dmbc_IC | R Documentation |
dmbc_IC()
is the main function for simultaneously selecting the
optimal latent space dimension (p) and number of clusters
(G) for a DMBC analysis.
dmbc_IC( data, pmax = 3, Gmax = 5, control = dmbc_control(), prior = NULL, est = "mean" )
data |
An object of class |
pmax |
A length-one numeric vector indicating the maximum number of dimensions of the latent space to consider. |
Gmax |
A length-one numeric vector indicating the maximum number of cluster to consider. |
control |
A list of control parameters that affect the sampling
but do not affect the posterior distribution See
|
prior |
A list containing the prior hyperparameters. See
|
est |
A length-one character vector indicating the estimate type to
use. Possible values are |
A dmbc_ic
object.
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()
for fitting a DMBC model.
dmbc_ic
for a description of the elements included
in the returned object.
## Not run: data(simdiss, package = "dmbc") pmax <- 2 Gmax <- 2 prm.prop <- list(z = 1.5, alpha = .75) burnin <- 2000 nsim <- 1000 seed <- 1809 set.seed(seed) control <- list(burnin = burnin, nsim = nsim, z.prop = prm.prop[["z"]], alpha.prop = prm.prop[["alpha"]], random.start = TRUE, verbose = TRUE, thin = 10, store.burnin = TRUE) sim.ic <- dmbc_IC(data = simdiss, pmax = pmax, Gmax = Gmax, control = control, est = "mean") pmax <- pmax + 1 Gmax <- Gmax + 2 new.ic <- update(sim.ic, pmax = pmax, Gmax = Gmax) new.ic # plot the results library(bayesplot) library(ggplot2) color_scheme_set("mix-yellow-blue") p <- plot(new.ic, size = c(4, 1.5)) p + panel_bg(fill = "gray90", color = NA) ## End(Not run)
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