# TopicModel-class

From topicmodels v0.2-4
by Bettina Gruen

##### Virtual class "TopicModel"

Fitted topic model.

- Keywords
- classes

##### 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.

`"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.

`"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.

`"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.

`"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.

`"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.

*Documentation reproduced from package topicmodels, version 0.2-4, License: GPL-2*

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