assess_convergence

A fitted model object of class <code>BayesMallows</code> returned from
<code><a rd-options="" href="/link/compute_mallows?package=BayesMallows&version=1.1.0" data-mini-rdoc="BayesMallows::compute_mallows">compute_mallows</a></code> or an object of class <code>BayesMallowsMixtures</code>
returned from <code><a rd-options="" href="/link/compute_mallows_mixtures?package=BayesMallows&version=1.1.0" data-mini-rdoc="BayesMallows::compute_mallows_mixtures">compute_mallows_mixtures</a></code>.

model_fit

Character string specifying which parameter to plot. Available
options are <code>"alpha"</code>, <code>"rho"</code>, <code>"Rtilde"</code>,
<code>"cluster_probs"</code>, or <code>"theta"</code>.

parameter

The items to study in the diagnostic plot for <code>rho</code>. Either
a vector of item names, corresponding to <code>model_fit$items</code> or a
vector of indices. If NULL, five items are selected randomly. Only used when
<code>parameter = "rho"</code> or <code>parameter = "Rtilde"</code>.

items

Numeric vector specifying the assessors to study in
the diagnostic plot for <code>"Rtilde"</code>.

assessors

<code>assess_convergence</code> provides trace plots for the parameters of the
Mallows Rank model, in order to study the convergence of the Metropolis-Hastings
algorithm.

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>). 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

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

Waldir Leoncio

assess_convergence function

A fitted model object of class <code>BayesMallows</code> returned from
<code><a rd-options='' href='compute_mallows'>compute_mallows</a></code> or an object of class <code>BayesMallowsMixtures</code>
returned from <code><a rd-options='' href='compute_mallows_mixtures'>compute_mallows_mixtures</a></code>.

assess_convergence: Trace Plots from Metropolis-Hastings Algorithm

Description

Usage

Arguments

See Also