sparseDecom2: Convenience wrapper for 2-view eigenanatomy decomposition.

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Description

Decomposes two matrices into paired sparse eigenevectors to maximize canonical correlation. Note: we do not scale the matrices internally. We leave scaling choices to the user.

Usage

sparseDecom2(
  inmatrix,
  inmask = list(NULL, NULL),
  sparseness = c(0.01, 0.01),
  nvecs = 3,
  its = 20,
  cthresh = c(0, 0),
  statdir = NA,
  perms = 0,
  uselong = 0,
  z = 0,
  smooth = 0,
  robust = 0,
  mycoption = 0,
  initializationList = list(),
  initializationList2 = list(),
  ell1 = 10,
  priorWeight = 0,
  verbose = FALSE,
  rejector = 0,
  maxBased = FALSE
)

Arguments

inmatrix	input as inmatrix=list(mat1,mat2). n by p input matrix and n by q input matrix , spatial variable lies along columns.
inmask	optional pair of antsImage masks
sparseness	a c(.,.) pair of values e.g c(0.01,0.1) enforces an unsigned 99 percent and 90 percent sparse solution for each respective view
nvecs	number of eigenvector pairs
its	number of iterations, 10 or 20 usually sufficient
cthresh	cluster threshold pair
statdir	temporary directory if you want to look at full output
perms	number of permutations. settings permutations greater than 0 will estimate significance per vector empirically. For small datasets, these may be conservative. p-values depend on how one scales the input matrices.
uselong	enforce solutions of both views to be the same - requires matrices to be the same size
z	subject space (low-dimensional space) sparseness value
smooth	smooth the data (only available when mask is used)
robust	rank transform input matrices
mycoption	enforce 1 - spatial orthogonality, 2 - low-dimensional orthogonality or 0 - both
initializationList	initialization for first view
initializationList2	initialization for 2nd view
ell1	gradient descent parameter, if negative then l0 otherwise use l1
priorWeight	Scalar value weight on prior between 0 (prior is weak) and 1 (prior is strong). Only engaged if initialization is used
verbose	activates verbose output to screen
rejector	rejects small correlation solutions
maxBased	boolean that chooses max-based thresholding

Value

outputs a decomposition of a pair of matrices

Author(s)

Avants BB

Examples

mat<-replicate(100, rnorm(20))
mat2<-replicate(100, rnorm(20))
mat<-scale(mat)
mat2<-scale(mat2)
mydecom<-sparseDecom2(
inmatrix = list(mat,mat2),
sparseness=c(0.1,0.3),
nvecs=3, its=3, perms=0)
wt<-0.666
mat3<-mat*wt+mat2*(1-wt)
mydecom<-sparseDecom2( inmatrix=list(mat,mat3),
  sparseness=c(0.2,0.2), nvecs=5, its=10, perms=5 )

## Not run: 
# a masked example
im<-antsImageRead( getANTsRData("r64"))
mask<-thresholdImage( im, 250, Inf )
dd = sum( mask == 1 )
mat1<-matrix( rnorm(dd*10) , nrow=10 )
mat2<-matrix( rnorm(dd*10) , nrow=10 )
initlist<-list()
for ( nvecs in 1:2 ) {
  init1<-antsImageClone( mask )
  init1[ mask == 1 ]<-rnorm( dd )
  initlist<-lappend( initlist, init1 )
}
ff<-sparseDecom2( inmatrix=list(mat1,mat2), inmask=list(mask,mask),
  sparseness=c(0.1,0.1) ,nvecs=length(initlist) , smooth=1,
  cthresh=c(0,0), initializationList = initlist ,ell1 = 11 )
### now SNPs ###
rf<-usePkg('randomForest')
bg<-usePkg('BGLR')
if ( bg & rf ) {
data(mice)
snps<-mice.X
numericalpheno<-as.matrix( mice.pheno[,c(4,5,13,15) ] )
numericalpheno<-residuals( lm( numericalpheno ~
  as.factor(mice.pheno$Litter) ) )
nfolds<-6
train<-sample( rep( c(1:nfolds), 1800/nfolds ) )
train<-( train < 4 )
snpd<-sparseDecom2( inmatrix=list( ( as.matrix(snps[train,]) ),
  numericalpheno[train,] ), nvecs=20, sparseness=c( 0.001, -0.5 ),
  its=3, ell1=0.1 , z=-1 )
for ( j in 3:3) {
traindf<-data.frame( bmi=numericalpheno[ train,j] ,
   snpse=as.matrix( snps[train, ] ) %*% as.matrix( snpd$eig1 ) )
testdf <-data.frame( bmi=numericalpheno[!train,j] ,
   snpse=as.matrix( snps[!train,] ) %*% as.matrix( snpd$eig1 ) )
myrf<-randomForest( bmi ~ . , data=traindf )
preddf<-predict(myrf, newdata=testdf )
print( cor.test(preddf, testdf$bmi ) )
plot( preddf, testdf$bmi )
}
} # check bg and rf

## End(Not run)

neuroconductor-devel/ANTsR documentation built on April 1, 2021, 1:02 p.m.