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PMA (version 1.0.4)

Penalized Multivariate Analysis

Description

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.

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Version

Install

install.packages('PMA')

Monthly Downloads

1,254

Version

1.0.4

License

GPL (>= 2)

Maintainer

Daniela M Witten

Last Published

October 23rd, 2009

Functions in PMA (1.0.4)

SPC

Perform sparse principal component analysis
MultiCCA.permute

Select tuning parameters for sparse multiple canonical correlation analysis using the penalized matrix decomposition.
CCA

Perform sparse canonical correlation analysis using the penalized matrix decomposition.
PMD

Get a penalized matrix decomposition for a data matrix.
breastdata

Breast cancer gene expression + DNA copy number data set from Chin et al (2006), Cancer Cell
SPC.cv

Perform cross-validation on sparse principal component analysis
PMA-package

Penalized Multivariate Analysis
CCA.permute

Select tuning parameters for sparse canonical correlation analysis using the penalized matrix decomposition.
PlotCGH

Plot CGH data
MultiCCA

Perform sparse multiple canonical correlation analysis.
PMD.cv

Do tuning parameter selection for PMD via cross-validation
PMA-internal

Internal PMA functions