modelSelection: Find optimal common and distinctive components
In llrs/STATegRa: Classes and methods for multi-omics data integration

Description Usage Arguments Value Author(s) See Also Examples

Uses selectCommonComps and PCA.selection to estimate the optimal number of common and distinctive components according to given selection criteria.

1	modelSelection(Input, Rmax, fac.sel, varthreshold=NULL, nvar=NULL, PCnum=NULL)

`Input`	List of two ExpressionSet objects
`Rmax`	Maximum common components (see `selectCommonComps`)
`fac.sel`	PCA criteria (see `PCA.selection`)
`varthreshold`	Cumulative variance criteria (see `PCA.selection`)
`nvar`	Relative variance criteria (see `PCA.selection`)
`PCnum`	Fixed component number (see `PCA.selection`)

List containing:

common: Number of common components
dist: Number of distinct components per input block

Patricia Sebastian-Leon

selectCommonComps,PCA.selection,omicsCompAnalysis

data(STATegRa_S3)
B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))
ms <- modelSelection(Input=list(B1, B2), Rmax=4, fac.sel="single\%", varthreshold=0.03)
ms