`R/topic_modeling_core.R`

`CalcGamma.Rd`

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.

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

- 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.

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)

```
# 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)
```