set_initial_values

Set initial values used by 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>;
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

set_initial_values function

<dl><dt>rho_init</dt>
<dd>Numeric vector specifying the initial value of the latent
consensus ranking \(\rho\). Defaults to NULL, which means that the
initial value is set randomly. If <code>rho_init</code> is provided when
<code>n_clusters &gt; 1</code>, each mixture component \(\rho_{c}\) gets the same
initial value.</dd>
<dt>alpha_init</dt>
<dd>Numeric value specifying the initial value of the scale
parameter \(\alpha\). Defaults to <code>1</code>. When <code>n_clusters &gt; 1</code>,
each mixture component \(\alpha_{c}\) gets the same initial value. When
chains are run in parallel, by providing an argument <code>cl = cl</code>, then
<code>alpha_init</code> can be a vector of of length <code>length(cl)</code>, each
element of which becomes an initial value for the given chain.</dd></dl>

Arguments

Set initial values of scale parameter and modal ranking — set_initial_values

<dl>

<dt>rho_init</dt>
<dd>Numeric vector specifying the initial value of the latent
consensus ranking \(\rho\). Defaults to NULL, which means that the
initial value is set randomly. If <code>rho_init</code> is provided when
<code>n_clusters &gt; 1</code>, each mixture component \(\rho_{c}\) gets the same
initial value.</dd>


<dt>alpha_init</dt>
<dd>Numeric value specifying the initial value of the scale
parameter \(\alpha\). Defaults to <code>1</code>. When <code>n_clusters &gt; 1</code>,
each mixture component \(\alpha_{c}\) gets the same initial value. When
chains are run in parallel, by providing an argument <code>cl = cl</code>, then
<code>alpha_init</code> can be a vector of of length <code>length(cl)</code>, each
element of which becomes an initial value for the given chain.</dd>

</dl>

set_initial_values: Set initial values of scale parameter and modal ranking

Description

Usage

Value

Arguments

See Also