sports


This data set is due to Louis Roussos who asked 130 students at the
University of Illinois to rank seven sports according to their preference in participating:
 A = Baseball, B = Football, C = Basketball, D = Tennis, E = Cycling, F =
 Swimming, G = Jogging.


datasets

Implementation of a model-based clustering algorithm for
ranking data. 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

sports function

A list containing :
<dl>
  <dt>data</dt><dd>a matrix containing 130 ranks of size 7 in 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>a matrix with 123 differents ranks of size 7. In each row the first 7 columns correspond to one observed ranking and the last column contains the observation frequency.</dd> <dt>m</dt><dd> the size of the rankings (m=7).</dd> </dl>

Format

J.I. Marden. "Analyzing and modeling rank data, volume 64 of Monographs on Statistics and Applied Probability". Chapman & Hall, London, 1995.

sports: rank data : sports

Description

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Source

Examples