PlackettLuce v0.2-9


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Plackett-Luce Models for Rankings

Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details.



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The PlackettLuce package implements a generalization of the model jointly attributed to Plackett (1975) and Luce (1959) for modelling rankings data. Examples of rankings data might be the finishing order of competitors in a race, or the preference of consumers over a set of competing products.

The output of the model is an estimated worth for each item that appears in the rankings. The parameters are generally presented on the log scale for inference.

The implementation of the Plackett-Luce model in PlackettLuce:

  • Accommodates ties (of any order) in the rankings, e.g. bananas (\succ) {apples, oranges} (\succ) pears.
  • Accommodates sub-rankings, e.g. pears (\succ) apples, when the full set of items is {apples, bananas, oranges, pears}.
  • Handles disconnected or weakly connected networks implied by the rankings, e.g. where one item always loses as in figure below. This is achieved by adding pseudo-rankings with a hypothetical or ghost item.

In addition the package provides methods for

  • Obtaining quasi-standard errors, that don’t depend on the constraints applied to the worth parameters for identifiability.
  • Fitting Plackett-Luce trees, i.e. a tree that partitions the rankings by covariate values, such as consumer attributes or racing conditions, identifying subgroups with different sets of worth parameters for the items.


The package may be installed from CRAN via


The development version can be installed via

# install.packages("devtools")


The Netflix Prize was a competition devised by Netflix to improve the accuracy of its recommendation system. To facilitate this they released ratings about movies from the users of the system that have been transformed to preference data and are available from PrefLib, (Bennett and Lanning 2007). Each data set comprises rankings of a set of 3 or 4 movies selected at random. Here we consider rankings for just one set of movies to illustrate the functionality of PlackettLuce.

The data can be read in using the read.soc function in PlackettLuce

preflib <- ""
netflix <- read.soc(file.path(preflib, "netflix/ED-00004-00000138.soc"))
head(netflix, 2)
##   Freq Rank 1 Rank 2 Rank 3 Rank 4
## 1   68      2      1      4      3
## 2   53      1      2      4      3

Each row corresponds to a unique ordering of the four movies in this data set. The number of Netflix users that assigned that ordering is given in the first column, followed by the four movies in preference order. So for example, 68 users ranked movie 2 first, followed by movie 1, then movie 4 and finally movie 3.

PlackettLuce, the model-fitting function in PlackettLuce requires that the data are provided in the form of rankings rather than orderings, i.e. the rankings are expressed by giving the rank for each item, rather than ordering the items. We can create a "rankings" object from a set of orderings as follows

R <- as.rankings(netflix[,-1], input = "orderings",
                 items = attr(netflix, "items"))
R[1:3, as.rankings = FALSE]
##   Mean Girls Beverly Hills Cop The Mummy Returns Mission: Impossible II
## 1          2                 1                 4                      3
## 2          1                 2                 4                      3
## 3          2                 1                 3                      4

Note that read.soc saved the names of the movies in the "items" attribute of netflix, so we have used these to label the items. Subsetting the rankings object R with as.rankings = FALSE, returns the underlying matrix of rankings corresponding to the subset. So for example, in the first ranking the second movie (Beverly Hills Cop) is ranked number 1, followed by the first movie (Mean Girls) with rank 2, followed by the fourth movie (Mission: Impossible II) and finally the third movie (The Mummy Returns), giving the same ordering as in the original data.

Various methods are provided for "rankings" objects, in particular if we subset the rankings without as.rankings = FALSE, the result is again a "rankings" object and the corresponding print method is used:

##                                          1 
## "Beverly Hills Cop > Mean Girls > Mis ..." 
##                                          2 
## "Mean Girls > Beverly Hills Cop > Mis ..." 
##                                          3 
## "Beverly Hills Cop > Mean Girls > The ..."
print(R[1:3], width = 60)
##                                                              1 
## "Beverly Hills Cop > Mean Girls > Mission: Impossible II  ..." 
##                                                              2 
## "Mean Girls > Beverly Hills Cop > Mission: Impossible II  ..." 
##                                                              3 
## "Beverly Hills Cop > Mean Girls > The Mummy Returns > Mis ..."

The rankings can now be passed to PlackettLuce to fit the Plackett-Luce model. The counts of each ranking provided in the downloaded data are used as weights when fitting the model.

mod <- PlackettLuce(R, weights = netflix$Freq)
coef(mod, log = FALSE)
##             Mean Girls      Beverly Hills Cop      The Mummy Returns 
##              0.2306285              0.4510655              0.1684719 
## Mission: Impossible II 
##              0.1498342

Calling coef with log = FALSE gives the worth parameters, constrained to sum to one. These parameters represent the probability that each movie is ranked first.

For inference these parameters are converted to the log scale, by default setting the first parameter to zero so that the standard errors are estimable:

## Call: PlackettLuce(rankings = R, weights = netflix$Freq)
## Coefficients:
##                        Estimate Std. Error z value Pr(>|z|)    
## Mean Girls              0.00000         NA      NA       NA    
## Beverly Hills Cop       0.67080    0.07472   8.978  < 2e-16 ***
## The Mummy Returns      -0.31404    0.07593  -4.136 3.53e-05 ***
## Mission: Impossible II -0.43128    0.07489  -5.759 8.47e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Residual deviance:  3493.5 on 3525 degrees of freedom
## AIC:  3499.5 
## Number of iterations: 7

In this way, Mean Girls is treated as the reference movie, the positive parameter for Beverly Hills Cop shows this was more popular among the users, while the negative parameters for the other two movies show these were less popular.

Comparisons between different pairs of movies can be made visually by plotting the log-worth parameters with comparison intervals based on quasi standard errors.

qv <- qvcalc(mod)
plot(qv, ylab = "Worth (log)", main = NULL)

If the intervals overlap there is no significant difference. So we can see that Beverly Hills Cop is significantly more popular than the other three movies, Mean Girls is significant more popular than The Mummy Returns or Mission: Impossible II, but there was no significant difference in users’ preference for these last two movies.

Going Further

The core functionality of PlackettLuce is illustrated in the package vignette, along with details of the model used in the package and a comparison to other packages. The vignette can be found on the package website or from within R once the package has been installed, e.g. via

vignette("Overview", package = "PlackettLuce")

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.


Bennett, J., and S. Lanning. 2007. “The Netflix Prize.” In Proceedings of the KDD Cup Workshop 2007, 3–6. ACM.
Luce, R. Duncan. 1959. Individual Choice Behavior: A Theoretical Analysis. New York: Wiley.
Plackett, Robert L. 1975. “The Analysis of Permutations.” Appl. Statist 24 (2): 193–202.

Functions in PlackettLuce

Name Description
plfit PlackettLuce Wrapper for Model-based Recursive Partitioning
nascar Results from 2002 NASCAR Season
itempar.PlackettLuce Extract Item Parameters of Plackett-Luce Models
pudding Paired Comparisons of Chocolate Pudding
qvcalc.PlackettLuce Quasi Variances for Model Coefficients
pltree-summaries Plackett-Luce Tree Summaries
preflib Read Preflib Election Data Files
adjacency Create an Adjacency Matrix for a set of Rankings
pltree Plackett-Luce Trees
reexports Objects exported from other packages
rankings Rankings Object
fitted.PlackettLuce Fitted Probabilities for PlackettLuce Objects
simulate.PlackettLuce Simulate from PlackettLuce fitted objects
group Group Rankings
summaries Plackett-Luce Model Summaries
PlackettLuce-deprecated Deprecated functions in package PlackettLuce
choices Choices Object
PlackettLuce Fit a Plackett-Luce Model
complete Complete Orderings with the Missing Redundant Rank
decode Decode Orderings using a Key to Item Names
connectivity Check Connectivity of Rankings
PlackettLuce-package Plackett-Luce Models for Rankings
beans Preferred Bean Varieties in Nicaragua
aggregate Aggregate Rankings
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Vignettes of PlackettLuce

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Type Package
License GPL-3
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
VignetteBuilder knitr
Language en-GB
NeedsCompilation no
Packaged 2019-09-16 13:59:53 UTC; hturner
Repository CRAN
Date/Publication 2019-09-16 16:00:02 UTC

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