FitLdaModel: Fit a Latent Dirichlet Allocation topic model

Description

Fit a Latent Dirichlet Allocation topic model using collapsed Gibbs sampling.

Usage

FitLdaModel(dtm, k, iterations = NULL, burnin = -1, alpha = 0.1,
  beta = 0.05, optimize_alpha = FALSE, calc_likelihood = FALSE,
  calc_coherence = TRUE, calc_r2 = FALSE, ...)

Arguments

dtm

A document term matrix or term co-occurrence matrix of class dgCMatrix

Integer number of topics

iterations

Integer number of iterations for the Gibbs sampler to run. A future version may include automatic stopping criteria.

burnin

Integer number of burnin iterations. If burnin is greater than -1, the resulting "phi" and "theta" matrices are an average over all iterations greater than burnin.

alpha

Vector of length k for asymmetric or a number for symmetric. This is the prior for topics over documents

beta

Vector of length ncol(dtm) for asymmetric or a number for symmetric. This is the prior for words over topics.

optimize_alpha

Logical. Do you want to optimize alpha every 10 Gibbs iterations? Defaults to FALSE.

calc_likelihood

Do you want to calculate the likelihood every 10 Gibbs iterations? Useful for assessing convergence. Defaults to FALSE.

calc_coherence

Do you want to calculate probabilistic coherence of topics after the model is trained? Defaults to TRUE.

calc_r2

Do you want to calculate R-squared after the model is trained? Defaults to FALSE.

...

Other arguments to be passed to TmParallelApply

Value

Returns an S3 object of class c("LDA", "TopicModel"). DESCRIBE MORE

Details

EXPLAIN IMPLEMENTATION DETAILS

Examples

Run this code

# NOT RUN {
# load some data
data(nih_sample_dtm)

# fit a model 
set.seed(12345)
m <- FitLdaModel(dtm = nih_sample_dtm[1:20,], k = 5,
                 iterations = 200, burnin = 175)

str(m)

# predict on held-out documents using gibbs sampling "fold in"
p1 <- predict(m, nih_sample_dtm[21:100,], method = "gibbs",
              iterations = 200, burnin = 175)

# predict on held-out documents using the dot product method
p2 <- predict(m, nih_sample_dtm[21:100,], method = "dot")

# compare the methods
barplot(rbind(p1[1,],p2[1,]), beside = TRUE, col = c("red", "blue")) 
# }