Description Usage Arguments Value Author(s) Examples
View source: R/clusterTimeSeries.R
Uses clustering methods to split a time series into similar subsets.
1 | clusterTimeSeries(mat, krange = 2:10, nsvddims = NA, criterion = "asw")
|
mat |
input time series matrix |
krange |
k cluster range to explore |
nsvddims |
eg 2 |
criterion |
for clustering see pamk |
matrix is output
Avants BB
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
if (!exists("fn") ) fn<-getANTsRData("pcasl")
# high motion subject
asl<-antsImageRead(fn,4)
tr<-antsGetSpacing(asl)[4]
aslmean<-getAverageOfTimeSeries( asl )
aslmask<-getMask(aslmean,lowThresh=mean(aslmean),cleanup=TRUE)
omat<-timeseries2matrix(asl, aslmask )
clustasl<-clusterTimeSeries( omat, krange=4:10 )
for ( ct in 1:max(clustasl$clusters) )
{
sel<-clustasl$clusters != ct
img<-matrix2timeseries( asl, aslmask, omat[sel,] )
perf <- aslPerfusion( img,
dorobust=0.9, useDenoiser=4, skip=10, useBayesian=0,
moreaccurate=0, verbose=F, mask=aslmask )
perfp <- list( sequence="pcasl", m0=perf$m0 )
cbf <- quantifyCBF( perf$perfusion, perf$mask, perfp )
}
## End(Not run)
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