CalcGamma: Calculate a matrix whose rows represent P(topic_i|tokens)

In textmineR: Functions for Text Mining and Topic Modeling

Description

This function takes a phi matrix (P(token|topic)) and a theta matrix (P(topic|document)) and returns the phi prime matrix (P(topic|token)). Phi prime can be used for classifying new documents and for alternative topic labels.

Usage

CalcGamma(phi, theta, p_docs = NULL, correct = TRUE)

Arguments

phi	The phi matrix whose rows index topics and columns index words. The i, j entries are P(word_i | topic_j)
theta	The theta matrix whose rows index documents and columns index topics. The i, j entries are P(topic_i | document_j)
p_docs	A numeric vector of length nrow(theta) that is proportional to the number of terms in each document. This is an optional argument. It defaults to NULL
correct	Logical. Do you want to set NAs or NaNs in the final result to zero? Useful when hitting computational underflow. Defaults to TRUE. Set to FALSE for troubleshooting or diagnostics.

Value

Returns a matrix whose rows correspond to topics and whose columns correspond to tokens. The i,j entry corresponds to P(topic_i|token_j)

Examples

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

# Make a gamma matrix, P(topic|words)
gamma <- CalcGamma(phi = nih_sample_topic_model$phi, 
                   theta = nih_sample_topic_model$theta)

textmineR documentation built on June 28, 2021, 9:08 a.m.