sim_LDA_data: Simulate LDA data from an LDA structure given parameters

In LDATS: Latent Dirichlet Allocation Coupled with Time Series Analyses

Simulate LDA data from an LDA structure given parameters

Description

For a given set of parameters alpha and Beta and document-specific total word counts, simulate a document-by-term matrix. Additional structuring variables (the numbers of topics (k), documents (M), terms (V)) are inferred from input objects.

Usage

sim_LDA_data(N, Beta, alpha = NULL, Theta = NULL, seed = NULL)

Arguments

N	A vector of document sizes (total word counts). Must be integer conformable. Is used to infer the total number of documents.
Beta	matrix of categorical distribution parameters defining terms within topics. Dimension: k x V (number of topics x number of terms). Used to infer both (k) and (V). Must be non-negative and sum to 1 within topics.
alpha	Single positive numeric value for the Dirichlet distribution parameter defining topics within documents. To specifically define document topic probabilities, use Theta.
Theta	matrix of probabilities defining topics within documents. Dimension: M x k (documents x topics). Must be non-negative and sum to 1 within documents. To generally define document topic probabilities, use alpha.
seed	Input to set.seed.

Value

A document-by-term matrix of counts (dim: M x V).

Examples

  N <- c(10, 22, 15, 31)
  alpha <- 1.2
  Beta <- matrix(c(0.1, 0.1, 0.8, 0.2, 0.6, 0.2), 2, 3, byrow = TRUE)
  sim_LDA_data(N, Beta, alpha = alpha)
  Theta <- matrix(c(0.2, 0.8, 0.8, 0.2, 0.5, 0.5, 0.9, 0.1), 4, 2, 
               byrow = TRUE)
  sim_LDA_data(N, Beta, Theta = Theta)

LDATS documentation built on Sept. 19, 2023, 5:08 p.m.