unfrequence

This function takes in input a matrix in which the m first columns are the different observed ranks and
the last column contains the observation frequency, and returns a matrix containing all the ranks (ranks with frequency&gt;1
are repeated).

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

unfrequence function

<dl><dt>data</dt>
<dd>a matrix containing rankings and observation frequency.</dd></dl>

Arguments

Convert data — unfrequence

<dl>

<dt>data</dt>
<dd>a matrix containing rankings and observation frequency.</dd>

</dl>

unfrequence: Convert data

Description

Usage

Value

Arguments

See Also

Examples