`CalcLikelihood.Rd`

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.

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

dtm | The document term matrix of class |
---|---|

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 |

Returns an object of class `numeric`

corresponding to the log likelihood.

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.

# 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#> [1] -57416.55