0.tgz
Mac OS X 10.9 (Mavericks) pcaMethods_1.60.0.tgz
Subversion source (username/password: readonly)
Git source https://github.com/Bioconductor-mirror/pcaMethods/tree/release-3.2
Package Short Url http://bioconductor.org/packages/pcaMethods/
Package Downloads Report Download Stats

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