topic_match: Define informative and uninformative components

In contefranz/OpTop: Optimal topic specification for latent dirichlet allocation models

Description

For above-optimal topics, detect and extract informative and uninformative components in terms of cosine similarities with the optimal topic model.

Usage

topic_match(lda_models, optimal_model, var_correction = TRUE)

Arguments

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.
var_correction	Use the unbiased estimator of the co-variance for i.i.d. observations by applying n - 1 in the denominator. Default is TRUE.

Value

A named list with the informative and uninformative components given as matrices.

Examples

## Not run: 
out3 <- topic_match( lda_models = lda_list, 
                     optimal_model = test1, 
                     var_correction = TRUE )

## End(Not run)

contefranz/OpTop documentation built on Feb. 14, 2022, 7:04 p.m.