R: topic variance

topicVar {maptpx} | R Documentation

## topic variance

### Description

Tools for looking at the variance of document-topic weights.

### Usage

    topicVar(counts, theta, omega)
    logit(prob)
    expit(eta)

### Arguments

`counts` | A matrix of multinomial response counts, as inputed to the `topics` or `predict.topics` functions.
`theta` | A fitted topic matrix, as ouput from the `topics` or `predict.topics` functions.
`omega` | A fitted document topic-weight matrix, as ouput from the `topics` or `predict.topics` functions.
`prob` | A probability vector (positive and sums to one) or a matrix with probability vector rows.
`eta` | A vector of the natural exponential family parameterization for a probability vector (with first category taken as null) or a matrix with each row the NEF parameters for a single observation.

### Details

These function use the natural exponential family (NEF) parametrization of a probability vector q_0 ... q_{K-1} with the first element corresponding to a 'null' category; that is, with NEF(q) = e_1 ... e_{K-1} and setting e_0 = 0, the probabilities are

q_k = \frac{exp[e_k]}{1 + ∑ exp[e_j]}.

Refer to Taddy (2012) for details.

### Value

`topicVar` returns an array with dimensions (K-1,K-1,n), where `K=ncol(omega)=ncol(theta)` and `n = nrow(counts) = nrow(omega)`, filled with the posterior covariance matrix for the NEF parametrization of each row of `omega`. Utility `logit` performs the NEF transformation and `expit` reverses it.

### 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, predict.topics

[Package maptpx version 1.9-7 Index]