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. Seeloglikelihood
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.
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