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#RegularizedSCA v0.5.0- An R package for regularized simultaneous component based data integration

Recent update: We rewrote some functions to incorporate the S3 method.

The package performs a true integrated analysis of multiple data blocks from multiple sources. The methods included in this package combine simultaneous component analysis methods (SCA) with regularization (such as Lasso and Group Lasso).

To use the package, please read the vignette. An article regarding this package has been submitted. We hope that the paper will be accepted and incorporated directly in this package.

For the latest version of 'RegularizedSCA', please go to https://github.com/ZhengguoGu/RSCA/.

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Version

Install

install.packages('RegularizedSCA')

Monthly Downloads

26

Version

0.5.0

License

GPL (>= 2)

Maintainer

Zhengguo Gu

Last Published

October 17th, 2017

Functions in RegularizedSCA (0.5.0)

VAF

Proportion of variance accounted for (VAF) for each block and each principal component.
cv_sparseSCA

A K-fold cross-validation procedure when common/distinctive processes are unknown with Lasso and Group Lasso penalties.
summary.DISCOsca

Display a summary of the results of DISCOsca().
summary.VAF

Display a summary of the results of VAF().
DISCOsca

DISCO-SCA rotation.
Herring

Herring data
RSCA

RSCA: A package for regularized simultaneous component analysis (SCA) for data integration.
TuckerCoef

Tucker coefficient of congruence.
summary.sparseSCA

Display a summary of the results of sparseSCA().
summary.structuredSCA

Display a summary of the results of structuredSCA().
cv_structuredSCA

A K-fold cross-validation procedure when common/distinctive processes are known, with a Lasso penalty.
maxLGlasso

An algorithm for determining the smallest values for Lasso and Group Lasso tuning parameters that yield all zeros.
plot.CVsparseSCA

Ploting Cross-validation results
plot.CVstructuredSCA

Cross-validation plot
sparseSCA

Variable selection with Lasso and Group Lasso with a multi-start procedure.
structuredSCA

Variable selection algorithm with a predefined component loading structure.
mySTD

Standardize the given data matrix per column, over the rows.
pca_gca

PCA-GCA method for selecting the number of common and distinctive components.
summary.CVsparseSCA

Display a summary of the results of cv_sparseSCA().
summary.CVstructuredSCA

Display a summary of the results of cv_structuredSCA().
summary.undoS

Display a summary of the results of undoShrinkage().
undoShrinkage

Undo shrinkage.