Collapsed LDA Gibbs sampling for sparse (d, v, c) triplet data.

This function performs collapsed Gibbs sampling for the standard LDA model using a sparse document-term representation:

1. initializes the LDA state via init_mod_from_count(),

2. runs n_iter iterations of the C++ Gibbs kernel eLDA_pass_b_fast(),

3. returns the final model state, including posterior topic-word and document-topic distributions.

Usage

run_lda_gibbs(
  count,
  K,
  alpha,
  beta,
  n_iter = 100L,
  phi = NULL,
  seed = NULL,
  verbose = TRUE,
  progress_every = 10L
)

Arguments

count

Integer matrix of size NZ x 3 with rows (d, v, c) in 0-based indexing: document index d, word index v, and count c for that pair.

K

Integer, number of topics. Required unless phi is supplied. If phi is provided, K is inferred from ncol(phi).

alpha

Scalar Dirichlet prior parameter for document-topic distributions.

beta

Scalar Dirichlet prior parameter for topic-word distributions.

n_iter

Integer, number of Gibbs sweeps to run.

phi

Optional V x K topic-word probability matrix used only for initializing topic assignments in init_mod_from_count().

seed

Optional integer random seed passed to the initializer.

verbose

Logical; if TRUE, print progress messages.

progress_every

Integer; print progress every this many iterations.

Value

A list mod containing:

z

Integer vector of length NZ; final topic assignments (0-based).

nd

D x K document-topic count matrix.

nw

K x V topic-word count matrix.

ndsum

Integer vector of length D; document token counts.

nwsum

Integer vector of length K; topic token counts.

phi

V x K topic-word posterior mean \(p(w \mid z=k)\) computed from nw.

theta

D x K document-topic posterior mean \(p(z=k \mid d)\) computed from nd.

loglik_trace

Vector of log-likelihoods.

D

Number of documents.

V

Vocabulary size.

K

Number of topics.

NZ

Number of non-zero (d, v, c) entries.