dmbc_fit-class: An S4 class to represent the results of fitting DMBC model.
In dmbc: Model Based Clustering of Binary Dissimilarity Measurements

dmbc_fit-class

R Documentation

An S4 class to represent the results of fitting DMBC model.

Description

An S4 class to represent the results of fitting DMBC model using a single Markov Chain Monte Carlo chain.

Slots

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 α parameters.
eta.chain: An object of class matrix; posterior draws from the MCMC algorithm for the η parameters.
sigma2.chain: An object of class matrix; posterior draws from the MCMC algorithm for the σ^2 parameters.
lambda.chain: An object of class matrix; posterior draws from the MCMC algorithm for the λ 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.

References

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>.