modelSelection: Find optimal common and distinctive components

Description

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

Usage

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

Arguments

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)

Value

List containing:

common

Number of common components

dist

Number of distinct components per input block

Author(s)

Patricia Sebastian-Leon

See Also

selectCommonComps,PCA.selection,omicsCompAnalysis

Examples

1
2
3
4
5
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

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.