plot_topics

An S4 object of class <code><a href="/link/?package=MoMPCA&version=1.0.1" mmpcaclust'="" data-mini-rdoc="MoMPCA::"></a>'>mmpcaClust</code>

an entropy correction parameter for the topic matrix. It is applied
to the beta matrix before sorting the words by highest probability. The
greater, the more emphasis is put towards words contributing a lot to the
entropy of a topic. Set s=1 to ignore.

the number of words to display per topic.

n_words

Cluster any count data matrix with a fixed number of variables, such as document/term matrices. It integrates the dimension reduction aspect of topic models in the mixture models framework. Inference is done by means of a greedy Classification Variational Expectation Maximisation (C-VEM) algorithm. An Integrated Classication Likelihood (ICL) model selection is designed for selecting the latent dimension (number of topics) and the number of clusters. For more details, see the article of Jouvin et. al. (2020) <arxiv:1909.00721>.

Nicolas Jouvin

MoMPCA

Inference and Clustering for Mixture of Multinomial Principal
Component Analysis

plot_topics function

An S4 object of class <code><a href='<a href='mmpcaClust'></a>'>mmpcaClust</a></code>

plot_topics: plot_topics

Description

Usage

Arguments

Value