binary_group_ind

Construct the binary group membership matrix from the multinomial classification vector.

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

binary_group_ind function

<dl><dt>class</dt>
<dd>Numeric vector of class memberships.</dd>
<dt>G</dt>
<dd>Number of possible different classes.</dd></dl>

Arguments

Cristina Mollica and Luca Tardella

Author

Binary group membership matrix — binary_group_ind

<dl>

<dt>class</dt>
<dd>Numeric vector of class memberships.</dd>


<dt>G</dt>
<dd>Number of possible different classes.</dd>

</dl>

binary_group_ind: Binary group membership matrix

Description

Usage

Value

Arguments

Author

Examples