STATegRa (version 1.0.0)

modelSelection: Number of optimal common and distinctive components in object-wise data

Description

This function combine the function selectCommonComps and PCA.selection to stimate the optimal number of common and distinctive components in object-wise data. That is, it calculates the optimal number of common and individual components depending on the maximal number of common components and the individual components selection criteria provided by the user. The result is a list with the optimal number of common and distinctive components. This optimal number can be use for component analysis with function omicsCompAnalysis

Usage

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

Arguments

Input

List of ExpressionSet objects, one for each block of data

Rmax

Maximum number of common components

fac.sel

Criterium for selecting number of components. The posible option are: "%accum", "single%", "rel.abs" and "fixed.num"

varthreshold

Threshold for the selection of components in "%accum", "single%" criterions

nvar

Threshold applied when the option "rel.abs" is selected

PCnum

Fixed number of components to select when the option "fixed.num" is selected

Value

The function returns a list with the following components:

common

Number of optimal common components

dist

Number of optimal distictive components for each block

See Also

selectCommonComps,PCA.selection,omicsCompAnalysis

Examples

data(STATegRa_S3)

## Create ExpressionSets
# Block1 - Expression data
B1 <- createOmicsExpressionSet(Data=Block1.PCA,pData=ed.PCA,pDataDescr=c("classname"))
# Block2 - miRNA expression data
B2 <- createOmicsExpressionSet(Data=Block2.PCA,pData=ed.PCA,pDataDescr=c("classname"))

## Model Selection
ms <- modelSelection(Input=list(B1,B2),Rmax=4,fac.sel="single%",
                      varthreshold=0.03)
ms