mmpcaClustcontrol-class

An S4 class for <code><a rd-options="" href="/link/mmpca_clust?package=MoMPCA&version=1.0.1" data-mini-rdoc="MoMPCA::mmpca_clust">mmpca_clust</a></code>(). It is mainly a
 wrapper around the class <code><a href="/link/?package=MoMPCA&version=1.0.1" topicmodelcontrol'="" data-mini-rdoc="MoMPCA::"></a>'>TopicModelcontrol</code>
 (specifically: LDA_VEMcontrol).

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

mmpcaClustcontrol-class function

<dl class="dl-horizontal">
<dt><code>control_lda_init</code></dt><dd>Object of class <code>"LDA_VEMcontrol"</code>; specifies the
controls of the VEM algorithm used for the initialization of beta.</dd></dl><dt><code>control_lda_loop</code></dt><dd>Object of class <code>"LDA_VEMcontrol"</code>; specifies the
controls for the VEM algorithm used after a swap in the branch &amp; bound.</dd>

Slots

An S4 class for <code><a rd-options='' href='mmpca_clust'>mmpca_clust</a></code>(). It is mainly a
 wrapper around the class <code><a href='<a href='TopicModelcontrol'></a>'>TopicModelcontrol</a></code>
 (specifically: LDA_VEMcontrol).

mmpcaClustcontrol-class: mmpcaClustcontrol

Description

Arguments

Slots