Convenience wrapper for eigenanatomy decomposition.

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

networkEiganat(
  Xin,
  sparseness = c(0.1, 0.1),
  nvecs = 5,
  its = 5,
  gradparam = 1,
  mask = NA,
  v,
  prior,
  pgradparam = 0.1,
  clustval = 0,
  downsample = 0,
  doscale = T,
  domin = T,
  verbose = F,
  dowhite = 0,
  timeme = T,
  addb = T,
  useregression = T
)

Arguments

Xin	n by p input images , subjects or time points by row , spatial variable lies along columns
sparseness	sparseness pair c( 0.1 , 0.1 )
nvecs	number of vectors
its	number of iterations
gradparam	gradient descent parameter for data
mask	optional antsImage mask
v	the spatial solultion
prior	the prior
pgradparam	gradient descent parameter for prior term
clustval	integer greater than or equal to zero
downsample	bool
doscale	bool
domin	bool
verbose	bool
dowhite	bool
timeme	bool
addb	bool
useregression	bool

Value

outputs a decomposition of a population or time series matrix

Author

Avants BB

Examples

if (FALSE) {
mat<-replicate(100, rnorm(20))
mydecom<-networkEiganat( mat, nvecs=5 )
ch1<-usePkg('randomForest')
ch2<-usePkg('BGLR')
if ( ch1 & ch2 ) {
data(mice)
snps<-quantifySNPs( mice.X )
numericalpheno<-as.matrix( mice.pheno[,c(4,5,13,15) ] )
numericalpheno<-residuals( lm( numericalpheno ~
   as.factor(mice.pheno$Litter) ) )
phind<-3
nfolds<-6
train<-sample( rep( c(1:nfolds), 1800/nfolds ) )
train<-( train < 4 )
lowr<-lowrankRowMatrix(as.matrix( snps[train,] ),900)
snpdS<-sparseDecom( lowr , nvecs=2 , sparseness=( -0.001), its=3  )
snpdF<-sparseDecom( lowrankRowMatrix(as.matrix( snps[train,] ),100),
  nvecs=2 , sparseness=( -0.001), its=3 )
projmat<-as.matrix( snpdS$eig )
projmat<-as.matrix( snpdF$eig )
snpdFast<-networkEiganat( as.matrix( snps[train,] ), nvecs=2 ,
  sparseness=c( 1, -0.001 ) , downsample=45, verbose=T, its=3,
  gradparam=10 )
snpdSlow<-networkEiganat( as.matrix( snps[train,] ), nvecs=2 ,
  sparseness=c( 1, -0.001 ) , downsample=0, verbose=T,
  its=3, gradparam=10 )
snpd<-snpdSlow
snpd<-snpdFast
projmat<-as.matrix( snpd$v )
snpdF<-sparseDecom( lowrankRowMatrix(as.matrix( snps[train,] ),10) ,
  nvecs=2 , sparseness=( -0.001), its=3  )
projmat<-as.matrix( snpdS$eig )
snpse<-as.matrix( snps[train, ]  ) %*% projmat
traindf<-data.frame( bmi=numericalpheno[train,phind] , snpse=snpse)
snpse<-as.matrix( snps[!train, ]  ) %*% projmat
testdf <-data.frame( bmi=numericalpheno[!train,phind] , snpse=snpse )
myrf<-glm( bmi ~ . , data=traindf )
preddf<-predict(myrf, newdata=testdf )
cor.test(preddf, testdf$bmi )
if ( usePkg('visreg') ) {
mydf<-data.frame( PredictedBMIfromSNPs=preddf, RealBMI=testdf$bmi )
mymdl<-lm( PredictedBMIfromSNPs ~ RealBMI, data=mydf)
visreg::visreg(mymdl) }
###########
# vs glmnet #
###########
haveglm<-usePkg('glmnet')
if ( haveglm ) {
kk<-glmnet(y=numericalpheno[train,phind],x=snps[train,] )
ff<-predict(kk,newx=snps[!train,])
cor.test(ff[,25],numericalpheno[!train,phind])
mydf<-data.frame( PredictedBMIfromSNPs=ff[,25], RealBMI=testdf$bmi )
mymdl<-lm( PredictedBMIfromSNPs ~ RealBMI, data=mydf)
} # glmnet check
} # ch1 and ch2
###########
}