CalcProbCoherence: Probailistic coherence of topics
In ChengMengli/topic: Functions for Text Mining and Topic Modeling

Description Usage Arguments Value Examples

Calculates the probabilistic coherence of a topic or topics. This approximates semmantic coherence or human understandability of a topic.

1	CalcProbCoherence(phi, dtm, M = 5)

`phi`	A numeric matrix or a numeric vector. The vector, or rows of the matrix represent the numeric relationship between topic(s) and terms. For example, this relationship may be p(word\|topic) or p(topic\|word).
`dtm`	A document term matrix or co-occurence matrix of class `matrix` or whose class inherits from the `Matrix` packge. Columns must index terms.
`M`	An integer for the number of words to be used in the calculation. Defaults to 5

Returns an object of class numeric corresponding to the probabilistic coherence of the input topic(s).

# Load a pre-formatted dtm and topic model
data(nih_sample_topic_model)
data(nih_sample_dtm) 

CalcProbCoherence(phi = nih_sample_topic_model$phi, dtm = nih_sample_dtm, M = 5)