PMA-package: Penalized Multivariate Analysis

In PMA: Penalized Multivariate Analysis

Penalized Multivariate Analysis

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.

Author(s)

Maintainer: Balasubramanian Narasimhan [e-mail]

Authors:

• Daniela Witten

• Rob Tibshirani [e-mail]

• Sam Gross

References

Witten D. M., Tibshirani R., and Hastie, T. (2009) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biostatistics/kxp008")}.

See Also

Useful links:

• https://github.com/bnaras/PMA

• Report bugs at https://github.com/bnaras/PMA/issues

PMA documentation built on Sept. 11, 2024, 7:02 p.m.