textmineR (version 3.0.4)

update.lda_topic_model: Update a Latent Dirichlet Allocation topic model with new data

Description

Update an LDA model with new data using collapsed Gibbs sampling.

Usage

# S3 method for lda_topic_model
update(object, dtm, additional_k = 0,
  iterations = NULL, burnin = -1, new_alpha = NULL,
  new_beta = NULL, optimize_alpha = FALSE, calc_likelihood = FALSE,
  calc_coherence = TRUE, calc_r2 = FALSE, ...)

Arguments

object

a fitted object of class lda_topic_model

dtm

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

additional_k

Integer number of topics to add, defaults to 0.

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.

new_alpha

For now not used. This is the prior for topics over documents used when updating the model

new_beta

For now not used. This is the prior for words over topics used when updating the model.

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").

Examples

# NOT RUN {
# load a document term matrix
d1 <- nih_sample_dtm[1:50,]

d2 <- nih_sample_dtm[51:100,]

# fit a model
m <- FitLdaModel(d1, k = 10, 
                 iterations = 200, burnin = 175,
                 optimize_alpha = TRUE, 
                 calc_likelihood = FALSE,
                 calc_coherence = TRUE,
                 calc_r2 = FALSE)

# update an existing model by adding documents
m2 <- update(object = m,
             dtm = rbind(d1, d2),
             iterations = 200,
             burnin = 175)
             
# use an old model as a prior for a new model
m3 <- update(object = m,
             dtm = d2, # new documents only
             iterations = 200,
             burnin = 175)
             
# add topics while updating a model by adding documents
m4 <- update(object = m,
             dtm = rbind(d1, d2),
             additional_k = 3,
             iterations = 200,
             burnin = 175)
             
# add topics to an existing model
m5 <- update(object = m,
             dtm = d1, # this is the old data
             additional_k = 3,
             iterations = 200,
             burnin = 175)

# }