ANTsR (version 0.3.3)

spatialbayesianlm: spatially constrained bayesian regression function.

Description

Take a standard lm result and use bayesian regression to impose spatial regularity.

Usage

spatialbayesianlm(mylm, ymat, mask, smth = 1, priorWeight = 1, nhood = NA, regweights = NA, smoothcoeffmat = NA)

Arguments

mylm

standard lm result of the form mylm<-lm(ymat~.)

ymat

outcome matrix - usually from imaging data

mask

mask with non-zero entries n-columns of ymat

smth

smoothness parameter

priorWeight

weight on the prior

nhood

size of neighborhood

regweights

weights on rows - size of ymat

smoothcoeffmat

prior coefficient matrix

Value

bayesian regression solution is output as a list of images

Examples

  # make some simple data
  ## Not run: 
#   if (!exists("fn") ) fn<-getANTsRData("pcasl")
#   asl<-antsImageRead(fn)
#   tr<-antsGetSpacing(asl)[4]
#   aslmean<-getAverageOfTimeSeries( asl )
#   aslmask<-getMask(aslmean,lowThresh=mean(aslmean),cleanup=TRUE)
#   pcaslpre <- aslPerfusion( asl, dorobust=0, useDenoiser=NA, skip=1,
#      useBayesian=0, moreaccurate=0, verbose=T, mask=aslmask ) 
#   # user might compare to useDenoiser=FALSE
#   pcasl.parameters <- list( sequence="pcasl", m0=pcaslpre$m0 )
#   aslmat<-timeseries2matrix(asl,aslmask)
#   tc<-as.factor(rep(c("C","T"),nrow(aslmat)/2))
#   dv<-computeDVARS(aslmat)
#   perfmodel<-lm( aslmat ~ tc + stats::poly(dv,4) ) # standard model
#   ssp<-spatialbayesianlm( perfmodel, aslmat, aslmask,
#     priorWeight=1.e2 ,smth=1.6, nhood=rep(2,3) )
#   plot( ssp[[1]], slices="2x16x2", axis=3 )
#   ## End(Not run)