LcaPrior-class

Lca-class

LcaPrior

An S4 class to represent a Latent Class Analysis model
Such model can be used to cluster a data.frame $X$ with several columns of factors with the following generative model :
$$\pi \sim \textrm{Dirichlet}(\alpha),$$
$$\forall k, \forall j, \quad \theta_{kj} \sim \textrm{Dirichlet}_{d_j}(\beta),$$
$$Z_i \sim \mathcal{M}_K(1,\pi),$$
$$\forall j=1, \ldots, p, \quad X_{ij}|Z_{ik}=1 \sim \mathcal{M}_{d_j}(1, \theta_{kj}),$$
These classes mainly store the prior parameters value ($\alpha,\beta$) of this generative model.
The <code>Lca-class</code> must be used when fitting a simple Latent Class Analysis whereas the <code>LcaPrior-class</code> must be used when fitting a <code>CombinedModels-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

Lca function

<dl><dt>beta</dt>
<dd>Dirichlet prior parameter for all the categorical feature (default to 1)</dd>
<dt>alpha</dt>
<dd>Dirichlet prior parameter over the cluster proportions (default to 1)</dd></dl>

Arguments

Latent Class Analysis Model Prior class — Lca

<dl>

<dt>beta</dt>
<dd>Dirichlet prior parameter for all the categorical feature (default to 1)</dd>


<dt>alpha</dt>
<dd>Dirichlet prior parameter over the cluster proportions (default to 1)</dd>

</dl>

Lca: Latent Class Analysis Model Prior class

Description

Usage

Value

Arguments

See Also

Examples