Run Multiple Independent Bayesian PGMM Chains — pgmm_rjmcmc

Runs independent `pgmm_rjmcmc()` chains, optionally in parallel. This is the safest way to use multiple CPU cores because each MCMC iteration depends on the previous state, while independent chains can be evaluated concurrently.

Usage

pgmm_rjmcmc_chains(
  X,
  m_init,
  m_range,
  q_new,
  delta = 2,
  ggamma = 2,
  burn = 20,
  niter = 1000,
  constraint = c(0, 0, 0),
  d_vec = c(1, 1, 1),
  s_vec = c(1, 1, 1),
  m_step = 0,
  v_step = 0,
  split_combine = 0,
  verbose = FALSE,
  chains = 2,
  cores = min(chains, available_cores()),
  seed = NULL
)

Arguments

X: the observation matrix with variables in rows and observations in columns.
m_init: the number of initial clusters.
m_range: the allowed range for the number of clusters.
q_new: the number of latent factors for a new cluster.
delta: scalar hyperparameter for the noise covariance prior
ggamma: scalar hyperparameter used in covariance-structure proposals
burn: the number of burn-in iterations
niter: the number of posterior sampling iterations
constraint: initial PGMM covariance constraint. Use a three-letter model label such as `"CCC"` or `"UUU"`, or a numeric vector of length three with binary entries. For example, `c(1, 1, 1)` is `CCC`, the fully constrained model, and `c(0, 0, 0)` is `UUU`, the fully unconstrained model.
d_vec: a vector of hyperparameters with length three, shape parameters for alpha1, alpha2 and bbeta respectively
s_vec: a vector of hyperparameters with length three, rate parameters for alpha1, alpha2 and bbeta respectively
m_step: indicator for RJMCMC model selection on the number of clusters.
v_step: indicator for RJMCMC model selection on covariance structures.
split_combine: indicator for using split/combine moves in the cluster-number RJMCMC step.
verbose: logical; if `TRUE`, print iteration progress.
chains: positive integer giving the number of independent chains.
cores: positive integer giving the number of worker processes to use. Values greater than `chains` are reduced to `chains`.
seed: optional integer seed used to generate deterministic per-chain seeds.

Value

A list with one fitted `pgmm_rjmcmc()` result per chain. The result has class `bpgmm_rjmcmc_chains` and stores the per-chain seeds in the `chain_seeds` attribute.