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PMA2 (version 2.1)

Penalized Multivariate Analysis

Description

A modified version of PMA. The CCA() and CCA.permute() functions can also compute the component-wise standard deviations of estimated U and V through permutations in addition to standardize them. Furthermore, it computes the non-parametric p-values for each components. Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Ali Mahzarnia, Alexander Badea (2022), "Joint Estimation of Vulnerable Brain Networks and Alzheimer<80><99>s Disease Risk Via Novel Extension of Sparse Canonical Correlation" at bioRxiv.

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Install

install.packages('PMA2')

Monthly Downloads

3

Version

2.1

License

GPL (>= 2)

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Maintainer

Ali Mhazarnia

Last Published

May 12th, 2022

Functions in PMA2 (2.1)

SPC.cv

Perform cross-validation on sparse principal component analysis
SPC

Perform sparse principal component analysis
breastdata

Breast cancer gene expression + DNA copy number data set from Chin et. al. and used in Witten, et. al. See references below.
CCA

Perform sparse canonical correlation analysis using the penalized matrix decomposition.
PMA-package

Penalized Multivariate Analysis
CCA.permute

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

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

Perform sparse multiple canonical correlation analysis.
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