plot,DiagGmmFit,missing-method

Plot a <code>DiagGmmFit-class</code> object

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Etienne C<c3><b4>me

greed

Clustering and Model Selection with the Integrated
Classification Likelihood

Etienne Côme

Nicolas Jouvin

plot,DiagGmmFit,missing-method function

<dl><dt>x</dt>
<dd>a <code>DiagGmmFit-class</code></dd>
<dt>type</dt>
<dd>a string which specify plot type:<ul>
<li><code>'marginals'</code>: plot the marginal densities</li>
<li><code>'violins'</code>: make a violin plot for each clusters and features</li>
</ul></dd></dl>

Arguments

Plot a DiagGmmFit-class object — plot,DiagGmmFit,missing-method

<dl>

<dt>x</dt>
<dd>a <code>DiagGmmFit-class</code></dd>


<dt>type</dt>
<dd>a string which specify plot type:<ul>
<li><code>'marginals'</code>: plot the marginal densities</li>
<li><code>'violins'</code>: make a violin plot for each clusters and features</li>
</ul></dd>

</dl>

Data engineering and BI courses are free!

plot,DiagGmmFit,missing-method: Plot a `DiagGmmFit-class` object

Description

Usage

Value

Arguments