PMA-package

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, and published in Biostatistics (2009). The paper is available at <http: www-stat.stanford.edu="" ~dwitten="">.The main functions are as follows:
(1) PMD
(2) CCA
(3) SPCThe 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.</http:>

package

Performs Penalized Multivariate Analysis: a penalized
        matrix decomposition, sparse principal components analysis, and
        sparse canonical correlation analysis, described in the
        following papers: (1) Witten, Tibshirani and Hastie (2009) A
        penalized matrix decomposition, with applications to sparse
        principal components and canonical correlation analysis.
        Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009)
        Extensions of sparse canonical correlation analysis, with
        applications to genomic data. Statistical Applications in
        Genetics and Molecular Biology 8(1): Article 28.

PMA-package: Penalized Multivariate Analysis

Description

Arguments

Details

References