PMA-package: Penalized Multivariate Analysis

Description Details References

Description

This package is called PMA, for __P__enalized __M__ultivariate __A__nalysis. It implements three methods: A penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlations analysis. All are described in the reference below. The main functions are: PMD, CCA and SPC.

Details

The first, 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 produces nice plots for DNA copy number data.

References

Witten D. M., Tibshirani R., and Hastie, T. (2009) doi: 10.1093/biostatistics/kxp008.


PMA documentation built on March 26, 2020, 7:27 p.m.