`update.lda_topic_model.Rd`

Update an LDA model with new data using collapsed Gibbs sampling.

# S3 method for lda_topic_model update(object, dtm, additional_k = 0, iterations = NULL, burnin = -1, new_alpha = NULL, new_beta = NULL, optimize_alpha = FALSE, calc_likelihood = FALSE, calc_coherence = TRUE, calc_r2 = FALSE, ...)

object | a fitted object of class |
---|---|

dtm | A document term matrix or term co-occurrence matrix of class dgCMatrix. |

additional_k | Integer number of topics to add, defaults to 0. |

iterations | Integer number of iterations for the Gibbs sampler to run. A future version may include automatic stopping criteria. |

burnin | Integer number of burnin iterations. If |

new_alpha | For now not used. This is the prior for topics over documents used when updating the model |

new_beta | For now not used. This is the prior for words over topics used when updating the model. |

optimize_alpha | Logical. Do you want to optimize alpha every 10 Gibbs iterations?
Defaults to |

calc_likelihood | Do you want to calculate the likelihood every 10 Gibbs iterations?
Useful for assessing convergence. Defaults to |

calc_coherence | Do you want to calculate probabilistic coherence of topics
after the model is trained? Defaults to |

calc_r2 | Do you want to calculate R-squared after the model is trained?
Defaults to |

... | Other arguments to be passed to |

Returns an S3 object of class c("LDA", "TopicModel").

if (FALSE) { # load a document term matrix d1 <- nih_sample_dtm[1:50,] d2 <- nih_sample_dtm[51:100,] # fit a model m <- FitLdaModel(d1, k = 10, iterations = 200, burnin = 175, optimize_alpha = TRUE, calc_likelihood = FALSE, calc_coherence = TRUE, calc_r2 = FALSE) # update an existing model by adding documents m2 <- update(object = m, dtm = rbind(d1, d2), iterations = 200, burnin = 175) # use an old model as a prior for a new model m3 <- update(object = m, dtm = d2, # new documents only iterations = 200, burnin = 175) # add topics while updating a model by adding documents m4 <- update(object = m, dtm = rbind(d1, d2), additional_k = 3, iterations = 200, burnin = 175) # add topics to an existing model m5 <- update(object = m, dtm = d1, # this is the old data additional_k = 3, iterations = 200, burnin = 175) }