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.