topic_stability: Compute topic stability for over-optimal topic specifications
In contefranz/OpTop: Optimal topic specification for latent dirichlet allocation models

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

View source: R/topic_stability.R

Implements fast chi-square like test to evaluate the stability of redundant topics.

topic_stability(
  lda_models,
  optimal_model,
  q = 0.8,
  alpha = 0.05,
  do_plot = TRUE
)

`lda_models`	A list of ordered LDA models as estimated by `LDA`. The LDA models must be in ascending order according to the number of topics.
`optimal_model`	A number corresponding to the optimal topic model.
`q`	Set a cutoff for important words as the quantile of the expected cumulative probability of word weights. Default to 0.80, meaning that the function reaches 80% of the distribution mass and leaves out the remaining 20%.
`alpha`	Alpha level to identify informative words from the Cumulative Distribution Function over the cosine similarities in the Topic Word Weights matrix. Default to 0.05.
`do_plot`	Plot the chi-square statistic as a function of the number of topics. Default to `TRUE`. The horizontal dot-dashed line represents the significance level according to `alpha`.

This function implements Test 3 as defined in Lewis and Grossetti (2019). Test 3 evaluates the aggregated stability of over-optimal topic specifications by summing each point-wise contribution. See 'Value' to understand how topic_stability returns the results.

A 'data.table' containing the following columns:

`topic`	An integer giving the number of topics.
`df`	An integer giving the degrees of freedom.
`chisq`	A numeric giving the chi-square statistic.

Francesco Grossetti francesco.grossetti@unibocconi.it

Craig M. Lewis craig.lewis@owen.vanderbilt.edu

Lewis, C. and Grossetti, F. (2019 - forthcoming):
A Statistical Approach for Optimal Topic Model Identification.

LDA data.table

## Not run: 
test2 <- topic_stability( lda_models = lda_list,
                          optimal_model = test1,
                          q = 0.00075,
                          alpha = 0.05 )

## End(Not run)