topicVar: topic variance
In TaddyLab/maptpx: MAP Estimation of Topic Models

Description Usage Arguments Details Value Author(s) References See Also

View source: R/topics.R

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

1
2
3

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

`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.

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.

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.

Matt Taddy mataddy@gmail.com

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

topics, predict.topics

TaddyLab/maptpx documentation built on May 29, 2020, 7:25 a.m.