R: Collapsed Gibbs Sampling for the Relational Topic Model...

rtm.collapsed.gibbs.sampler {lda} | R Documentation

## Collapsed Gibbs Sampling for the Relational Topic Model (RTM).

### Description

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.

### Usage

    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)

### Arguments

`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.
`num.e.iterations` | For `rtm.em`, the number of iterations in each Gibbs sampling E-step.
`num.m.iterations` | For `rtm.em`, the number of M-step iterations.
`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.
`lambda` | For `rtm.em`, the regularization parameter used when estimating beta. lambda expresses the number of non-links to simulate among all possible connections between documents.
`initial.beta` | For `rtm.em`, an initial value of `beta` at which to start the EM process.
`trace` | When `trace` is greater than zero, diagnostic messages will be output. Larger values of `trace` imply more messages.
`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.

### Details

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 EM-wrapper around `rtm.collapsed.gibbs.sampler` which iteratively estimates the regression parameters `beta`.

### Value

A fitted model as a list with the same components as returned by `lda.collapsed.gibbs.sampler`.

### Author(s)

Jonathan Chang (slycoder@gmail.com)

### References

Chang, Jonathan and Blei, David M. Relational Topic Models for Document Networks. Artificial intelligence and statistics. 2009.

### See Also

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.

### Examples

    ## See demo.

    ## Not run: demo(rtm)

[Package lda version 1.4.2 Index]