dmbc_prior: Auxiliary Function for Setting DMBC Model Priors

Description

dmbc_prior() is an auxiliary function as user interface for dmbc() fitting. Typically only used when calling the dmbc() function. It is used to set prior hyperparameters.prior_dmbc() is an alias for dmbc_prior().check_prior() is an auxiliary function that verifies the correctness of the prior hyperparameters provided before a DMBC is fitted with dmbc().update_prior() is an auxiliary function to modify a set of prior choices using a new value of p and G. It is intended for internal use mainly in the dmbc_ic() function.

Usage

dmbc_prior(
  eta = list(a = rep(1.5, .dmbcEnv$current_G), b = rep(0.5, .dmbcEnv$current_G)),
  sigma2 = list(a = 0.1, b = 0.1),
  lambda = rep(1, .dmbcEnv$current_G)
)
prior_dmbc(
  eta = list(a = rep(1.5, .dmbcEnv$current_G), b = rep(0.5, .dmbcEnv$current_G)),
  sigma2 = list(a = 0.1, b = 0.1),
  lambda = rep(1, .dmbcEnv$current_G)
)
check_prior(prior)
update_prior(prior, p, G)

Value

A list with the prior hyperparameters as components.

Arguments

eta: A named list containing the hyperparameters for the prior distribution of the \(\eta_1,\ldots,\eta_G\) parameters. It should contain two numeric vectors, namely a and b.
sigma2: A named list containing the hyperparameters for the prior distributions of the \(\sigma^2_1,\ldots,\sigma^2_G\) parameters. It should contain two numeric scalars, namely a and b.
lambda: A list containing the hyperparameters for the prior distribution of the \(\lambda_1,\ldots,\lambda_G\) parameters. It should contain a single numeric vector.
prior: A named list of prior hyperparameters.
p: A length-one numeric vector indicating the number of dimensions of the latent space.
G: A length-one numeric vector indicating the number of cluster to partition the S subjects.

Author

Sergio Venturini sergio.venturini@unicatt.it

Examples

Run this code

if (FALSE) {
data(simdiss, package = "dmbc")
# Shorter run than default.
sim.fit <- dmbc(simdiss,
  control = dmbc_control(burnin = 1000, nsim = 2000, thin = 1, verbose = TRUE),
  prior = dmbc_prior(sigma2 = list(a = 1, b = 4)))
}