rtm.collapsed.gibbs.sampler {lda} | R Documentation |
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.
rtm.collapsed.gibbs.sampler(documents, links, K, vocab, num.iterations, alpha, eta, beta)
documents |
A collection of documents in LDA format. See
lda.collapsed.gibbs.sampler for details.
|
links |
A list representing the connections between the documents. This
list should be of the same length as the documents. Each
element, links[[i]] , is an integer vector expressing connections
between document i and the 0-indexed documents pointed to by the
elements of the vector.
|
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. |
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 K numeric of regression coefficients expressing the
relationship between each topic and the probability of link.
|
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.
A fitted model as a list with the same components as returned by
lda.collapsed.gibbs.sampler
.
Jonathan Chang (jcone@princeton.edu)
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.
## See demo. ## Not run: demo(rtm)