d_rice

<p>The Rice Voting dataset (<code>d_rice</code>) collects the results of the 1992
election of a faculty member to serve on the Presidential Search Committee
in the Rice University. A total of \(N=300\) people casted their vote in
the ballots by ranking the \(K=5\) candidates in the short list in a
preferential manner. The dataset is composed of partial top orderings of
varying lengths. Missing positions are denoted with zero entries.</p>

datasets

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

d_rice function

<p>Object of S3 class <code>c("top_ordering","matrix")</code> gathering a
matrix of partial orderings with \(N=300\) rows and \(K=5\) columns.
Each row lists the faculty members from the most-liked (<code>Rank_1</code>) to
the least-liked (<code>Rank_5</code>) in a given voting ballot.</p>

d_rice: Rice Voting Data (partial orderings)

Description

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Format

References

Examples