words

The data was collected under the auspices of the Graduate Record
Examination Board. A sample of 98 college students were asked to rank five words according to strength of association (least to most associated) with the target word "Idea":
 A = Thought, B = Play, C = Theory, D = Dream and E = Attention.

datasets

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

words function

A list containing :<dl class="dl-horizontal">
 <dt>data</dt><dd>A matrix of size 98*5 containing the 98 answers. Each row corresponding to one ranking (ranking representation).The ranking representation r=(r_1,...,r_m) contains the ranks assigned to the objects, and means that the ith object is in r_ith position.For example, if the ranking representation of a rank is (4,3,1,2,5), it means that judge ranks the first object in 4th position, second object in 3rd position, ...</dd>
 <dt>frequency</dt><dd>matrix of size 15*6. Each row corresponds to one of the 15 different observed rankings, and the last column contains the observation frequency.</dd>
 <dt>m</dt><dd>the size of the rankings (m=5).</dd>
</dl>

words: rank data : words

Description

Arguments

Format

Examples