Fit a generative topic model which accounts for both the words which occur in a collection of documents as well as the links between the documents.
1 2 3 4 5 6 7  rtm.collapsed.gibbs.sampler(documents, links, K, vocab, num.iterations,
alpha, eta, beta, trace = 0L, test.start = length(documents) + 1L)
rtm.em(documents, links, K, vocab, num.e.iterations, num.m.iterations,
alpha, eta,
lambda = sum(sapply(links, length))/(length(links) * (length(links) 1)/2),
initial.beta = rep(3, K), trace = 0L,
test.start = length(documents) + 1L, tempering = 0.0)

documents 
A collection of documents in LDA format. See

links 
A list representing the connections between the documents. This
list should be of the same length as the documents. Each
element, 
K 
A scalar integer indicating the number of latent topics for the model. 
vocab 
A character vector specifying the vocabulary words associated with the word indices used in documents. 
num.iterations 
The number of sweeps of Gibbs sampling over the entire corpus to make. 
num.e.iterations 
For 
num.m.iterations 
For 
alpha 
The scalar value of the Dirichlet hyperparameter for topic proportions. 
eta 
The scalar value of the Dirichlet hyperparamater for topic multinomials. 
beta 
A length 
lambda 
For 
initial.beta 
For 
trace 
When 
test.start 
Internal use only. 
tempering 
A numeric between 0 and 1 indicating how newly computed parameters should be averaged with the previous iterations parameters. By default, the new values are used directly and the old value discarded. When set to 1, the new values are ignored and the initial values retained indefinitely. 
The Relational Topic Model uses LDA to model the content of documents but adds connections between documents as dependent on the similarity of the distribution of latent topic assignments. (See reference for details).
Only the exponential link probability function is implemented here. Note that the collapsed Gibbs sampler is different than the variational inference procedure proposed in the paper and is extremely experimental.
rtm.em
provides an EMwrapper around
rtm.collapsed.gibbs.sampler
which iteratively estimates the
regression parameters beta
.
A fitted model as a list with the same components as returned by
lda.collapsed.gibbs.sampler
.
Jonathan Chang (slycoder@gmail.com)
Chang, Jonathan and Blei, David M. Relational Topic Models for Document Networks. Artificial intelligence and statistics. 2009.
See lda.collapsed.gibbs.sampler
for a description of the
input formats and similar models.
nubbi.collapsed.gibbs.sampler
is a different kind of
model for document networks.
predictive.link.probability
makes predictions based on
the output of this model.
1 2 3  ## See demo.
## Not run: demo(rtm)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
All documentation is copyright its authors; we didn't write any of that.