Description Usage Arguments Value References Examples
Using the the N highest probability tokens for each topic, calculate the Hellinger distance between the token frequencies and the document frequencies
1  tf_df_dist(topic_model, dtm_data, top_n_tokens = 10)

topic_model 
a fitted topic model object from one of the following:

dtm_data 
a documentterm matrix of token counts coercible to 
top_n_tokens 
an integer indicating the number of top words to consider, the default is 10 
A vector of distances with length equal to the number of topics in the fitted model
Jordan BoydGraber, David Mimno, and David Newman, 2014. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. CRC Handbooks ofModern Statistical Methods. CRC Press, Boca Raton, Florida.
1 2 3 4 5  # Using the example from the LDA function
library(topicmodels)
data("AssociatedPress", package = "topicmodels")
lda < LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2)
tf_df_dist(lda, AssociatedPress[1:20,])

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