ANTsR (version 0.3.1)

networkEiganat: Convenience wrapper for eigenanatomy decomposition.

Description

Decomposes a matrix into sparse eigenevectors to maximize explained variance.

Usage

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

Examples

# NOT RUN {
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
###########
# }