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"))
# }