fitLDA: Fit LDA model via Gibbs sampler

In kshirley/LDAtools: Tools to fit a topic model using Latent Dirichlet Allocation (LDA)

Description

This function implements the Gibbs sampling method described by Griffiths and Steyvers (2004). The Gibbs sampler portion of the function is a call to C code. Note that we only return the latent topic assignments (for each token) from the last iteration. Thus, memory limitations aren't really an issue. However, the run time is O(num.chains*n.iter*N*k) where n.chains is number of MCMC chains, n.iter is the number of iterations, N is the total number of tokens in the data, and k is the number of topics. It is possible to resume a Gibbs sampler from a previous fit by using the topics from that fit to initiate the next set of iterations using topics.init.

Usage

fitLDA(word.id = integer(), doc.id = integer(), k = 10, n.chains = 1,
  n.iter = 1000, topics.init = NULL, alpha = 0.01, beta = 0.01)

Arguments

word.id	Unique token ID. Can be taken directly from the output of filter.
doc.id	Unique document ID. Can be taken directly from the output of filter.
k	number of topics.
n.chains	number of MCMC chains.
n.iter	number of iterations.
topics.init	A vector of topics to initially assign. The Markov property of MCMC allows one to input the topic assignments from the last iteration of a previous model fit. Note that this vector should be the same length of the word.id vector times the number of chains.
alpha	Dirichlet hyperparameter
beta	Dirichlet hyperparameter

Value

A list of length two. The first element is the sampled latent topic value from the last iteration (for each token). The second element is a vector with the log-likelihood values for every iteration of the gibbs sampler.

References

Griffiths and Steyvers (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences. 101: 5228-5235.

Examples

data(APinput)
#takes a while
## Not run: o <- fitLDA(APinput$word.id, APinput$doc.id, k=20)

kshirley/LDAtools documentation built on May 20, 2019, 7:03 p.m.