slda.predict {lda} | R Documentation |
Predict the response variable of documents using an sLDA model.
Description
These functions take a fitted sLDA model and predict the value of the response variable (or document-topic sums) for each given document.
Usage
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)
Arguments
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 model field from the output of
|
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 |
Details
Inference is first performed on the documents by using Gibbs sampling
and holding the word-topic matrix \beta_{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.
Value
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.
Author(s)
Jonathan Chang (slycoder@gmail.com)
References
Blei, David M. and McAuliffe, John. Supervised topic models. Advances in Neural Information Processing Systems, 2008.
See Also
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.
Examples
## The sLDA demo shows an example usage of this function.
## Not run: demo(slda)