Obtains predictions of topics for new documents from a fitted LDA model

# S3 method for lda_topic_model
predict(object, newdata, method = c("gibbs",
  "dot"), iterations = NULL, burnin = -1, ...)

Arguments

object

a fitted object of class lda_topic_model

newdata

a DTM or TCM of class dgCMatrix or a numeric vector

method

one of either "gibbs" or "dot". If "gibbs" Gibbs sampling is used and iterations must be specified.

iterations

If method = "gibbs", an integer number of iterations for the Gibbs sampler to run. A future version may include automatic stopping criteria.

burnin

If method = "gibbs", an integer number of burnin iterations. If burnin is greater than -1, the entries of the resulting "theta" matrix are an average over all iterations greater than burnin.

...

Other arguments to be passed to TmParallelApply

Value

a "theta" matrix with one row per document and one column per topic

Examples

# 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"))
# }