Marginal content words by one content covariate

For a multi-covariate (crossed) content model, recovers the topic-word labels for each level of a single content covariate, averaging the crossed topic-word distributions over the other covariate(s). Lets you read off how topics' vocabulary shifts with one covariate while marginalizing the rest.

Usage

content_topics(model, by = NULL, n = 7L, type = c("prob", "lift", "frex"))

Arguments

model

A content (SAGE) faSTM fit.

by

Content covariate name to marginalize to (default: the first).

n

Words per topic.

type

"prob", "lift", or "frex".

Value

A named list (one entry per level of by) of K x n word matrices.