merge_topics

Combine two or more topics in a multinomial topic
 model fit.

Implements fast, scalable optimization algorithms for
fitting topic models ("grade of membership" models) and
non-negative matrix factorizations to count data. The methods
exploit the special relationship between the multinomial topic
model (also, "probabilistic latent semantic indexing") and Poisson
non-negative matrix factorization. The package provides tools to
compare, annotate and visualize model fits, including functions to
efficiently create "structure plots" and identify key features in
topics. The 'fastTopics' package is a successor to the
'CountClust' package. For more information, see
<doi:10.48550/arXiv.2105.13440> and
<doi:10.1186/s13059-023-03067-9>. Please also see the GitHub
repository for additional vignettes not included in the package on
CRAN.

Peter Carbonetto

fastTopics

Fast Algorithms for Fitting Topic Models and Non-Negative Matrix
Factorizations to Count Data

Kevin Luo

Kushal Dey

Joyce Hsiao

Abhishek Sarkar

Anthony Hung

Xihui Lin

Paul C. Boutros

Minzhe Wang

Tracy Ke

Eric Weine

Matthew Stephens

merge_topics function

<dl><dt>fit</dt>
<dd>A multinomial topic model fit.</dd>
<dt>k</dt>
<dd>The names or numbers of the topics to be combined. Two or
more topics should be chosen.</dd></dl>

Arguments

Combine Topics in Multinomial Topic Model — merge_topics

<dl>

<dt>fit</dt>
<dd>A multinomial topic model fit.</dd>


<dt>k</dt>
<dd>The names or numbers of the topics to be combined. Two or
more topics should be chosen.</dd>

</dl>

Combine Topics in Multinomial Topic Model

merge_topics: Combine Topics in Multinomial Topic Model

Description

Usage

Value

Arguments

Details