fit.sentopicmodel: Estimate a topic model

Description

This function is used to estimate a topic model created by LDA(), JST() or rJST(). In essence, this function iterates a Gibbs sampler MCMC.

Usage

# S3 method for sentopicmodel
fit(
  object,
  iterations = 100,
  nChains = 1,
  displayProgress = TRUE,
  computeLikelihood = TRUE,
  seed = NULL,
  ...
)
# S3 method for multiChains
fit(
  object,
  iterations = 100,
  nChains = NULL,
  displayProgress = TRUE,
  computeLikelihood = TRUE,
  seed = NULL,
  ...
)

Value

a sentopicmodel of the relevant model class if nChains is unspecified or equal to 1. A multiChains object if nChains is greater than 1.

Arguments

object: a model created with the LDA(), JST() or rJST() function.
iterations: the number of iterations by which the model should be fitted.
nChains: if set above 1, the model will be fitted multiple times from various starting positions. Latent variables will be re-initialized if object has not been fitted before.
displayProgress: if TRUE, a progress bar will be displayed indicating the progress of the computation. When nChains is greater than 1, this requires the package progressr and optionally progress.
computeLikelihood: if set to FALSE, does not compute the likelihood at each iteration. This can slightly decrease the computing time.
seed: for reproducibility, a seed can be provided.
...: arguments passed to other methods. Not used.

Parallelism

When nChains > 1, the function can take advantage of future.apply::future_lapply (if installed) to spread the computation over multiple processes. This requires the specification of a parallel strategy using future::plan(). See the examples below.

Examples

Run this code

model <- rJST(ECB_press_conferences_tokens)
fit(model, 10)

# -- Parallel computation --
require(future.apply)
future::plan("multisession", workers = 2) # Set up 2 workers
fit(model, 10, nChains = 2)

future::plan("sequential") # Shut down workers