PMA-package {PMA}R Documentation

Penalized Multivariate Analysis

Description

This package is called PMA, for "Penalized Multivariate Analysis". It implements three methods: A penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlations analysis. All are described in the paper "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis", by D Witten, R Tibshirani, and T Hastie. The paper is available at <http://www-stat.stanford.edu/~dwitten>.

The main functions are as follows: (1) PMD (2) CCA (3) SPC

The first function, PMD, performs a penalized matrix decomposition. CCA performs sparse canonical correlation analysis. SPC performs sparse principal components analysis.

There also are cross-validation functions for tuning parameter selection for each of the above methods: SPC.cv, PMD.cv, CCA.permute. And PlotCGH results in nice plots for DNA copy number data.

Details

Package: PMA
Type: Package
Version: 1.0
Date: 2009-02-10
License: GPL >= 2

Author(s)

Daniela M. Witten and Robert Tibshirani

References

Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Submitted. <http://www-stat.stanford.edu/~dwitten>


[Package PMA version 1.0.1 Index]