plot,mmpcaClust,missing-method

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

Either:<ul>
<li>'bound': plot the lower bound evolution during
the greedy procedure. See <code><a rd-options="" href="/link/plot_bound?package=MoMPCA&version=1.0.1" data-mini-rdoc="MoMPCA::plot_bound">plot_bound</a></code> documention for more details.</li>
</ul>

type

optional argument specifying the number of words to display and
the entropy correction to apply when calling <code>plot_topics</code>().

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,mmpcaClust,missing-method function

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

Either:<ul>
<li>'bound': plot the lower bound evolution during
the greedy procedure. See <code><a rd-options='' href='plot_bound'>plot_bound</a></code> documention for more details.</li>
</ul>

plot,mmpcaClust,missing-method: Plot function for object mmpcaClust

Description

Usage

Arguments

Value