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Modelling rankings in R: the PlackettLuce package

Author

Listed:
  • Heather L. Turner

    (University of Warwick)

  • Jacob Etten

    (Bioversity International)

  • David Firth

    (University of Warwick
    The Alan Turing Institute)

  • Ioannis Kosmidis

    (University of Warwick
    The Alan Turing Institute)

Abstract

This paper presents the R package PlackettLuce, which implements a generalization of the Plackett–Luce model for rankings data. The generalization accommodates both ties (of arbitrary order) and partial rankings (complete rankings of subsets of items). By default, the implementation adds a set of pseudo-comparisons with a hypothetical item, ensuring that the underlying network of wins and losses between items is always strongly connected. In this way, the worth of each item always has a finite maximum likelihood estimate, with finite standard error. The use of pseudo-comparisons also has a regularization effect, shrinking the estimated parameters towards equal item worth. In addition to standard methods for model summary, PlackettLuce provides a method to compute quasi standard errors for the item parameters. This provides the basis for comparison intervals that do not change with the choice of identifiability constraint placed on the item parameters. Finally, the package provides a method for model-based partitioning using covariates whose values vary between rankings, enabling the identification of subgroups of judges or settings with different item worths. The features of the package are demonstrated through application to classic and novel data sets.

Suggested Citation

  • Heather L. Turner & Jacob Etten & David Firth & Ioannis Kosmidis, 2020. "Modelling rankings in R: the PlackettLuce package," Computational Statistics, Springer, vol. 35(3), pages 1027-1057, September.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:3:d:10.1007_s00180-020-00959-3
    DOI: 10.1007/s00180-020-00959-3
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    References listed on IDEAS

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    1. Cristina Mollica & Luca Tardella, 2017. "Bayesian Plackett–Luce Mixture Models for Partially Ranked Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 442-458, June.
    2. Hatzinger, Reinhold & Dittrich, Regina, 2012. "prefmod: An R Package for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i10).
    3. R. L. Plackett, 1975. "The Analysis of Permutations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 24(2), pages 193-202, June.
    4. Irurozki, Ekhine & Calvo, Borja & Lozano, Jose A., 2016. "PerMallows: An R Package for Mallows and Generalized Mallows Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 71(i12).
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    Cited by:

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    2. Occelli, M. & Compaore, E. & Brown, D. & Barry, F. & Poda, L.S. & Traore, F. & Waongo, A. & Rubin, D. & Tufan, H.A., 2024. "Ex-ante priority setting in crop breeding: Towards inclusive innovation for impact," Agricultural Systems, Elsevier, vol. 217(C).
    3. Oliveira, Isabela Kopperschmidt de & Meira, Leonardo Herszon & Oliveira, Leise Kelli, 2024. "Key factors for developing freight and passenger integrated transportation systems in Brazil," Research in Transportation Economics, Elsevier, vol. 104(C).
    4. Holý, Vladimír, 2024. "Ranking-based second stage in data envelopment analysis: An application to research efficiency in higher education," Operations Research Perspectives, Elsevier, vol. 12(C).

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