heat_plot

Generates a heat plot with items in their consensus ordering along the
horizontal axis and ranking along the vertical axis. The color denotes
posterior probability.

An implementation of the Bayesian version of the Mallows rank model
(Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>;
Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>;
Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings
and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
posterior distributions of parameters are provided. The package also provides functions for estimating
the partition function (normalizing constant) of the Mallows rank model, both with the importance
sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
(Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

Oystein Sorensen

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

Waldir Leoncio

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

heat_plot function

<dl><dt>model_fit</dt>
<dd>An object of type <code>BayesMallows</code>, returned from
<code>compute_mallows</code>.</dd>
<dt>burnin</dt>
<dd>A numeric value specifying the number of iterations
to discard as burn-in. Defaults to <code>x$burnin</code>, and must be
provided if <code>x$burnin</code> does not exist. See <code>assess_convergence</code>.</dd>
<dt>...</dt>
<dd>Additional arguments passed on to other methods. In particular,
<code>type = "CP"</code> or <code>type = "MAP"</code> can be passed on to
<code>compute_consensus</code> to determine the order of items along the
horizontal axis.</dd></dl>

Arguments

Heat plot of posterior probabilities — heat_plot

<dl>

<dt>model_fit</dt>
<dd>An object of type <code>BayesMallows</code>, returned from
<code>compute_mallows</code>.</dd>


<dt>burnin</dt>
<dd>A numeric value specifying the number of iterations
to discard as burn-in. Defaults to <code>x$burnin</code>, and must be
provided if <code>x$burnin</code> does not exist. See <code>assess_convergence</code>.</dd>


<dt>...</dt>
<dd>Additional arguments passed on to other methods. In particular,
<code>type = "CP"</code> or <code>type = "MAP"</code> can be passed on to
<code>compute_consensus</code> to determine the order of items along the
horizontal axis.</dd>

</dl>

heat_plot: Heat plot of posterior probabilities

Description

Usage

Value

Arguments

See Also

Examples