tm-class: Virtual class "TopicModel"

Description Objects from the Class Slots Author(s)

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


topicmodels documentation built on May 30, 2017, 5:16 a.m.