initialize,dmbc_fit-method: Create an instance of the `dmbc_fit` class using new/initialize.

Description

Create an instance of the dmbc_fit class using new/initialize.

Usage

# S4 method for dmbc_fit
initialize(
  .Object,
  z.chain = array(),
  z.chain.p = array(),
  alpha.chain = matrix(),
  eta.chain = matrix(),
  sigma2.chain = matrix(),
  lambda.chain = matrix(),
  prob.chain = array(),
  x.ind.chain = array(),
  x.chain = matrix(),
  accept = matrix(),
  diss = list(),
  dens = list(),
  control = list(),
  prior = list(),
  dim = list(),
  model = NA
)

Arguments

.Object: Prototype object from the class dmbc_fit.
z.chain: An object of class array; posterior draws from the MCMC algorithm for the (untransformed) latent configuration \(Z\).
z.chain.p: An object of class array; posterior draws from the MCMC algorithm for the (Procrustes-transformed) latent configuration \(Z\).
alpha.chain: An object of class matrix; posterior draws from the MCMC algorithm for the \(\alpha\) parameters.
eta.chain: An object of class matrix; posterior draws from the MCMC algorithm for the \(\eta\) parameters.
sigma2.chain: An object of class matrix; posterior draws from the MCMC algorithm for the \(\sigma^2\) parameters.
lambda.chain: An object of class matrix; posterior draws from the MCMC algorithm for the \(\lambda\) parameters.
prob.chain: An object of class array; posterior draws from the MCMC algorithm for the cluster membership probabilities.
x.ind.chain: An object of class array; posterior draws from the MCMC algorithm for the cluster membership indicators.
x.chain: An object of class matrix; posterior draws from the MCMC algorithm for the cluster membership labels.
accept: An object of class matrix; final acceptance rates for the MCMC algorithm.
diss: An object of class list; list of observed dissimilarity matrices.
dens: An object of class list; list of log-likelihood, log-prior and log-posterior values at each iteration of the MCMC simulation.
control: An object of class list; list of the control parameters (number of burnin and sample iterations, number of MCMC chains, etc.). See dmbc_control() for more information.
prior: An object of class list; list of the prior hyperparameters. See dmbc_prior() for more information.
dim: An object of class list; list of dimensions for the estimated model, i.e. number of objects (n), number of latent dimensions (p), number of clusters (G), and number of subjects (S).
model: An object of class dmbc_model.

Author

Sergio Venturini sergio.venturini@unicatt.it