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

CalcGamma {textmineR} | R Documentation

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

### 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)

[Package textmineR version 3.0.5 Index]