perplexity(object, newdata, ...)
"perplexity"(object, newdata, control, ...)
"perplexity"(object, newdata, control, use_theta = TRUE,
estimate_theta = TRUE, ...)
"perplexity"(object, newdata, control, use_theta = TRUE,
estimate_theta = TRUE, ...)"TopicModel" or "Gibbs_list".newdata needs to be specified.control of the fitted model is
used with suitable changes of the relevant parameters (see
Details)."logical". If TRUE
the estimated topic distributions for the documents are
used. Otherwise equal weights are assigned to the topics for each document."logical". If FALSE the
data provided is assumed to be the same as the data used for fitting the
model. The topic distributions therefore do not need to be estimated
and the data in newdata is used for weighting the
term-document occurrences.estimate.beta=FALSE and (2) nstart=1. For "Gibbs_list" objects the control is further modified
to have (1) iter=thin and (2) best=TRUE and the model is
fitted to the new data with this control for each available
iteration. The perplexity is then determined by averaging over the
same number of iterations.
If a list is supplied as object, it is assumed that it
consists of several models which were fitted using different starting
configurations.
Griffiths T.L., Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences of the United States of America, 101, Suppl. 1, 5228--5235. Newman D., Asuncion A., Smyth P., Welling M. (2009). Distributed Algorithms for Topic Models. Journal of Machine Learning Research, 10, 1801--1828.