R: Get predictions from a Latent Dirichlet Allocation model

predict.lda_topic_model {textmineR} | R Documentation

## Get predictions from a Latent Dirichlet Allocation model

### Description

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

### Usage

    ## S3 method for class '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"))

    ## End(Not run)

[Package textmineR version 3.0.5 Index]