initialize-dmbc_fit-method: Create an instance of the 'dmbc_fit' class using...

In dmbc: Model Based Clustering of Binary Dissimilarity Measurements

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 signature '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(s)

Sergio Venturini [e-mail]

dmbc documentation built on Sept. 30, 2024, 9:31 a.m.