ANTsR (version 1.0)

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

initdataframe<-initializeEigenanatomy(  initmat  , mask=NA, nreps=1  )

Arguments

mat

input matrix where rows provide initial vector values

nreps

nrepetitions to use

mask

mask if available

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 
# }
# NOT RUN {
<!-- %*% t( eanatMatrix ) -->
# }
# NOT RUN {
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)) )
for ( i in 1:nrow(mat) ) {
  mat[i,]<-eanatsparsify( mat[i,] , 0.5^(i+1)  )
  print(paste(sum(mat[i,]>0)/ncol(mat), 0.5^(i+1)  ))
}
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 )
# }