R: topic predict

predict.topics {maptpx} | R Documentation

## topic predict

### Description

Predict function for Topic Models

### Usage

    ## S3 method for class 'topics'
    predict( object, newcounts, loglhd=FALSE, ... )

### Arguments

`object` | An output object from the `topics` function, or the corresponding matrix of estimated topics.
`newcounts` | An `nrow(object$theta)`-column matrix of multinomial phrase/category counts for new documents/observations. Can be either a simple `matrix` or a `simple_triplet_matrix`.
`loglhd` | Whether or not to calculate and return `sum(x*log(p))`, the un-normalized log likelihood.
`...` | Additional arguments to the undocumented internal `tpx*` functions.

### Details

Under the default mixed-membership topic model, this function uses sequential quadratic programming to fit topic weights Ω for new documents. Estimates for each new ω_i are, conditional on `object$theta`, MAP in the (K-1)-dimensional logit transformed parameter space.

### Value

The output is an `nrow(newcounts)` by `object$K` matrix of document topic weights, or a list with including these weights as `W` and the log likelihood as `L`.

### Author(s)

Matt Taddy mataddy@gmail.com

### References

Taddy (2012), On Estimation and Selection for Topic Models. http://arxiv.org/abs/1109.4518

### See Also

topics, plot.topics, summary.topics, congress109

### Examples

    ## Simulate some data
    omega <- t(rdir(500, rep(1/10,10)))
    theta <- rdir(10, rep(1/1000,1000))
    Q <- omega%*%t(theta)
    counts <- matrix(ncol=1000, nrow=500)
    totals <- rpois(500, 200)
    for(i in 1:500){ counts[i,] <- rmultinom(1, size=totals[i], prob=Q[i,]) }

    ## predict omega given theta
    W <- predict.topics( theta, counts )
    plot(W, omega, pch=21, bg=8)

[Package maptpx version 1.9-7 Index]