plot,DcLbmFit,missing-method

Plot a <code>DcLbmFit-class</code>

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,DcLbmFit,missing-method function

<dl><dt>x</dt>
<dd>a <code>DcLbmFit-class</code></dd>
<dt>type</dt>
<dd>a string which specify plot type:<ul>
<li><code>'blocks'</code>: plot a block matrix with summarizing connections between row and column clusters</li>
<li><code>'nodelink'</code>: plot a nodelink diagram of the bipartite graph summarizing connections between row and column clusters</li>
</ul></dd></dl>

Arguments

Plot a DcLbmFit-class — plot,DcLbmFit,missing-method

<dl>

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


<dt>type</dt>
<dd>a string which specify plot type:<ul>
<li><code>'blocks'</code>: plot a block matrix with summarizing connections between row and column clusters</li>
<li><code>'nodelink'</code>: plot a nodelink diagram of the bipartite graph summarizing connections between row and column clusters</li>
</ul></dd>

</dl>

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plot,DcLbmFit,missing-method: Plot a `DcLbmFit-class`

Description

Usage

Value

Arguments