topicmodels (version 0.2-6)

TopicModel-class: 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.