assign_cluster

An object of type <code>BayesMallows</code>, returned from
<code><a rd-options="" href="/link/compute_mallows?package=BayesMallows&version=0.3.0" data-mini-rdoc="BayesMallows::compute_mallows">compute_mallows</a></code>.

model_fit

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=0.3.0" data-mini-rdoc="BayesMallows::assess_convergence">assess_convergence</a></code>.

burnin

A logical specifying whether to perform soft or
hard clustering. If <code>soft=TRUE</code>, all cluster probabilities
are returned, whereas if <code>soft=FALSE</code>, only the maximum a
posterior (MAP) cluster probability is returned, per assessor. In the
case of a tie between two or more cluster assignments, a random cluster
is taken as MAP estimate.

soft

A logical specifying whether or not to expand the rowset
of each assessor to also include clusters for which the assessor has
0 a posterior assignment probability. Only used when <code>soft = TRUE</code>. Defaults
to <code>FALSE</code>.

expand

Assign assessors to clusters by finding the cluster with highest
posterior probability.

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <http://jmlr.org/papers/v18/15-481.html>; Crispino et al., to appear in Annals of Applied Statistics). Both 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

assign_cluster function

An object of type <code>BayesMallows</code>, 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>.

assign_cluster: Assign Assessors to Clusters

Description

Usage

Arguments

Value

See Also