d_german

<p>The German Sample dataset (<code>d_german</code>) is part of a comparative cross-sectional study on political actions and mass participation involving five Western countries. The dataset regards a sample of \(N=2262\) German respondents who were asked to rank \(K=4\) political goals in order of desirability, namely: 1 = maintaining order in the nation, 2 = giving people more say in the decisions of government, 3 = fighting rising prices and 4 = protecting freedom of speech. The dataset is composed of complete orderings.</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.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.

Cristina Mollica

PLMIX

Bayesian Analysis of Finite Mixtures of Plackett-Luce Models for
Partial Rankings/Orderings

Luca Tardella

d_german function

<p>Object of S3 class <code>c("top_ordering","matrix")</code> gathering a matrix of complete orderings with \(N=2262\) rows and \(K=4\) columns. Each row lists the political goals from the most desiderable (<code>Rank_1</code>) to the least desiderable (<code>Rank_4</code>) for a given respondent.</p>

d_german: German Sample Data (complete orderings)

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