These functions take a fitted sLDA model and predict the value of the response variable (or document-topic sums) for each given document.

1 2 3 4 5 | ```
slda.predict(documents, topics, model, alpha, eta,
num.iterations = 100, average.iterations = 50, trace = 0L)
slda.predict.docsums(documents, topics, alpha, eta,
num.iterations = 100, average.iterations = 50, trace = 0L)
``` |

`documents` |
A list of document matrices comprising a corpus, in the format
described in |

`topics` |
A |

`model` |
A fitted model relating a document's topic distribution to the
response variable. The |

`alpha` |
The scalar value of the Dirichlet hyperparameter for topic proportions. See references for details. |

`eta` |
The scalar value of the Dirichlet hyperparamater for topic multinomials. |

`num.iterations` |
Number of iterations of inference to perform on the documents. |

`average.iterations` |
Number of samples to average over to produce the predictions. |

`trace` |
When |

Inference is first performed on the documents by using Gibbs sampling
and holding the word-topic matrix *β_{w,k}* constant. Typically
for a well-fit model only a small number of iterations are required to
obtain good fits for new documents. These topic vectors are then
piped through `model`

to yield numeric predictions associated
with each document.

For `slda.predict`

, a numeric vector of the same length as
`documents`

giving the predictions. For `slda.predict.docsums`

, a
*K \times N* matrix of document assignment counts.

Jonathan Chang (slycoder@gmail.com)

Blei, David M. and McAuliffe, John. Supervised topic models. Advances in Neural Information Processing Systems, 2008.

See `lda.collapsed.gibbs.sampler`

for a description of the
format of the input data, as well as more details on the model.

See `predictive.distribution`

if you want to make
predictions about the contents of the documents instead of the
response variables.

1 2 | ```
## The sLDA demo shows an example usage of this function.
## Not run: demo(slda)
``` |

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