ANTsR (version 0.3.1)

initializeEigenanatomy: Convert a matrix to a form that can be used to initialize sparse cca and pca.

Description

InitializeEigenanatomy is a helper function to initialize sparseDecom and sparseDecom2.

Usage

initializeEigenanatomy(initmat, mask = NA, nreps = 1)

Arguments

initmat

input matrix where rows provide initial vector values

mask

mask if available

nreps

nrepetitions to use

Value

list is output

Examples

# NOT RUN {
mat<-t(replicate(3, rnorm(100)) )
for ( i in 1:nrow(mat) ) mat[i, abs(mat[i,]) < 1 ]<-0
initdf<-initializeEigenanatomy( mat )
dmat<-replicate(100, rnorm(20))
eanat<-sparseDecom( dmat, inmask=initdf$mask,
  sparseness=0, smooth=0,
  initializationList=initdf$initlist, cthresh=0,
  nvecs=length(initdf$initlist) )
initdf2<-initializeEigenanatomy( mat, nreps=2 )
eanat<-sparseDecom( dmat, inmask=initdf$mask,
  sparseness=0, smooth=0, z=-0.5,
  initializationList=initdf2$initlist, cthresh=0,
  nvecs=length(initdf2$initlist) )
# now a regression
eanatMatrix<-imageListToMatrix(  eanat$eigenanatomyimages, initdf$mask )
# 'averages' loosely speaking anyway
myEigenanatomyRegionAverages<-dmat %*% t( eanatMatrix )
dependentvariable<-rnorm( nrow(dmat) )
summary(lm( dependentvariable ~ myEigenanatomyRegionAverages ))

nvox<-1000
dmat<-replicate(nvox, rnorm(20))
dmat2<-replicate(30, rnorm(20))
mat<-t(replicate(3, rnorm(nvox)) )
initdf<-initializeEigenanatomy( mat )
eanat<-sparseDecom2( list(dmat,dmat2), inmask=c(initdf$mask,NA),
  sparseness=c( -0.1, -0.2 ), smooth=0,
  initializationList=initdf$initlist, cthresh=c(0,0),
  nvecs=length(initdf$initlist), priorWeight = 0.1 )
# }