dmbc_get_postmean | R Documentation |
dmbc_get_postmean()
is an extractor function for extracting the
posterior mean estimates of the parameters for a fitted DMBC model.
dmbc_get_postmean(res, chain = 1)
res |
An object of class |
chain |
A length-one numeric vector indicating the MCMC chain number to use. |
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
chain
: length-one numeric vector of the MCMC chain number used
Sergio Venturini sergio.venturini@unicatt.it
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>.
dmbc_data
for a description of the data format.
dmbc_fit_list
for a description of the elements
included in the returned object.
## 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_postmean(sim.dmbc, chain = 1) ## End(Not run)
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