DiagGmmFit-class

An S4 class to represent a fit of a multivariate diagonal Gaussian mixture model, extend <code>IclFit-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

DiagGmmFit-class function

<dl>
<dt><code>model</code></dt>
<dd>a <code>DiagGmm-class</code> object to store the model fitted</dd></dl>
<dt><code>name</code></dt>
<dd>generative model name</dd>
<dt><code>icl</code></dt>
<dd>icl value of the fitted model</dd>
<dt><code>K</code></dt>
<dd>number of extracted clusters over row and columns</dd>
<dt><code>cl</code></dt>
<dd>a numeric vector with row and columns cluster indexes</dd>
<dt><code>obs_stats</code></dt>
<dd>a list with the following elements:<ul>
<li>counts: numeric vector of size K with number of elements in each clusters</li>
<li>regs: list of size $K$ with statistics for each clusters</li>
</ul></dd>
<dt><code>move_mat</code></dt>
<dd>binary matrix which store move constraints</dd>
<dt><code>train_hist</code></dt>
<dd>data.frame with training history information (details depends on the training procedure)</dd>

Slots

Diagonal Gaussian mixture model fit results class — DiagGmmFit-class

<dl>
<dt><code>model</code></dt>
<dd>a <code>DiagGmm-class</code> object to store the model fitted</dd>


<dt><code>name</code></dt>
<dd>generative model name</dd>


<dt><code>icl</code></dt>
<dd>icl value of the fitted model</dd>


<dt><code>K</code></dt>
<dd>number of extracted clusters over row and columns</dd>


<dt><code>cl</code></dt>
<dd>a numeric vector with row and columns cluster indexes</dd>


<dt><code>obs_stats</code></dt>
<dd>a list with the following elements:<ul>
<li>counts: numeric vector of size K with number of elements in each clusters</li>
<li>regs: list of size $K$ with statistics for each clusters</li>
</ul></dd>


<dt><code>move_mat</code></dt>
<dd>binary matrix which store move constraints</dd>


<dt><code>train_hist</code></dt>
<dd>data.frame with training history information (details depends on the training procedure)</dd>


</dl>

Data engineering and BI courses are free!

DiagGmmFit-class: Diagonal Gaussian mixture model fit results class

Description

Arguments

Slots

See Also