R: Calculate the log likelihood of a document term matrix given...

CalcLikelihood {textmineR} | R Documentation

## Calculate the log likelihood of a document term matrix given a topic model

### Description

This function takes a DTM, phi matrix (P(word|topic)), and a theta matrix (P(topic|document)) and returns a single value for the likelihood of the data given the model.

### Usage

    CalcLikelihood(dtm, phi, theta, ...)

### Arguments

`dtm` | The document term matrix of class `dgCMatrix`.
`phi` | The phi matrix whose rows index topics and columns index words. The i, j entries are P(word_i | topic_j)
`theta` | The theta matrix whose rows index documents and columns index topics. The i, j entries are P(topic_i | document_j)
`...` | Other arguments to pass to `TmParallelApply`. See note, below.

### Value

Returns an object of class `numeric` corresponding to the log likelihood.

### Note

This function performs parallel computation if `dtm` has more than 3,000 rows. The default is to use all available cores according to `detectCores`. However, this can be modified by passing the `cpus` argument when calling this function.

### Examples

    # Load a pre-formatted dtm and topic model
    data(nih_sample_dtm)
    data(nih_sample_topic_model)

    # Get the likelihood of the data given the fitted model parameters
    ll <- CalcLikelihood(dtm = nih_sample_dtm,
                         phi = nih_sample_topic_model$phi,
                         theta = nih_sample_topic_model$theta)

    ll

[Package textmineR version 3.0.5 Index]