compute_consensus.BayesMallows

An object 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>.

model_fit

Character string specifying which consensus to compute. Either
<code>"CP"</code> or <code>"MAP"</code>. Defaults to <code>"CP"</code>.

type

A numeric value specifying the number of iterations
to discard as burn-in. Defaults to <code>model_fit$burnin</code>, and must be
provided if <code>model_fit$burnin</code> does not exist. See <code><a rd-options="" href="/link/assess_convergence?package=BayesMallows&version=1.1.0" data-mini-rdoc="BayesMallows::assess_convergence">assess_convergence</a></code>.

burnin

Character string defining the parameter for which to compute the
consensus. Defaults to <code>"rho"</code>. Available options are <code>"rho"</code> and <code>"Rtilde"</code>,
with the latter giving consensus rankings for augmented ranks.

parameter

When <code>parameter = "rho"</code>, this integer vector is used to
define the assessors for which to compute the augmented ranking. Defaults to
<code>1L</code>, which yields augmented rankings for assessor 1.

assessors

other arguments passed to methods.

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

compute_consensus.BayesMallows function

An object returned from <code><a rd-options='' href='compute_mallows'>compute_mallows</a></code>.

A numeric value specifying the number of iterations
to discard as burn-in. Defaults to <code>model_fit$burnin</code>, and must be
provided if <code>model_fit$burnin</code> does not exist. See <code><a rd-options='' href='assess_convergence'>assess_convergence</a></code>.

compute_consensus.BayesMallows: Compute Consensus Ranking

Description

Usage

Arguments