Convenience wrapper for eigenanatomy decomposition.

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

sparseDecomboot(
  inmatrix,
  inmask = NULL,
  sparseness = 0.01,
  nvecs = 50,
  its = 5,
  cthresh = 250,
  z = 0,
  smooth = 0,
  initializationList = list(),
  mycoption = 0,
  nboot = 10,
  nsamp = 0.9,
  robust = 0,
  doseg = TRUE
)

Arguments

inmatrix

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

optional antsImage mask
sparseness

lower values equal more sparse
nvecs

number of vectors
its

number of iterations
cthresh

cluster threshold
z

u penalty, experimental
smooth

smoothness eg 0.5
initializationList

see initializeEigenanatomy
mycoption

0, 1 or 2 all produce different output 0 is combination of 1 (spatial orthogonality) and 2 (subject space orthogonality)
nboot

boostrap integer e.g. 10 equals 10 boostraps
nsamp

value less than or equal to 1, e.g. 0.9 means 90 percent of data will be used in each boostrap resampling
robust

boolean
doseg

orthogonalize bootstrap results

Author

Avants BB

Examples


mat<-replicate(100, rnorm(20))
mydecom<-sparseDecomboot( mat, nboot=5, nsamp=0.9, nvecs=2 )
#> [1] "boot 1 sample 18"
#> [1] "boot 2 sample 18"
#> [1] "boot 3 sample 18"
#> [1] "boot 4 sample 18"
#> [1] "boot 5 sample 18"

if (FALSE) {
# for prediction
if ( usePkg("randomForest") & usePkg("spls") ) {
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$x[training,]  %*% t(outmat) )
testdf<-data.frame(  lclass=as.factor(lymphoma$y[ !training ]),
  eig = lymphoma$x[!training,] %*% t(outmat) )
myrf<-randomForest( lclass ~ . ,   data=traindf )
predlymp<-predict(myrf, newdata=testdf)
print(paste('N-errors:',sum(abs( testdf$lclass != predlymp ) ),
  'non-zero ',sum(abs( outmat ) > 0 ) ) )
for ( i in 1:nv )
  print(paste(' non-zero ',i,' is: ',sum(abs( outmat[i,] ) > 0 ) ) )
}
} # end dontrun