tf_df_dist: Calculate the distance between token and document frequencies

Description Usage Arguments Value References Examples

View source: R/tf_df_dist.R

Description

Using the the N highest probability tokens for each topic, calculate the Hellinger distance between the token frequencies and the document frequencies

Usage

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tf_df_dist(topic_model, dtm_data, top_n_tokens = 10)

Arguments

topic_model

a fitted topic model object from one of the following: tm-class

dtm_data

a document-term matrix of token counts coercible to simple_triplet_matrix

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

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# 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,])

topicdoc documentation built on Oct. 30, 2019, 11:26 a.m.