tf_df_dist {topicdoc} | R Documentation |
Calculate the distance between token and document frequencies
Description
Using the the N highest probability tokens for each topic, calculate the Hellinger distance between the token frequencies and the document frequencies
Usage
tf_df_dist(topic_model, dtm_data, top_n_tokens = 10)
Arguments
topic_model |
a fitted topic model object from one of the following:
|
dtm_data |
a document-term matrix of token counts coercible to |
top_n_tokens |
an integer indicating the number of top words to consider, the default is 10 |
Value
A vector of distances with length equal to the number of topics in the fitted model
References
Jordan Boyd-Graber, 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.
Examples
# 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,])
[Package topicdoc version 0.1.1 Index]