Description Usage Arguments Value Author(s) Examples
View source: R/spatialbayesianlm.R
Take a standard lm result and use bayesian regression to impose spatial regularity.
1 2 3 4 5 6 7 8 9 10 | spatialbayesianlm(
mylm,
ymat,
mask,
smth = 1,
priorWeight = 1,
nhood = NA,
regweights = NA,
smoothcoeffmat = NA
)
|
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 |
bayesian regression solution is output as a list of images
Avants BB
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | # 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=NULL, 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)
|
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