ANTsR (version 1.0)

sparseDecom: Convenience wrapper for eigenanatomy decomposition.

Description

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

Usage

sparseDecom( inmatrix=NA,  inmask=NA , sparseness=0.01 , nvecs=50 , its=5 , cthresh=250 )

Arguments

inmatrix

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

inmask

optional antsImage mask

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<-sparseDecom( mat ) 
# for prediction
library(spls)
library(randomForest)
data(lymphoma)
training<-sample( rep(c(TRUE,FALSE),31)  )
sp<-0.02 ; myz<-0
ldd<-sparseDecom( lymphoma$x[training,], nvecs=5 , sparseness=( sp ), mycoption=1, z=myz ) # NMF style
traindf<-data.frame( lclass=as.factor(lymphoma$y[ training  ]), eig = lymphoma$x[training,]  <!-- %*% as.matrix(ldd$eigenanatomyimages )) -->
testdf<-data.frame(  lclass=as.factor(lymphoma$y[ !training ]), eig = lymphoma$x[!training,] <!-- %*% as.matrix(ldd$eigenanatomyimages )) -->
myrf<-randomForest( lclass ~ . ,   data=traindf )
predlymp<-predict(myrf, newdata=testdf)
print(paste("N-errors:",sum(abs( testdf$lclass != predlymp ) )," non-zero ",sum(abs( ldd$eigenanatomyimages ) > 0 ) ) )
# compare to http://arxiv.org/pdf/0707.0701v2.pdf 
# now SNPs
library(randomForest)
library(BGLR)
data(mice)
snps<-quantifySNPs( mice.X, shiftit =T )
numericalpheno<-as.matrix( mice.pheno[,c(4,5,13,15) ] )
nfolds<-6
train<-sample( rep( c(1:nfolds), 1800/nfolds ) )
train<-( train < 4 )
lrmat<-lowrankRowMatrix( as.matrix( snps[train,] ) ,  50 )
snpd<-sparseDecom( lrmat, nvecs=20 , sparseness=( 0.001), z=-1 )
projmat<-as.matrix( snpd$eig ) 
snpse<-as.matrix( snps[train, ]  ) <!-- %*% projmat  -->
traindf<-data.frame( bmi=numericalpheno[train,3] , snpse=snpse)
snpse<-as.matrix( snps[!train, ]  ) <!-- %*% projmat  -->
testdf <-data.frame( bmi=numericalpheno[!train,3] , snpse=snpse )
myrf<-randomForest( bmi ~ . , data=traindf )
preddf<-predict(myrf, newdata=testdf )
cor.test(preddf, testdf$bmi )
plot(preddf, testdf$bmi )
# }