PMA v1.2.1

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Penalized Multivariate Analysis

Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008> and Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data <doi:10.2202/1544-6115.1470>.

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PMA

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Penalized Multivariate Analysis

Functions in PMA

Name Description
PlotCGH Plot CGH data
MultiCCA.permute Select tuning parameters for sparse multiple canonical correlation analysis using the penalized matrix decomposition.
SPC.cv Perform cross-validation on sparse principal component analysis
PMD Get a penalized matrix decomposition for a data matrix.
SPC Perform sparse principal component analysis
PMA-package Penalized Multivariate Analysis
MultiCCA Perform sparse multiple canonical correlation analysis.
PMD.cv Do tuning parameter selection for PMD via cross-validation
CCA Perform sparse canonical correlation analysis using the penalized matrix decomposition.
CCA.permute Select tuning parameters for sparse canonical correlation analysis using the penalized matrix decomposition.
breastdata Breast cancer gene expression + DNA copy number data set from Chin et. al. and used in Witten, et. al. See references below.
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Details

Type Package
Date 2020-02-04
URL https://github.com/bnaras/PMA
BugReports https://github.com/bnaras/PMA/issues
License GPL (>= 2)
Encoding UTF-8
RoxygenNote 7.0.2
NeedsCompilation yes
Packaged 2020-02-03 16:55:16 UTC; tibs
Repository CRAN
Date/Publication 2020-02-03 17:30:07 UTC

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