R: Use the RTM to predict whether a link exists between two...

predictive.link.probability {lda} | R Documentation

## Use the RTM to predict whether a link exists between two documents.

### Description

This function takes a fitted LDA-type model (e.g., LDA or RTM) and makes predictions about the likelihood of a link existing between pairs of documents.

### Usage

    predictive.link.probability(edgelist, document_sums, alpha, beta)

### Arguments

`edgelist` | A two-column integer matrix where each row represents an edge on which to make a prediction. An edge is expressed as a pair of integer indices (1-indexed) into the columns (i.e., documents) of document_sums (see below).
`document_sums` | A K \times D matrix where each entry is a numeric proportional to the probability of seeing a topic (row) conditioned on document (column) (this entry is sometimes denoted θ_{d,k} in the literature, see details). The document_sums field or the document_expects field from the output of `lda.collapsed.gibbs.sampler` and `rtm.collapsed.gibbs.sampler` can be used.
`alpha` | The value of the Dirichlet hyperparamter generating the distribution over document_sums. This, in effect, smooths the similarity between documents.
`beta` | A numeric vector of regression weights which is used to determine the similarity between two vectors (see details). Arguments will be recycled to create a vector of length `dim(document_sums)[1]`.

### Details

Whether or not a link exists between two documents i and j is a function of the weighted inner product of the `document_sums[,i]` and `document_sums[,j]`. After normalizing `document_sums` column-wise, this inner product is weighted by beta.

This quantity is then passed to a link probability function. Like `rtm.collapsed.gibbs.sampler` in this package, only the exponential link probability function is supported. Note that quantities are automatically scaled to be between 0 and 1.

### Value

A numeric vector of length `dim(edgelist)[1]`, representing the probability of a link existing between each pair of documents given in the edge list.

### 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

`rtm.collapsed.gibbs.sampler` for the format of document_sums. `links.as.edgelist` produces values for edgelist. `predictive.distribution` makes predictions about document content instead.

### Examples

    ## See demo.

    ## Not run: demo(rtm)

[Package lda version 1.4.2 Index]