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

Description Usage Arguments Value Author(s) See Also Examples

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,center=FALSE,scale=FALSE,weight=FALSE, plot_common=FALSE, plot_dist=FALSE)

`Input`	List of `ExpressionSet` objects, one for each block of data
`Rmax`	Maximum common components
`fac.sel`	PCA criteria for selection ("%accum", "single%", "rel.abs", "fixed.num")
`varthreshold`	Cumulative variance criteria for PCA selection. Threshold for "%accum" or "single%" criteria.
`nvar`	Relative variance criteria. Threshold for "rel.abs".
`PCnum`	Fixed number of components for "fixed.num".
`center`	Character (or FALSE) specifying which (if any) centering will be applied before analysis. Choices are "PERBLOCKS" (each block separately) or "ALLBLOCKS" (all data together).
`scale`	Character (or FALSE) specifying which (if any) scaling will be applied before analysis. Choices are "PERBLOCKS" (each block separately) or "ALLBLOCKS" (all data together).
`weight`	Logical indicating whether weighting is to be done. Choices are "BETWEEN-BLOCKS"
`plot_common`	Logical indicating whether to plot the explained variances (SSQ) of each block and its estimation and the ratios
`plot_dist`	Logical indicating whether to plot the explained variances (SSQ) and the accumulated variance for each block

List containing:

common: List with common components results
commonComps: Optimal number of common components
ssqs: Matrix of SSQ for each block and estimator
pssq: ggplot object showing SSQ for each block and estimator
pratios: ggplot object showing SSQ ratios between each block and estimator
dist: List containg the results of distinct PCA for each input block; for each block PCAres and numComps is returned within a list
PCAres: List containing results of PCA, with fields "eigen", "var.exp", "scores" and "loadings"
nomComps: Number of components selected

Patricia Sebastian-Leon

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=3, fac.sel="single\%", varthreshold=0.03, center=TRUE, scale=FALSE, weight=TRUE, plot_common=FALSE, plot_dist=FALSE)
ms