Description Usage Arguments Value

View source: R/fit.topics.perplexity.R

Calculates perplexity for values of k on an LDA topic model using the topicmodels package, split into training and testing sets using k-folds

1 | ```
fit.topics.perplexity(dtm, folds, k.values, alpha, beta, control.test)
``` |

`dtm` |
Document-term matrix. Constructed using the DocumentTermMatrix() command from the tm package |

`folds` |
Integer. The number of folds to make training and testing sets; recommended values are '5' and '10' - note that higher values considerably increase the time that model fitting takes |

`k.values` |
Numeric vector. Values to test k for. A good starting point is 2:10. All values must be greater than 1 |

`alpha` |
Numeric. Optional parameter: the value of alpha used in the LDA model. By default alpha is set to 0.1 |

`beta` |
Numeric. Optional parameter: the value of beta used in the LDA model. By default beta is set to 0.1 |

`control.test` |
List. Optional parameter: the LDA control list used in the LDA model. It is strongly recommended not to use this parameter unless you have good reason. Default settings are: nstart = 5, best = T, burnin = 1000, iter = 2000, thin = 500 |

Dataframe of perplexity for the k.values, calculated for the number of stipulated folds

bvidgen/RPackage documentation built on April 18, 2018, 8:26 p.m.

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