rlbm

<code>rlbm</code> returns the adjacency matrix and the cluster labels generated randomly with a Latent Block Model.

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

rlbm function

<dl><dt>Nr</dt>
<dd>desired Number of rows</dd>
<dt>Nc</dt>
<dd>desired Number of column</dd>
<dt>pir</dt>
<dd>A numeric vector of length Kr with rows clusters proportions (will be normalized to sum up to 1).</dd>
<dt>pic</dt>
<dd>A numeric vector of length Kc with columns clusters proportions (will be normalized to sum up to 1).</dd>
<dt>mu</dt>
<dd>A numeric matrix of dim Kr x Kc with the connectivity pattern to generate. elements in [0,1].</dd></dl>

Arguments

Generate a data matrix using a Latent Block Model — rlbm

<dl>

<dt>Nr</dt>
<dd>desired Number of rows</dd>


<dt>Nc</dt>
<dd>desired Number of column</dd>


<dt>pir</dt>
<dd>A numeric vector of length Kr with rows clusters proportions (will be normalized to sum up to 1).</dd>


<dt>pic</dt>
<dd>A numeric vector of length Kc with columns clusters proportions (will be normalized to sum up to 1).</dd>


<dt>mu</dt>
<dd>A numeric matrix of dim Kr x Kc with the connectivity pattern to generate. elements in [0,1].</dd>

</dl>

rlbm: Generate a data matrix using a Latent Block Model

Description

Usage

Value

Arguments

Details

Examples