print.summary.gsPLMIX

<code>print</code> method for class <code>summary.gsPLMIX</code>. It shows some general information on the Gibbs sampling simulation of a Bayesian mixture of Plackett-Luce models.

Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi:10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi:10.1002/sim.6224>.

Cristina Mollica

PLMIX

Bayesian Analysis of Finite Mixture of Plackett-Luce Models

Luca Tardella

print.summary.gsPLMIX function

<dl><dt>x</dt>
<dd>Object of class <code>summary.gsPLMIX</code> returned by the <code>summary.gsPLMIX</code> function.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods (not used).</dd></dl>

Arguments

Cristina Mollica and Luca Tardella

Author

Print of the summary of Gibbs sampling simulation of a Bayesian mixture of Plackett-Luce models. — print.summary.gsPLMIX

<dl>

<dt>x</dt>
<dd>Object of class <code>summary.gsPLMIX</code> returned by the <code>summary.gsPLMIX</code> function.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods (not used).</dd>

</dl>

print.summary.gsPLMIX: Print of the summary of Gibbs sampling simulation of a Bayesian mixture of Plackett-Luce models.

Description

Usage

Arguments

Author

References