Fitted topic model.
Objects of class "LDA" are returned by LDA() and
of class "CTM" by CTM().
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.
Bettina Gruen