paired_comparisons

Construct the paired comparison matrix for a partial ordering/ranking dataset.

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

paired_comparisons function

<dl><dt>data</dt>
<dd>Numeric \(N\)\(\times\)\(K\) data matrix of partial sequences.</dd>
<dt>format_input</dt>
<dd>Character string indicating the format of the <code>data</code> input, namely <code>"ordering"</code> or <code>"ranking"</code>.</dd>
<dt>nranked</dt>
<dd>Optional numeric vector of length \(N\) with the number of items ranked by each sample unit.</dd></dl>

Arguments

Cristina Mollica and Luca Tardella

Author

Paired comparison matrix for a partial ordering/ranking dataset — paired_comparisons

<dl>

<dt>data</dt>
<dd>Numeric \(N\)\(\times\)\(K\) data matrix of partial sequences.</dd>


<dt>format_input</dt>
<dd>Character string indicating the format of the <code>data</code> input, namely <code>"ordering"</code> or <code>"ranking"</code>.</dd>


<dt>nranked</dt>
<dd>Optional numeric vector of length \(N\) with the number of items ranked by each sample unit.</dd>

</dl>

paired_comparisons: Paired comparison matrix for a partial ordering/ranking dataset

Description

Usage

Value

Arguments

Author

References

See Also

Examples