ANTsR (version 1.0)

sparseDecomboot: Convenience wrapper for eigenanatomy decomposition.

Description

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

Usage

sparseDecomboot( inmatrix=NA,  inmask=NA , sparseness=0.01 , nvecs=50 , its=5 , cthresh=250  , nboot = 20, nsamp = 0.9 )

Arguments

inmatrix

n by p input images , subjects or time points by row , spatial variable lies along columns

inmask

optional antsImage mask

nboot

n bootstrap runs

nsamp

number of samples e.g. 0.9 indicates 90 percent of data

other params

see sccan for other parameters

Value

outputs a decomposition of a population or time series matrix

Examples

# NOT RUN {
mat<-replicate(100, rnorm(20)) 
mydecom<-sparseDecomboot( mat ) 
# for prediction
library(spls)
library(randomForest)
data(lymphoma)
training<-sample( rep(c(TRUE,FALSE),31)  )
sp<-0.001 ; myz<-0 ; nv<-5
ldd<-sparseDecomboot( lymphoma$x[training,], nvecs=nv , sparseness=( sp ), mycoption=1, z=myz , nsamp=0.9, nboot=50 ) # NMF style
outmat<-as.matrix(ldd$eigenanatomyimages )
# outmat<-t(ldd$cca1outAuto)
traindf<-data.frame( lclass=as.factor(lymphoma$y[ training  ]), eig = lymphoma$x[training,]  <!-- %*% outmat ) -->
testdf<-data.frame(  lclass=as.factor(lymphoma$y[ !training ]), eig = lymphoma$x[!training,] <!-- %*% outmat ) -->
myrf<-randomForest( lclass ~ . ,   data=traindf )
predlymp<-predict(myrf, newdata=testdf)
print(paste("N-errors:",sum(abs( testdf$lclass != predlymp ) )," non-zero ",sum(abs( outmat ) > 0 ) ) )
for ( i in 1:nv )
  print(paste(" non-zero ",i,' is: ',sum(abs( outmat[,i] ) > 0 ) ) )
# }