dmbc_get_ml: Extractor function for a fitted DMBC model.

In dmbc: Model Based Clustering of Binary Dissimilarity Measurements

Extractor function for a fitted DMBC model.

Description

dmbc_get_ml() is an extractor function for extracting the maximum likelihood estimates of the parameters for a fitted DMBC model.

Usage

dmbc_get_ml(res, chain = 1)

Arguments

res	An object of class dmbc_fit_list.
chain	A length-one numeric vector indicating the MCMC chain number to use.

Value

A named list with the following elements:

z:

array of latent coordinates posterior mean estimates

alpha:

numeric vector of alpha posterior mean estimates

eta:

numeric vector of eta posterior mean estimates

sigma2:

numeric vector of sigma2 posterior mean estimates

lambda:

numeric vector of lambda posterior mean estimates

prob:

numeric matrix of probability posterior mean estimates

cluster:

numeric vector of cluster membership posterior mean estimates

loglik:

length-one numeric vector of the maximum log-likelihood value

chain:

length-one numeric vector of the MCMC chain number used

Author(s)

Sergio Venturini [e-mail]

References

Venturini, S., Piccarreta, R. (2021), "A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: the dmbc Package in R", Journal of Statistical Software, 100, 16, 1–35, <10.18637/jss.v100.i16>.

See Also

dmbc_data for a description of the data format.

dmbc_fit_list for a description of the elements included in the returned object.

Examples

## Not run: 
data(simdiss, package = "dmbc")

G <- 3
p <- 2
prm.prop <- list(z = 1.5, alpha = .75)
burnin <- 2000
nsim <- 1000
seed <- 2301

set.seed(seed)

control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]],
  alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE,
  nchains = 2, thin = 10, store.burnin = TRUE, threads = 2,
  parallel = "snow")
sim.dmbc <- dmbc(simdiss, p, G, control)

dmbc_get_ml(sim.dmbc, chain = 1)

## End(Not run)

dmbc documentation built on April 26, 2022, 5:05 p.m.