Regularized Simultaneous Component Based Data Integration

Documentation for package ‘RegularizedSCA’ version 0.5.4

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RSCA-package	RSCA: A package for regularized simultaneous component analysis (SCA) for data integration.
cv_sparseSCA	A K-fold cross-validation procedure when common/distinctive processes are unknown with Lasso and Group Lasso penalties.
cv_structuredSCA	A K-fold cross-validation procedure when common/distinctive processes are known, with a Lasso penalty.
DISCOsca	DISCO-SCA rotation.
Herring	Herring data
maxLGlasso	An algorithm for determining the smallest values for Lasso and Group Lasso tuning parameters that yield all zeros.
pca_gca	PCA-GCA method for selecting the number of common and distinctive components.
plot.CVsparseSCA	Ploting Cross-validation results
plot.CVstructuredSCA	Cross-validation plot
pre_process	Standardize the given data matrix per column, over the rows, with multiple imputation for missing data.
RSCA	RSCA: A package for regularized simultaneous component analysis (SCA) for data integration.
sparseSCA	Variable selection with Lasso and Group Lasso with a multi-start procedure.
structuredSCA	Variable selection algorithm with a predefined component loading structure.
summary.CVsparseSCA	Display a summary of the results of 'cv_sparseSCA()'.
summary.CVstructuredSCA	Display a summary of the results of 'cv_structuredSCA()'.
summary.DISCOsca	Display a summary of the results of 'DISCOsca()'.
summary.sparseSCA	Display a summary of the results of 'sparseSCA()'.
summary.structuredSCA	Display a summary of the results of 'structuredSCA()'.
summary.undoS	Display a summary of the results of 'undoShrinkage()'.
summary.VAF	Display a summary of the results of 'VAF()'.
TuckerCoef	Tucker coefficient of congruence.
undoShrinkage	Undo shrinkage.
VAF	Proportion of variance accounted for (VAF) for each block and each principal component.