postdpmixciz: Summary statistics and cluster estimation

In dpmixsim: Dirichlet Process Mixture Model Simulation for Clustering and Image Segmentation

Description

postdpmixciz computes post-simulation summary statistics, and estimates cluster partition.

Usage

postdpmixciz(x, res, kmax=30, rec=300, ngrid=200, plot=TRUE)

Arguments

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

Value

z	cluster partition estimation

Author(s)

A. Ferreira da Silva, Universidade Nova de Lisboa, Faculdade de Ciencias e Tecnologia,
[e-mail].

References

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.

See Also

dpmixsim

Examples

## 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)

dpmixsim documentation built on May 1, 2019, 7:29 p.m.