R: Methods for Function perplexity

perplexity {topicmodels} | R Documentation

## Methods for Function perplexity

### Description

Determine the perplexity of a fitted model.

### Usage

    perplexity(object, newdata, ...)

    ## S4 method for signature 'VEM,simple_triplet_matrix'
    perplexity(object, newdata, control, ...)

    ## S4 method for signature 'Gibbs,simple_triplet_matrix'
    perplexity(object, newdata, control, use_theta = TRUE,
    estimate_theta = TRUE, ...)

    ## S4 method for signature 'Gibbs_list,simple_triplet_matrix'
    perplexity(object, newdata, control, use_theta  = TRUE,
    estimate_theta = TRUE, ...)

### Arguments

`object` | Object of class `"TopicModel"` or `"Gibbs_list"`.
`newdata` | If missing, the perplexity for the data to which the model was fitted is determined. For objects fitted using Gibbs sampling `newdata` needs to be specified.
`control` | If missing, the `control` of the fitted model is used with suitable changes of the relevant parameters (see Details).
`use_theta` | Object of class `"logical"`. If `TRUE` the estimated topic distributions for the documents are used. Otherwise equal weights are assigned to the topics for each document.
`estimate_theta` | Object of class `"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.
`...` | Further arguments passed to the different methods.

### Details

The specified control is modified to ensure that (1) `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.

### Value

A numeric value.

### Author(s)

Bettina Gruen

### References

Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

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.

[Package topicmodels version 0.2-9 Index]