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Modelling rankings in R: the PlackettLuce package
Computation · Submitted 29 Oct 2018
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This paper presents the R package PlackettLuce, which implements a generalization of the Plackett-Luce model for rankings data.
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The abstract, sentence by sentence
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 that have different item worths. The features of the package are demonstrated through application to classic and novel data sets.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This paper presents the R package PlackettLuce, which implements a generalization of the Plackett-Luce model for rankings data. | contribution 1.00 | 0.88 | 0.20 | asserted 1.00 |
| s1 | The generalization accommodates both ties (of arbitrary order) and partial rankings (complete rankings of subsets of items). | contribution 0.55 | 0.56 | 0.07 | asserted 1.00 |
| s2 | 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. | method 0.97 | 0.85 | 0.06 | asserted 1.00 |
| s3 | In this way, the worth of each item always has a finite maximum likelihood estimate, with finite standard error. | result 0.60 | 0.75 | 0.07 | asserted 1.00 |
| s4 | The use of pseudo-comparisons also has a regularization effect, shrinking the estimated parameters towards equal item worth. | interpretation 0.58 | 0.71 | 0.06 | asserted 1.00 |
| s5 | In addition to standard methods for model summary, PlackettLuce provides a method to compute quasi standard errors for the item parameters. | contribution 0.89 | 0.86 | 0.05 | asserted 1.00 |
| s6 | This provides the basis for comparison intervals that do not change with the choice of identifiability constraint placed on the item parameters. | interpretation 0.34 | 0.78 | 0.06 | asserted 0.99 |
| s7 | 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 that have different item worths. | contribution 0.81 | 0.82 | 0.09 | asserted 1.00 |
| s8 | The features of the package are demonstrated through application to classic and novel data sets. | contribution 0.53 | 0.83 | 0.09 | asserted 1.00 |
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The paper
Original 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 that have different item worths. The features of the package are demonstrated through application to classic and novel data sets.
Human review
How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 3 sentences without a confident label
Uses the method or tool of 1 paper
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To accommodate such ties, PlackettLuce models each choice in the ranking with the Davidson-Luce model as developed recently by [ref] .
Davidson-Luce model for multi-item choice with ties · Modelling partial rankings with ties · 0.87
Uses data or values from 1 paper
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For a practical example of modelling rankings where the underlying network is weakly connected, we consider the NASCAR data from [ref] .
Models for Paired Comparison Data: A Review with Emphasis on Dependent Data · NASCAR example · 1.00
Compares with 1 paper
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In order to demonstrate the correspondence with the results from [ref] , we order the item parameters by the driver's average rank:
Models for Paired Comparison Data: A Review with Emphasis on Dependent Data · NASCAR example · 0.97
Cites as background 1 paper
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Further description of this general model for handling multi-item ties, including its origins and its most important properties, can be found in [ref] .
Davidson-Luce model for multi-item choice with ties · Modelling partial rankings with ties · 0.97
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