PlackettLuce 0.4.1

The main model-fitting function in the **PlackettLuce** package, `PlackettLuce`

,
directly models the worth of items with a separate parameter estimate for
each item (see Introduction to PlackettLuce). This vignette
introduces a new function, `pladmm`

, that models the log-worth of
items by a linear function of item covariates. This functionality is under
development and provided for experimental use - the user interface is likely
to change in upcoming versions of PlackettLuce.

`pladmm`

supports partial rankings, but otherwise has limited functionality
compared to `PlackettLuce`

. In particular, ties, pseudo-rankings, prior
information on log-worths, and ranker adherence parameters are not supported.

The standard Plackett-Luce model specifies the probability of a ranking of \(J\) items, \({i_1 \succ \ldots \succ i_J}\), is given by

\[\prod_{j=1}^J \frac{\alpha_{i_j}}{\sum_{i \in A_j} \alpha_i}\]

where \(\alpha_{i_j}\) represents the **worth** of item \(i_j\) and \(A_j\) is the
set of alternatives \(\{i_j, i_{j + 1}, \ldots, i_J\}\) from which item \(i_j\) is
chosen.

`pladmm`

models the log-worth as a linear function of item covariates:

\[\log \alpha_i = \beta_0 + \beta_1 x_{i1} + \ldots + \beta_p x_{ip}\]

where \(\beta_0\) is fixed by the constraint that \(\sum_i \alpha_i = 1\). The
parameters are estimated using an Alternating Directions Method of Multipliers
(ADMM) algorithm proposed by (Yildiz et al. 2020), hence the name `pladmm`

.

ADMM alternates between estimating the worths \(\alpha_i\) and the linear coefficients \(\beta_k\), encapsulating them in a quadratic penalty on the likelihood:

\[L(\boldsymbol{\beta}, \boldsymbol{\alpha}, \boldsymbol{u}) = \mathcal{L}(\mathcal{D}|\boldsymbol{\alpha}) + \frac{\rho}{2}||\boldsymbol{X}\boldsymbol{\beta} - \log \boldsymbol{\alpha} + \boldsymbol{u}||^2_2 - \frac{\rho}{2}||\boldsymbol{u}||^2_2\] where \(\boldsymbol{u}\) is a dual variable that imposes the equality constraints (so that \(\log \boldsymbol{\alpha}\) converges to \(\boldsymbol{X}\boldsymbol{\beta}\)).

We shall illustrate the use of `pladmm`

with a classic data set presented by
(Critchlow and Fligner 1991) that is available as the `salad`

data set in the
**prefmod** package. The data are 32 full rankings of 4 salad dressings
(A, B, C, D) by tartness, with 1 being the least tart and 4 being the most
tart, according to the ranker.

```
library(prefmod)
head(salad, 4)
```

```
## A B C D
## 1 1 2 3 4
## 2 1 2 3 4
## 3 2 1 3 4
## 4 2 1 4 3
```

The salad dressings were made with known quantities of acetic acid and gluconic acid, as specified in the following data frame:

```
features <- data.frame(salad = LETTERS[1:4],
acetic = c(0.5, 0.5, 1, 0),
gluconic = c(0, 10, 0, 10))
```

We begin by using `pladmm`

to fit a standard Plackett-Luce model, with a
separate parameter for each salad dressing. The first three arguments are the
rankings (a matrix or `rankings`

object), a formula specifying the model for
the log-worth (must include an intercept) and a data frame of item features
containing variables in the model formula. `rho`

is the penalty parameter
determining the strength of penalty on the log-likelihood. As a rule of thumb,
`rho`

should be ~10% of the fitted log-likelihood.

```
library(PlackettLuce)
standardPL <- pladmm(salad, ~ salad, data = features, rho = 8)
summary(standardPL)
```

```
## Call: pladmm(rankings = salad, formula = ~salad, data = features, rho = 8)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1740 NA NA NA
## saladB 2.7305 0.4481 6.093 1.11e-09 ***
## saladC 1.5621 0.3965 3.939 8.17e-05 ***
## saladD 1.0275 0.3771 2.725 0.00644 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual deviance: 152.83 on 189 degrees of freedom
## AIC: 158.83
## Number of iterations: 7
```

In this case, the intercept represents the log-worth of salad dressing A, which is fixed by the constraint that the worths sum to 1.

`sum(exp(standardPL$x %*% coef(standardPL)))`

`## [1] 1`

The remaining coefficients are the difference in log-worth between each salad
dressing and salad dressing A. We can compare this to the results from
`PlackettLuce`

, which sets the log-worth of salad dressing A to zero:

```
standardPL_PlackettLuce <- PlackettLuce(salad, npseudo = 0)
summary(standardPL_PlackettLuce)
```

```
## Call: PlackettLuce(rankings = salad, npseudo = 0)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## A 0.0000 NA NA NA
## B 2.7299 0.4481 6.093 1.11e-09 ***
## C 1.5615 0.3965 3.939 8.20e-05 ***
## D 1.0268 0.3771 2.723 0.00646 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual deviance: 152.83 on 189 degrees of freedom
## AIC: 158.83
## Number of iterations: 6
```

The differences in log-worth are the same to ~3 decimal places. We can improve
the accuracy of `pladmm`

by reducing `rtol`

(by default 1e-4):

```
standardPL <- pladmm(salad, ~ salad, data = features, rho = 8, rtol = 1e-6)
summary(standardPL)
```

```
## Call: pladmm(rankings = salad, formula = ~salad, data = features, rho = 8,
## rtol = 1e-06)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1735 NA NA NA
## saladB 2.7299 0.4481 6.093 1.11e-09 ***
## saladC 1.5615 0.3965 3.939 8.20e-05 ***
## saladD 1.0268 0.3771 2.723 0.00646 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual deviance: 152.83 on 189 degrees of freedom
## AIC: 158.83
## Number of iterations: 17
```

The `itempar`

function can be used to obtain the worth estimates, e.g.

`itempar(standardPL)`

```
## Item response item parameters (PLADMM):
## A B C D
## 0.04186 0.64176 0.19950 0.11688
```

To model the log-worth by item covariates, we simply update the model formula:

```
regressionPL <- pladmm(salad, ~ acetic + gluconic, data = features, rho = 8)
summary(regressionPL)
```

```
## Call: pladmm(rankings = salad, formula = ~acetic + gluconic, data = features,
## rho = 8)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.84097 NA NA NA
## acetic 3.27431 0.57650 5.680 1.35e-08 ***
## gluconic 0.27392 0.04505 6.081 1.20e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual deviance: 152.9 on 190 degrees of freedom
## AIC: 156.9
## Number of iterations: 14
```

The model uses one less degree of freedom, but there is only a slight increase in the deviance, that is not significant:

`anova(standardPL, regressionPL)`

```
## Analysis of Deviance Table
##
## Model 1: ~salad
## Model 2: ~acetic + gluconic
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 189 152.83
## 2 190 152.91 1 0.074411 0.785
```

So it is sufficient to model the log-worth by the concentration of acetic and gluconic acids.

An advantage of modelling log-worth by covariates is that we can predict the log-worth for new items. For example, suppose we have salad dressings with the following features:

```
features2 <- data.frame(salad = LETTERS[5:6],
acetic = c(0.5, 0),
gluconic = c(5, 5))
```

the predicted log-worth is given by

`predict(regressionPL, features2)`

```
## 1 2
## -1.834198 -3.471352
```

Note that the names in `features2$salad`

are unused as `salad`

was not a
variable in the model. The predicted log-worths have the same location as the
original fitted values

`fitted(regressionPL)`

```
## A B C D
## -3.2038115 -0.4645852 -1.5666574 -2.1017393
```

i.e. they are contrasts with the log-worth of salad dressing A. If we want
to express the predictions as a new set of constrained item parameters, we
can specify `type = "itempar"`

(vs the default `type = "lp"`

for
linear predictor). The
parameterization can then be specified by passing arguments on to `itempar()`

,
e.g. the following will compute the predicted worths constrained to sum to 1:

`predict(regressionPL, features2, type = "itempar", log = FALSE, ref = NULL)`

```
## 1 2
## 0.8371473 0.1628527
```

Standard errors can optionally be returned, by specifying `se.fit = TRUE`

```
predict(regressionPL, features2, type = "itempar", log = FALSE, ref = NULL,
se.fit = TRUE)
```

```
## $fit
## 1 2
## 0.8371473 0.1628527
##
## $se.fit
## 1 2
## 0.03929727 0.03929727
```

The Plackett-Luce model with item covariates can also be used in model-based partitioning. To illustrate, we shall simulate some covariate data for the judges than ranked the four salads, based on their ranking of salad A

```
set.seed(1)
judge_features <- data.frame(varC = rpois(nrow(salad), lambda = salad$C^2))
```

This simulates the scenario where some characteristic of the judge affects how they rank salad A, so we expect the item worth to depend on this variable.

Now we group the rankings by judge in preparation to fit a Plackett-Luce tree:

`grouped_salad <- group(as.rankings(salad), 1:nrow(salad))`

We specify the Plackett-Luce tree to partition the grouped rankings by any of
the judge features (`grouped_salad ~ .`

), with the log-worth of the salads
modelled by a linear function of the acetic and gluconic acid concentrations
(`~acetic + gluconic`

). The corresponding variables are found in `data`

, which
should be a list of two data frames, the first containing the group covariates
and the second containing the item covariates. We set a minimum group size of
10 and reduce the `rho`

parameter accordingly.

```
tree <- pltree(grouped_salad ~ .,
worth = ~acetic + gluconic,
data = list(judge_features, features),
rho = 2, minsize = 10)
plot(tree, ylines = 2)
```

The result is a tree with two nodes; both groups prefer salad B, but the first group (varC ≤ 7) places salad C in second place, while the second group (varC > 7) prefer salad D. This is as we might expect, since we simulated the judge covariate varC to correlate with the ranking of C, so a higher value of this variable correlates to a lower preference for C. We can see the difference in the coefficients of the item features:

`tree`

```
## Plackett-Luce tree
##
## Model formula:
## grouped_salad ~ .
##
## Fitted party:
## [1] root
## | [2] varC <= 7: n = 21
## | (Intercept) acetic gluconic
## | -5.5121162 4.3035356 0.2845096
## | [3] varC > 7: n = 11
## | (Intercept) acetic gluconic
## | -5.0821780 2.7423782 0.3355964
##
## Number of inner nodes: 1
## Number of terminal nodes: 2
## Number of parameters per node: 3
## Objective function (negative log-likelihood): 71.40774
```

From the first group to the second group, the coefficient for acetic acid concentration reduces from 4.3 to 2.7. Since the acetic acid concentration for salad C is 1, with 0 gluconic acid, this reduces the worth of salad C in the second group. At the same time, the coefficient for gluconic acid concentration increases 0.28 to 0.34 between the first and second groups. Since the gluconic acid concentration for salad D is 1, with 0 acetic acid, this increases the worth of salad D in the second group.

The PLADMM algorithm should in theory converge to the maximum likelihood
estimates for the parameters. However, the algorithm may not behave well if the
rankings are very sparse or if the penalty parameter `rho`

is not set to a
suitable value. Currently, `pladmm`

does not provide checks/warnings to assist
the user the validate the result. It is recommended that the standard
Plackett-Luce model is fitted initially to give a reference of the expected
log-likelihood and item parameters - `pladmm`

should give broadly similar
results.

`pladmm`

also returns two estimates of the worths. The first set are the direct
estimates from the last iteration of ADMM:

`regressionPL$pi`

```
## A B C D
## 0.04061305 0.62842986 0.20872416 0.12223294
```

The second set are the estimates given by the estimates of \(\boldsymbol{\beta}\) from the last iteration:

`regressionPL$tilde_pi`

```
## A B C D
## 0.04060714 0.62839568 0.20874175 0.12224363
```

These two sets of estimates should be approximately the same (but being approximately the same does not guarantee the solution is the global optimum).

Critchlow, Douglas, and Michael Fligner. 1991. “Paired comparison, triple comparison, and ranking experiments as generalized linear models, and their implementation on GLIM.” *Psychometrika* 56 (3): 517–33. https://doi.org/10.1007/BF02294488.

Yildiz, Ilkay, Jennifer Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, and Stratis Ioannidis. 2020. “Fast and Accurate Ranking Regression.” In *Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics*, edited by Silvia Chiappa and Roberto Calandra, 108:77–88. Proceedings of Machine Learning Research. http://proceedings.mlr.press/v108/yildiz20a.html.