random_start

Appropriate simulation of starting values for tandom initialization of Gibbs Sampling. It start from the mle corresponding to no-group structure and then it randomly selects rescaled random support points (with sum 1) of G mixture components such that the marginal support coincides with the mle support for G=1
Random generation of starting values of the component-specific support parameters for Gibbs sampling

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

random_start function

<dl><dt>mlesupp</dt>
<dd>MLE of support parameters</dd>
<dt>givenweights</dt>
<dd>A numeric vector of \(G\) mixture weights</dd>
<dt>alpha</dt>
<dd>A numeric vector of \(G\) positive reals to be used as Dirichlet parameters for the random start which corresponds to a convex combination of \(G\) support parameter vertices</dd></dl>

Arguments

Cristina Mollica and Luca Tardella

Author

Appropriate simulation of starting values for tandom initialization of Gibbs Sampling. It start from the mle corresponding to no-group structure and then it randomly selects rescaled random support points (with sum 1) of G mixture components such that the marginal support coincides with the mle support for G=1
Random generation of starting values of the component-specific support parameters for Gibbs sampling — random_start

<dl>

<dt>mlesupp</dt>
<dd>MLE of support parameters</dd>


<dt>givenweights</dt>
<dd>A numeric vector of \(G\) mixture weights</dd>


<dt>alpha</dt>
<dd>A numeric vector of \(G\) positive reals to be used as Dirichlet parameters for the random start which corresponds to a convex combination of \(G\) support parameter vertices</dd>

</dl>

Appropriate simulation of starting values for tandom initialization of Gibbs Sampling. It start from the mle corresponding to no-group structure and then it randomly selects rescaled random support points (with sum 1) of G mixture components such that the marginal support coincides with the mle support for G=1
Random generation of starting values of the component-specific support parameters for Gibbs sampling

Description

Usage

Value

Arguments

Author