dmbc_fit-class {dmbc}

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 \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.

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>.

Examples

showClass("dmbc_fit")