Description Usage Arguments Value Author(s) References See Also Examples
postdpmixciz
computes post-simulation summary statistics,
and estimates cluster partition.
1 | postdpmixciz(x, res, kmax=30, rec=300, ngrid=200, plot=TRUE)
|
x |
data used in the simulation |
kmax |
maximum number of clusters |
res |
output of the MCMC simulation |
rec |
number of recorded iteration steps |
ngrid |
dimension of the grid used in density estimation |
plot |
logical variable to omit plots (default = TRUE |
z |
cluster partition estimation |
A. Ferreira da Silva, Universidade Nova de Lisboa,
Faculdade de Ciencias e Tecnologia,
afs@fct.unl.pt.
Adelino Ferreira da Silva, A Dirichlet process mixture model for brain MRI tissue classification, Medical Image Analysis 11 (2007) 169-182.
Adelino Ferreira da Silva, Bayesian mixture models of variable dimension for image segmentation, Comput. Methods Programs Biomed. 94 (2009) 1-14.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## Not run:
## Example: MRI brain image segmentation
slicedata <- readsliceimg(fbase="t1_pn3_rf0", swap=FALSE)
image(slicedata$niislice, col=gray((0:255)/256), main="original image")
x0 <- premask(slicedata, subsamp=TRUE)
x <- prescale(x0)
rec <- 3000
res <- dpmixsim(x, M=1, a=1, b=2, upalpha=1, maxiter=4000,
rec=rec, nclinit=8)
## post-simulation
ngrid <- 200
z <- postdpmixciz(x, res=res, rec=rec, ngrid=ngrid, plot=TRUE)
## End(Not run)
|
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