PMA-package: Penalized Multivariate Analysis
In PMA2: Penalized Multivariate Analysis
PMA-package R Documentation
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.
References
Ali Mahzarnia, Alexander Badea (2022), Joint Estimation of Vulnerable Brain Networks and Alzheimer’s Disease Risk Via Novel Extension of Sparse Canonical Correlation at bioRxiv.
PMA2 documentation built on May 12, 2022, 9:06 a.m.
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| PMA-package | R Documentation |
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.
References
Ali Mahzarnia, Alexander Badea (2022), Joint Estimation of Vulnerable Brain Networks and Alzheimer’s Disease Risk Via Novel Extension of Sparse Canonical Correlation at bioRxiv.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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