[,Rankclust-method

Extract values of various
slots of the output from the function <code>rankclust</code>.

Implementation of a model-based clustering algorithm for
ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>).
Multivariate rankings as well as partial rankings are taken
into account. This algorithm is based on an extension of the Insertion
Sorting Rank (ISR) model for ranking data, which is a meaningful and
effective model parametrized by a position parameter (the modal ranking,
quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
of the rank population is modelled by a mixture of ISR, whereas conditional
independence assumption is considered for multivariate rankings.

Quentin Grimonprez

Rankcluster

Model-Based Clustering for Multivariate Partial Ranking Data

Julien Jacques

Christophe Biernacki

[,Rankclust-method function

<dl><dt>x</dt>
<dd>object from which to extract element(s) or in which to replace element(s).</dd>
<dt>i</dt>
<dd>the number of cluster of the element we want to extract or "bic", "icl", "ll"</dd>
<dt>j, drop</dt>
<dd>not used</dd></dl>

Arguments

Getter method for rankclust output — [,Rankclust-method

<dl>

<dt>x</dt>
<dd>object from which to extract element(s) or in which to replace element(s).</dd>


<dt>i</dt>
<dd>the number of cluster of the element we want to extract or "bic", "icl", "ll"</dd>


<dt>j, drop</dt>
<dd>not used</dd>

</dl>

[,Rankclust-method: Getter method for rankclust output

Description

Usage

Arguments