R: Virtual class "TopicModel"

TopicModel-class {topicmodels} | R Documentation

## Virtual class "TopicModel"

### Description

Fitted topic model.

### Objects from the Class

Objects of class `"LDA"` are returned by `LDA()` and of class `"CTM"` by `CTM()`.

### Slots

Class `"TopicModel"` contains

`call`:

  Object of class `"call"`.

`Dim`:

  Object of class `"integer"`; number of documents and terms.

`control`:

  Object of class `"TopicModelcontrol"`; options used for estimating the topic model.

`k`:

  Object of class `"integer"`; number of topics.

`terms`:

  Vector containing the term names.

`documents`:

  Vector containing the document names.

`beta`:

  Object of class `"matrix"`; logarithmized parameters of the word distribution for each topic.

`gamma`:

  Object of class `"matrix"`; parameters of the posterior topic distribution for each document.

`iter`:

  Object of class `"integer"`; the number of iterations made.

`logLiks`:

  Object of class `"numeric"`; the vector of kept intermediate log-likelihood values of the corpus. See `loglikelihood` how the log-likelihood is determined.

`n`:

  Object of class `"integer"`; number of words in the data used.

`wordassignments`:

  Object of class `"simple_triplet_matrix"`; most probable topic for each observed word in each document.

Class `"VEM"` contains

`loglikelihood`:

  Object of class `"numeric"`; the log-likelihood of each document given the parameters for the topic distribution and for the word distribution of each topic is approximated using the variational parameters and underestimates the log-likelihood by the Kullback-Leibler divergence between the variational posterior probability and the true posterior probability.

Class `"LDA"` extends class `"TopicModel"` and has the additional slots

`loglikelihood`:

  Object of class `"numeric"`; the posterior likelihood of the corpus conditional on the topic assignments is returned.

`alpha`:

  Object of class `"numeric"`; parameter of the Dirichlet distribution for topics over documents.

Class `"LDA_Gibbs"` extends class `"LDA"` and has the additional slots

`seed`:

  Either `NULL` or object of class `"simple_triplet_matrix"`; parameter for the prior distribution of the word distribution for topics if seeded.

`z`:

  Object of class `"integer"`; topic assignments of words ordered by terms with suitable repetition within documents.

Class `"CTM"` extends class `"TopicModel"` and has the additional slots

`mu`:

  Object of class `"numeric"`; mean of the topic distribution on the logit scale.

`Sigma`:

  Object of class `"matrix"`; variance-covariance matrix of topics on the logit scale.

Class `"CTM_VEM"` extends classes `"CTM"` and `"VEM"` and has the additional slots

`nusqared`:

  Object of class `"matrix"`; variance of the variational distribution on the parameter mu.

### Author(s)

Bettina Gruen

[Package topicmodels version 0.2-11 Index]