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PMA (version 1.1)

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

1.1

License

GPL (>= 2)

Maintainer

ORPHANED

Last Published

February 5th, 2019

Functions in PMA (1.1)

CCA

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

Breast cancer gene expression + DNA copy number data set from Chin et al (2006), Cancer Cell
PMA-package

Penalized Multivariate Analysis
MultiCCA

Perform sparse multiple canonical correlation analysis.
MultiCCA.permute

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

Get a penalized matrix decomposition for a data matrix.
PMD.cv

Do tuning parameter selection for PMD via cross-validation
PlotCGH

Plot CGH data
SPC

Perform sparse principal component analysis
PMA-internal

Internal PMA functions
SPC.cv

Perform cross-validation on sparse principal component analysis
CCA.permute

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