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 main functions are as follows: (1) PMD (2) CCA (3) SPC
The 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.
| Package: | PMA |
| Type: | Package |
| Version: | 1.0.9 |
| Date: | 2013-03-23 |
| License: | GPL >= 2 |
Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3): 515-534.