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#RegularizedSCA v0.5.4 - An R package for regularized simultaneous component based data integration

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/.

#Update information The function maxLGlasso() has been further improved.

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Version

Install

install.packages('RegularizedSCA')

Monthly Downloads

29

Version

0.5.4

License

GPL (>= 2)

Maintainer

Zhengguo Gu

Last Published

June 7th, 2018

Functions in RegularizedSCA (0.5.4)

pre_process

Standardize the given data matrix per column, over the rows, with multiple imputation for missing data.
summary.DISCOsca

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

Undo shrinkage.
summary.undoS

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

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

Tucker coefficient of congruence.
pca_gca

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

DISCO-SCA rotation.
maxLGlasso

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

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

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

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

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

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

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

Ploting Cross-validation results
cv_structuredSCA

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

Display a summary of the results of structuredSCA().
plot.CVstructuredSCA

Cross-validation plot
Herring

Herring data
summary.sparseSCA

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

Variable selection algorithm with a predefined component loading structure.