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In BradleyTerry2: Bradley-Terry Models
``` {r include=FALSE}
library <- function(...) suppressPackageStartupMessages(base::library(...))
library(knitr)
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Contents { .unnumbered}
\@ref(sec:intro) [Introduction]
\@ref(sec:BTmodel) [Standard Bradley-Terry model]
\@ref(sec:citations) [Example: Analysis of journal citations]
\@ref(sec:bias-reduced) [Bias-reduced estimates]
\@ref(sec:covariates) [Abilities predicted by explanatory variables]
\@ref(sec:player-specific) ['Player-specific' predictor variables]
\@ref(sec:missing) [Missing values]
\@ref(sec:order) [Order effect]
\@ref(sec:CEMS) [More general (contest-specific) predictors]
\@ref(sec:ability) [Ability scores]
\@ref(sec:residuals) [Residuals]
\@ref(sec:model) [Model search]
\@ref(sec:data) [Setting up the data]
\@ref(sec:contest) [Contest-specific data]
\@ref(sec:non-contest) [Non contest-specific data]
\@ref(sec:wide) [Converting data from a 'wide' format]
\@ref(sec:BradleyTerry) [Converting data from the format required by the earlier BradleyTerry package]
\@ref(sec:functions) [A list of the functions provided in BradleyTerry2]
\@ref(sec:finalremarks) [Some final remarks]
\@ref(sec:ties) [A note on the treatment of ties]
\@ref(sec:random-effects) [A note on 'contest-specific' random effects]
[Acknowledgments]
[References]
Introduction {#sec:intro}
The Bradley-Terry model [@brad:terr:52] assumes that
in a 'contest' between any two 'players', say player $i$ and player $j$
$(i, j \in {1,\ldots,K})$, the odds that $i$ beats $j$ are
$\alpha_i/\alpha_j$, where $\alpha_i$ and $\alpha_j$ are positive-valued
parameters which might be thought of as representing 'ability'. A
general introduction can be found in @brad:84 or @agre:02.
Applications are many, ranging from experimental
psychology to the analysis of sports tournaments to genetics [for
example, the allelic transmission/disequilibrium test of @sham:curt:95
is based on a Bradley-Terry model in which the
'players' are alleles]. In typical psychometric applications the
'contests' are comparisons, made by different human subjects, between
pairs of items.
The model can alternatively be expressed in the logit-linear form
$$\mathop{\rm logit}[\mathop{\rm pr}(i\ \mathrm{beats}\ j)]=\lambda_i-\lambda_j,
\label{eq:unstructured} (#eq:unstructured)$$
where $\lambda_i=\log\alpha_i$ for all $i$. Thus, assuming independence
of all contests, the parameters ${\lambda_i}$ can be estimated by
maximum likelihood using standard software for generalized linear
models, with a suitably specified model matrix. The primary purpose of
the BradleyTerry2 package [@turn:12],
implemented in the R statistical computing environment [@ihak:gent:96;@r],
is to facilitate the specification and fitting of such models and some
extensions.
The BradleyTerry2 package supersedes the earlier BradleyTerry
package [@firt:05], providing a more flexible user interface
to allow a wider range of models to be fitted. In particular,
BradleyTerry2 allows the inclusion of simple random effects so that
the ability parameters can be related to available explanatory variables
through a linear predictor of the form
$$\lambda_i=\sum_{r=1}^p\beta_rx_{ir} + U_i.
(#eq:autonumber2) $$
The inclusion of the prediction error $U_i$ allows for variability
between players with equal covariate values and induces correlation
between comparisons with a common player. BradleyTerry2 also allows
for general contest-specific effects to be included in the model and
allows the logit link to be replaced, if required, by a different
symmetric link function (probit or cauchit).
The remainder of the paper is organised as follows.
Section \@ref(sec:BTmodel) demonstrates how to use the BradleyTerry2
package to fit a standard (i.e., unstructured) Bradley-Terry model, with
a separate ability parameter estimated for each player, including the
use of bias-reduced estimation for such models.
Section \@ref(sec:covariates) considers variations of the standard model,
including the use of player-specific variables to model ability and
allowing for contest-specific effects such as an order effect or judge
effects. Sections \@ref(sec:ability) and \@ref(sec:residuals) explain how
to obtain important information about a fitted model, in particular the
estimates of ability and their standard errors, and player-level
residuals, whilst Section \@ref(sec:model) notes the functions available
to aid model search. Section \@ref(sec:data) explains in more detail how
set up data for use with the BradleyTerry2 package,
Section \@ref(sec:functions) lists the functions provided by the package
and finally Section \@ref(sec:finalremarks) comments on two directions
for further development of the software.
Standard Bradley-Terry model {#sec:BTmodel}
Example: Analysis of journal citations {#sec:citations}
The following data come from page 448 of @agre:02,
extracted from the larger table of @stig:94. The data are
counts of citations among four prominent journals of statistics and are
included the BradleyTerry2 package as the data set citations:
``` {r LoadBradleyTerry2}
library("BradleyTerry2")
``` {r CitationData}
data("citations", package = "BradleyTerry2")
``` {r CitationData2}
citations
Thus, for example, *Biometrika* was cited 498 times by papers in
*Journal of the American Statistical Association* (JASA) during the
period under study. In order to fit a Bradley-Terry model to these data
using `BTm` from the **BradleyTerry2** package, the data must first be
converted to binomial frequencies. That is, the data need to be
organised into pairs (`player1`, `player2`) and corresponding
frequencies of wins and losses for `player1` against `player2`. The
**BradleyTerry2** package provides the utility function
`countsToBinomial` to convert a contingency table of wins to the format
just described:
``` {r countsToBinomial}
citations.sf <- countsToBinomial(citations)
names(citations.sf)[1:2] <- c("journal1", "journal2")
citations.sf
Note that the self-citation counts are ignored -- these provide no
information on the ability parameters, since the abilities are relative
rather than absolute quantities. The binomial response can then be
modelled by the difference in player abilities as follows:
``` {r citeModel}
citeModel <- BTm(cbind(win1, win2), journal1, journal2, ~ journal,
id = "journal", data = citations.sf)
citeModel
The coefficients here are maximum likelihood estimates of
$\lambda_2, \lambda_3,
\lambda_4$, with $\lambda_1$ (the log-ability for *Biometrika*) set to
zero as an identifying convention.
The one-sided model formula
``` r
~ journal
specifies the model for player ability, in this case the 'citeability'
of the journal. The id argument specifies that "journal" is the name
to be used for the factor that identifies the player -- the values of
which are given here by journal1 and journal2 for the first and
second players respectively. Therefore in this case a separate
citeability parameter is estimated for each journal.
If a different 'reference' journal is required, this can be achieved
using the optional refcat argument: for example, making use of
update to avoid re-specifying the whole model,
``` {r citeModelupdate}
update(citeModel, refcat = "JASA")
-- the same model in a different parameterization.
The use of the standard Bradley-Terry model for this application might
perhaps seem rather questionable -- for example, citations within a
published paper can hardly be considered independent, and the model
discards potentially important information on self-citation. @stig:94
provides arguments to defend the model's use despite such
concerns.
### Bias-reduced estimates {#sec:bias-reduced}
Estimation of the standard Bradley-Terry model in `BTm` is by default
computed by maximum likelihood, using an internal call to the `glm`
function. An alternative is to fit by bias-reduced maximum likelihood
[@firt:93]: this requires additionally the **brglm** package
[@kosm:07], and is specified by the optional argument
`br = TRUE`. The resultant effect, namely removal of first-order
asymptotic bias in the estimated coefficients, is often quite small. One
notable feature of bias-reduced fits is that all estimated coefficients
and standard errors are necessarily finite, even in situations of
'complete separation' where maximum likelihood estimates take infinite
values [@hein:sche:02].
For the citation data, the parameter estimates are only very slightly
changed in the bias-reduced fit:
``` {r citeModelupdate2}
update(citeModel, br = TRUE)
Here the bias of maximum likelihood is small because the binomial counts
are fairly large. In more sparse arrangements of contests -- that is,
where there is less or no replication of the contests -- the effect of
bias reduction would typically be more substantial than the
insignificant one seen here.
Abilities predicted by explanatory variables {#sec:covariates}
'Player-specific' predictor variables {#sec:player-specific}
In some application contexts there may be 'player-specific' explanatory
variables available, and it is then natural to consider model
simplification of the form
$$\lambda_i=\sum_{r=1}^p\beta_rx_{ir} + U_i,
(#eq:autonumber3) $$
in which ability of each player $i$ is related to explanatory variables
$x_{i1},\ldots,x_{ip}$ through a linear predictor with coefficients
$\beta_1,\ldots,\beta_p$; the ${U_i}$ are independent errors.
Dependence of the player abilities on explanatory variables can be
specified via the formula argument, using the standard S-language
model formulae. The difference in the abilities of player $i$ and player
$j$ is modelled by
$$\sum_{r=1}^p\beta_rx_{ir} - \sum_{r=1}^p\beta_rx_{jr} + U_i - U_j,
\label{eq:structured} (#eq:structured)$$
where $U_i \sim N(0, \sigma^2)$ for all $i$. The Bradley-Terry model is
then a generalized linear mixed model, which the BTm function
currently fits by using the penalized quasi-likelihood algorithm of
@bres:93.
As an illustration, consider the following simple model for the
flatlizards data, which predicts the fighting ability of Augrabies
flat lizards by body size (snout to vent length):
``` {r lizModel}
options(show.signif.stars = FALSE)
data("flatlizards", package = "BradleyTerry2")
lizModel <- BTm(1, winner, loser, ~ SVL[..] + (1|..),
data = flatlizards)
Here the winner of each fight is compared to the loser, so the outcome
is always 1. The special name '`..`' appears in the formula as the
default identifier for players, in the absence of a user-specified `id`
argument. The values of this factor are given by `winner` for the
winning lizard and `loser` for the losing lizard in each contest. These
factors are provided in the data frame `contests` that is the first
element of the list object `flatlizards`. The second element of
`flatlizards` is another data frame, `predictors`, containing
measurements on the observed lizards, including `SVL`, which is the
snout to vent length. Thus `SVL[..]` represents the snout to vent length
indexed by lizard (`winner` or `loser` as appropriate). Finally a random
intercept for each lizard is included using the bar notation familiar to
users of the **lme4** package [@bate:11]. (Note that a random intercept is the only random effect structure
currently implemented in **BradleyTerry2**.)
The fitted model is summarized below:
``` {r summarize_lizModel}
summary(lizModel)
The coefficient of snout to vent length is weakly significant; however,
the standard deviation of the random effect is quite large, suggesting
that this simple model has fairly poor explanatory power. A more
appropriate model is considered in the next section.
Missing values {#sec:missing}
The contest data may include all possible pairs of players and hence
rows of missing data corresponding to players paired with themselves.
Such rows contribute no information to the Bradley-Terry model and are
simply discarded by BTm.
Where there are missing values in player-specific predictor (or
explanatory) variables which appear in the formula, it will typically
be very wasteful to discard all contests involving players for which
some values are missing. Instead, such cases are accommodated by the
inclusion of one or more parameters in the model. If, for example,
player $1$ has one or more of its predictor values
$x_{11},\ldots,x_{1p}$ missing, then the combination of
Equations \@ref(eq:unstructured) and \@ref(eq:structured) above yields
$$\mathop{\rm logit}[\mathop{\rm pr}(1\ \mathrm{beats}\ j)]=\lambda_1 - \left(\sum_{r=1}^p\beta_rx_{jr} + U_j\right),
(#eq:autonumber5) $$
for all other players $j$. This results in the inclusion of a 'direct'
ability parameter for each player having missing predictor values, in
addition to the common coefficients $\beta_1,\ldots,\beta_p$ -- an
approach which will be appropriate when the missingness mechanism is
unrelated to contest success. The same device can be used also to
accommodate any user-specified departures from a structured
Bradley-Terry model, whereby some players have their abilities
determined by the linear predictor but others do not.
In the original analysis of the flatlizards data [@whit:06],
the final model included the first and third principal components
of the spectral reflectance from the throat (representing brightness and
UV intensity respectively) as well as head length and the snout to vent
length seen in our earlier model. The spectroscopy data was missing for
two lizards, therefore the ability of these lizards was estimated
directly. The following fits this model, with the addition of a random
intercept as before:
``` {r lizModel2}
lizModel2 <- BTm(1, winner, loser,
~ throat.PC1[..] + throat.PC3[..] +
head.length[..] + SVL[..] + (1|..),
data = flatlizards)
summary(lizModel2)
Note that `BTm` detects that lizards 96 and 99 have missing values in
the specified predictors and automatically includes separate ability
parameters for these lizards. This model was found to be the single best
model based on the principal components of reflectance and the other
predictors available and indeed the standard deviation of the random
intercept is much reduced, but still highly significant. Allowing for
this significant variation between lizards with the same predictor
values produces more realistic (i.e., larger) standard errors for the
parameters when compared to the original analysis of @whit:06.
Although this affects the significance of the morphological
variables, it does not affect the significance of the principal
components, so in this case does not affect the main conclusions of the
study.
### Order effect {#sec:order}
In certain types of application some or all contests have an associated
'bias', related to the order in which items are presented to a judge or
with the location in which a contest takes place, for example. A natural
extension of the Bradley-Terry model (Equation \@ref(eq:unstructured))
is then
$$\mathop{\rm logit}[\mathop{\rm pr}(i\ \mathrm{beats}\ j)]=\lambda_i-\lambda_j + \delta z,
(\#eq:autonumber6) $$
where $z=1$ if $i$ has the supposed advantage and $z=-1$ if $j$ has it.
(If the 'advantage' is in fact a disadvantage, $\delta$ will be
negative.) The scores $\lambda_i$ then relate to ability in the absence
of any such advantage.
As an example, consider the baseball data given in @agre:02, page 438:
``` {r baseball}
data("baseball", package = "BradleyTerry2")
head(baseball)
The data set records the home wins and losses for each baseball team
against each of the 6 other teams in the data set. The head function
is used to show the first 6 records, which are the Milwaukee home games.
We see for example that Milwaukee played 7 home games against Detroit
and won 4 of them. The 'standard' Bradley-Terry model without a
home-advantage parameter will be fitted if no formula is specified in
the call to BTm:
``` {r baseballModel}
baseballModel1 <- BTm(cbind(home.wins, away.wins), home.team, away.team,
data = baseball, id = "team")
summary(baseballModel1)
The reference team is Baltimore, estimated to be the weakest of these
seven, with Milwaukee and Detroit the strongest.
In the above, the ability of each team is modelled simply as ` team`
where the values of the factor `team` are given by `home.team` for the
first team and `away.team` for the second team in each game. To estimate
the home-advantage effect, an additional variable is required to
indicate whether the team is at home or not. Therefore data frames
containing both the team factor and this new indicator variable are
required in place of the factors `home.team` and `away.team` in the call
to `BTm`. This is achieved here by over-writing the `home.team` and
`away.team` factors in the `baseball` data frame:
``` {r baseballDataUpdate}
baseball$home.team <- data.frame(team = baseball$home.team, at.home = 1)
baseball$away.team <- data.frame(team = baseball$away.team, at.home = 0)
The at.home variable is needed for both the home team and the away
team, so that it can be differenced as appropriate in the linear
predictor. With the data organised in this way, the ability formula can
now be updated to include the at.home variable as follows:
``` {r baseballModelupdate}
baseballModel2 <- update(baseballModel1, formula = ~ team + at.home)
summary(baseballModel2)
This reproduces the results given on page 438 of @agre:02:
the home team has an estimated odds-multiplier of
$\exp(0.3023) = 1.35$ in its favour.
### More general (contest-specific) predictors {#sec:CEMS}
The 'home advantage' effect is a simple example of a contest-specific
predictor. Such predictors are necessarily interactions, between aspects
of the contest and (aspects of) the two 'players' involved.
For more elaborate examples of such effects, see `?chameleons` and
`?CEMS`. The former includes an 'experience' effect, which changes
through time, on the fighting ability of male chameleons. The latter
illustrates a common situation in psychometric applications of the
Bradley-Terry model, where *subjects* express preference for one of two
*objects* (the 'players'), and it is the influence on the results of
subject attributes that is of primary interest.
As an illustration of the way in which such effects are specified,
consider the following model specification taken from the examples in
`?CEMS`, where data on students' preferences in relation to six European
management schools is analysed.
``` {r CEMSmodel}
data("CEMS", package = "BradleyTerry2")
table8.model <- BTm(outcome = cbind(win1.adj, win2.adj),
player1 = school1, player2 = school2, formula = ~ .. +
WOR[student] * LAT[..] + DEG[student] * St.Gallen[..] +
STUD[student] * Paris[..] + STUD[student] * St.Gallen[..] +
ENG[student] * St.Gallen[..] + FRA[student] * London[..] +
FRA[student] * Paris[..] + SPA[student] * Barcelona[..] +
ITA[student] * London[..] + ITA[student] * Milano[..] +
SEX[student] * Milano[..],
refcat = "Stockholm", data = CEMS)
This model reproduces results from Table 8 of @ditt:01
apart from minor differences due to the
different treatment of ties. Here the outcome is the binomial frequency
of preference for school1 over school2, with ties counted as half a
'win' and half a 'loss'. The formula specifies the model for school
'ability' or worth. In this formula, the default label '..' represents
the school (with values given by school1 or school2 as appropriate)
and student is a factor specifying the student that made the
comparison. The remaining variables in the formula use
[R]{.sans-serif}'s standard indexing mechanism to include
student-specific variables, e.g., WOR: whether or not the student was
in full-time employment, and school-specific variables, e.g., LAT:
whether the school was in a 'Latin' city. Thus there are three types of
variables: contest-specific (school1, school2, student),
subject-specific (WOR, DEG, ...) and object-specific (LAT,
St.Gallen, ...). These three types of variables are provided in three
data frames, contained in the list object CEMS.
Ability scores {#sec:ability}
The function BTabilities extracts estimates and standard errors for
the log-ability scores $\lambda_1, \ldots,\lambda_K$. These will either
be 'direct' estimates, in the case of the standard Bradley-Terry model
or for players with one or more missing predictor values, or
'model-based' estimates of the form
$\hat\lambda_i=\sum_{r=1}^p\hat\beta_rx_{ir}$ for players whose ability
is predicted by explanatory variables.
As a simple illustration, team ability estimates in the home-advantage
model for the baseball data are obtained by:
``` {r BTabilities}
BTabilities(baseballModel2)
This gives, for each team, the estimated ability when the team enjoys no
home advantage.
Similarly, estimates of the fighting ability of each lizard in the
`flatlizards` data under the model based on the principal components of
the spectral reflectance from the throat are obtained as follows:
``` {r BTabilities2}
head(BTabilities(lizModel2), 4)
The ability estimates in an unstructured Bradley-Terry model are
particularly well suited to presentation using the device of
quasi-variances [@firt:04]. The qvcalc
package [@firt:10,version 0.8-5 or later] contains a
function of the same name which does the necessary work:
> library("qvcalc")
> baseball.qv <- qvcalc(BTabilities(baseballModel2))
> plot(baseball.qv,
+ levelNames = c("Bal", "Bos", "Cle", "Det", "Mil", "NY", "Tor"))
knitr::include_graphics(c("baseball-qvplot.png"))
The 'comparison intervals' as shown in Figure \@ref(fig:figqvplot) are
based on 'quasi standard errors', and can be interpreted as if they
refer to independent estimates of ability for the journals. This has
the advantage that comparison between any pair of journals is readily
made (i.e., not only comparisons with the 'reference' journal). For
details of the theory and method of calculation see @firt:04.
Residuals {#sec:residuals}
There are two main types of residuals available for a Bradley-Terry
model object.
First, there are residuals obtained by the standard methods for models
of class "glm". These all deliver one residual for each contest or
type of contest. For example, Pearson residuals for the model
lizModel2 can be obtained simply by
``` {r residuals}
res.pearson <- round(residuals(lizModel2), 3)
head(cbind(flatlizards$contests, res.pearson), 4)
More useful for diagnostics on the linear predictor $\sum\beta_rx_{ir}$
are 'player'-level residuals, obtained by using the function `residuals`
with argument `type = "grouped"`. These residuals can then be plotted
against other player-specific variables.
``` {r BTresiduals}
res <- residuals(lizModel2, type = "grouped")
# with(flatlizards$predictors, plot(throat.PC2, res))
# with(flatlizards$predictors, plot(head.width, res))
These residuals estimate the error in the linear predictor; they are
obtained by suitable aggregation of the so-called 'working' residuals
from the model fit. The weights attribute indicates the relative
information in these residuals -- weight is roughly inversely
proportional to variance -- which may be useful for plotting and/or
interpretation; for example, a large residual may be of no real concern
if based on very little information. Weighted least-squares regression
of these residuals on any variable already in the model is null. For
example:
``` {r residualWLS}
lm(res ~ throat.PC1, weights = attr(res, "weights"),
data = flatlizards$predictors)
lm(res ~ head.length, weights = attr(res, "weights"),
data = flatlizards$predictors)
As an illustration of evident *non-null* residual structure, consider
the unrealistically simple model `lizModel` that was fitted in
Section \@ref(sec:covariates) above. That model lacks the clearly
significant predictor variable `throat.PC3`, and the plot shown in
Figure \@ref(fig:figresiduals) demonstrates this fact graphically:
``` r
lizModel.residuals <- residuals(lizModel, type = "grouped")
plot(flatlizards$predictors$throat.PC3, lizModel.residuals)
knitr::include_graphics(c("residuals.png"))
The residuals in the plot exhibit a strong, positive regression slope in
relation to the omitted predictor variable throat.PC3.
Model search {#sec:model}
In addition to update() as illustrated in preceding sections, methods
for the generic functions add1(), drop1() and anova() are
provided. These can be used to investigate the effect of adding or
removing a variable, whether that variable is contest-specific, such as
an order effect, or player-specific; and to compare the fit of nested
models.
Setting up the data {#sec:data}
Contest-specific data {#sec:contest}
The outcome argument of BTm represents a binomial response and can
be supplied in any of the formats allowed by the glm function. That
is, either a two-column matrix with the columns giving the number of
wins and losses (for player1 vs. player2), a factor where the first
level denotes a loss and all other levels denote a win, or a binary
variable where 0 denotes a loss and 1 denotes a win. Each row represents
either a single contest or a set of contests between the same two
players.
The player1 and player2 arguments are either factors specifying the
two players in each contest, or data frames containing such factors,
along with any contest-specific variables that are also player-specific,
such as the at.home variable seen in Section \@ref(sec:order). If
given in data frames, the factors identifying the players should be
named as specified by the id argument and should have identical
levels, since they represent a particular sample of the full set of
players.
Thus for the model baseballModel2, which was specified by the
following call:
``` {r baseballModel2_call}
baseballModel2$call
the data are provided in the `baseball` data frame, which has the
following structure:
``` {r str_baseball}
str(baseball, vec.len = 2)
In this case home.team and away.team are both data frames, with the
factor team specifying the team and the variable at.home specifying
whether or not the team was at home. So the first comparison
``` {r first_comparison}
baseball$home.team[1,]
baseball$away.team[1,]
is Milwaukee playing at home against Detroit. The outcome is given by
``` {r first_outcome}
baseball[1, c("home.wins", "away.wins")]
Contest-specific variables that are not player-specific -- for
example, whether it rained or not during a contest -- should only be
used in interactions with variables that are player-specific,
otherwise the effect on ability would be the same for both players and
would cancel out. Such variables can conveniently be provided in a
single data frame along with the outcome, player1 and player2
data.
An offset in the model can be specified by using the offset argument
to BTm. This facility is provided for completeness: the authors have
not yet encountered an application where it is needed.
To use only certain rows of the contest data in the analysis, the
subset argument may be used in the call to BTm. This should either
be a logical vector of the same length as the binomial response, or a
numeric vector containing the indices of rows to be used.
Non contest-specific data {#sec:non-contest}
Some variables do not vary by contest directly, but rather vary by a
factor that is contest-specific, such as the player ID or the judge
making the paired comparison. For such variables, it is more economical
to store the data by the levels of the contest-specific factor and use
indexing to obtain the values for each contest.
The CEMS example in Section \@ref(sec:CEMS) provides an illustration
of such variables. In this example student-specific variables are
indexed by student and school-specific variables are indexed by ..,
i.e., the first or second school in the comparison as appropriate. There
are then two extra sets of variables in addition to the usual
contest-specific data as described in the last section. A good way to
provide these data to BTm is as a list of data frames, one for each
set of variables, e.g.,
``` {r str_CEMS}
str(CEMS, vec.len = 2)
The names of the data frames are only used by `BTm` if they match the
names specified in the `player1` and `player2` arguments, in which case
it is assumed that these are data frames providing the data for the
first and second player respectively. The rows of data frames in the
list should either correspond to the contests or the levels of the
factor used for indexing.
Player-specific offsets should be included in the formula by using the
`offset` function.
### Converting data from a 'wide' format {#sec:wide}
The `BTm` function requires data in a 'long' format, with one row per
contest, provided either directly as in Section \@ref(sec:contest) or
via indexing as in Section \@ref(sec:non-contest). In studies where the
same set of paired comparisons are made by several judges, as in a
questionnaire for example, the data may be stored in a 'wide' format,
with one row per judge.
As an example, consider the `cemspc` data from the **prefmod** package
[@hatz:12], which provides data from the
CEMS study in a wide format. Each row corresponds to one student; the
first 15 columns give the outcome of all pairwise comparisons between
the 6 schools in the study and the last two columns correspond to two of
the student-specific variables: `ENG` (indicating the student's
knowledge of English) and `SEX` (indicating the student's gender).
The following steps convert these data into a form suitable for analysis
with `BTm`. First a new data frame is created from the student-specific
variables and these variables are converted to factors:
``` {r student-specific_data}
library("prefmod")
student <- cemspc[c("ENG", "SEX")]
student$ENG <- factor(student$ENG, levels = 1:2,
labels = c("good", "poor"))
student$SEX <- factor(student$SEX, levels = 1:2,
labels = c("female", "male"))
This data frame is put into a list, which will eventually hold all the
necessary data. Then a student factor is created for indexing the
student data to produce contest-level data. This is put in a new data
frame that will hold the contest-specific data.
``` {r student_factor}
cems <- list(student = student)
student <- gl(303, 1, 303 * 15) #303 students, 15 comparisons
contest <- data.frame(student = student)
Next the outcome data is converted to a binomial response, adjusted for
ties. The result is added to the `contest` data frame.
``` {r binomial_response}
win <- cemspc[, 1:15] == 0
lose <- cemspc[, 1:15] == 2
draw <- cemspc[, 1:15] == 1
contest$win.adj <- c(win + draw/2)
contest$lose.adj <- c(lose + draw/2)
Then two factors are created identifying the first and second school in
each comparison. The comparisons are in the order 1 vs. 2, 1 vs. 3, 2
vs. 3, 1 vs. 4, ..., so the factors can be created as follows:
``` {r school_factors}
lab <- c("London", "Paris", "Milano", "St. Gallen", "Barcelona",
"Stockholm")
contest$school1 <- factor(sequence(1:5), levels = 1:6, labels = lab)
contest$school2 <- factor(rep(2:6, 1:5), levels = 1:6, labels = lab)
Note that both factors have exactly the same levels, even though only
five of the six players are represented in each case. In other words,
the numeric factor levels refer to the same players in each case, so
that the player is unambiguously identified. This ensures that
player-specific parameters and player-specific covariates are correctly
specified.
Finally the `contest` data frame is added to the main list:
``` {r cems_data}
cems$contest <- contest
This creates a single data object that can be passed to the data
argument of BTm. Of course, such a list could be created on-the-fly as
in data = list(contest, student), which may be more convenient in
practice.
Converting data from the format required by the earlier BradleyTerry package {#sec:BradleyTerry}
The BradleyTerry package described in @firt:05 required
contest/comparison results to be in a data frame with columns named
winner, loser and Freq. The following example shows how xtabs
and countsToBinomial can be used to convert such data for use with the
BTm function in BradleyTerry2:
library("BradleyTerry") ## the /old/ BradleyTerry package
## load data frame with columns "winner", "loser", "Freq"
data("citations", package = "BradleyTerry")
## convert to 2-way table of counts
citations <- xtabs(Freq ~ winner + loser, citations)
## convert to a data frame of binomial observations
citations.sf <- countsToBinomial(citations)
The citations.sf data frame can then be used with BTm as shown in
Section \@ref(sec:citations).
A list of the functions provided in BradleyTerry2 {#sec:functions}
The standard R help files provide the definitive reference. Here we
simply list the main user-level functions and their arguments, as a
convenient overview:
``` {r functions, echo = FALSE}
cf. prompt
options(width = 55)
for (fn in getNamespaceExports("BradleyTerry2")) {
name <- as.name(fn)
args <- formals(fn)
n <- length(args)
arg.names <- arg.n <- names(args)
arg.n[arg.n == "..."] <- "\dots"
is.missing.arg <- function(arg) typeof(arg) == "symbol" &&
deparse(arg) == ""
Call <- paste(name, "(", sep = "")
for (i in seq_len(n)) {
Call <- paste(Call, arg.names[i], if (!is.missing.arg(args[[i]]))
paste(" = ", paste(deparse(args[[i]]),
collapse = "\n"), sep = ""), sep = "")
if (i != n)
Call <- paste(Call, ", ", sep = "")
}
Call <- paste(Call, ")", sep = "")
cat(deparse(parse(text = Call)[[1]], width.cutoff = 50), fill = TRUE)
}
options(width = 60)
```
Some final remarks {#sec:finalremarks}
A note on the treatment of ties {#sec:ties}
The present version of BradleyTerry2 provides no sophisticated
facilities for handling tied contests/comparisons; the well-known models
of @rao:kupp:67 and @davi:70 are
not implemented here. At present the BTm function requires a binary or
binomial response variable, the third ('tied') category of response is
not allowed.
In several of the data examples (e.g., ?CEMS, ?springall,
?sound.fields), ties are handled by the crude but simple device of
adding half of a 'win' to the tally for each player involved; in each of
the examples where this has been done it is found that the result is
very similar, after a simple re-scaling, to the more sophisticated
analyses that have appeared in the literature. Note that this device
when used with BTm typically gives rise to warnings produced by the
back-end glm function, about non-integer 'binomial' counts; such
warnings are of no consequence and can be safely ignored.
It is likely that a future version of BradleyTerry2 will have a more
general method for handling ties.
A note on 'contest-specific' random effects {#sec:random-effects}
The current version of BradleyTerry2 provides facilities for fitting
models with random effects in 'player-specific' predictor functions, as
illustrated in Section \@ref(#sec:covariates). For more general,
'contest-specific' random-effect structures, such as random 'judge'
effects in psychological studies [e.g., @bock:01],
BradleyTerry2 provides (through BTm) the necessary user interface
but as yet no back-end calculation. It is hoped that this important
generalization can be made successfully in a future version of
BradleyTerry2.
Acknowledgments {#sec:acknowledgments .unnumbered}
This work was supported by the UK Engineering and Physical Sciences
Research Council.
References {#sec:references .unnumbered}
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Contents { .unnumbered}
\@ref(sec:intro) [Introduction]
\@ref(sec:BTmodel) [Standard Bradley-Terry model]
\@ref(sec:citations) [Example: Analysis of journal citations]
\@ref(sec:bias-reduced) [Bias-reduced estimates]
\@ref(sec:covariates) [Abilities predicted by explanatory variables]
\@ref(sec:player-specific) ['Player-specific' predictor variables]
\@ref(sec:missing) [Missing values]
\@ref(sec:order) [Order effect]
\@ref(sec:CEMS) [More general (contest-specific) predictors]
\@ref(sec:ability) [Ability scores]
\@ref(sec:residuals) [Residuals]
\@ref(sec:model) [Model search]
\@ref(sec:data) [Setting up the data]
\@ref(sec:contest) [Contest-specific data]
\@ref(sec:non-contest) [Non contest-specific data]
\@ref(sec:wide) [Converting data from a 'wide' format]
\@ref(sec:BradleyTerry) [Converting data from the format required by the earlier BradleyTerry package]
\@ref(sec:functions) [A list of the functions provided in BradleyTerry2]
\@ref(sec:finalremarks) [Some final remarks]
\@ref(sec:ties) [A note on the treatment of ties]
\@ref(sec:random-effects) [A note on 'contest-specific' random effects]
[Acknowledgments]
[References]
Introduction {#sec:intro}
The Bradley-Terry model [@brad:terr:52] assumes that in a 'contest' between any two 'players', say player $i$ and player $j$ $(i, j \in {1,\ldots,K})$, the odds that $i$ beats $j$ are $\alpha_i/\alpha_j$, where $\alpha_i$ and $\alpha_j$ are positive-valued parameters which might be thought of as representing 'ability'. A general introduction can be found in @brad:84 or @agre:02. Applications are many, ranging from experimental psychology to the analysis of sports tournaments to genetics [for example, the allelic transmission/disequilibrium test of @sham:curt:95 is based on a Bradley-Terry model in which the 'players' are alleles]. In typical psychometric applications the 'contests' are comparisons, made by different human subjects, between pairs of items.
The model can alternatively be expressed in the logit-linear form
$$\mathop{\rm logit}[\mathop{\rm pr}(i\ \mathrm{beats}\ j)]=\lambda_i-\lambda_j, \label{eq:unstructured} (#eq:unstructured)$$
where $\lambda_i=\log\alpha_i$ for all $i$. Thus, assuming independence of all contests, the parameters ${\lambda_i}$ can be estimated by maximum likelihood using standard software for generalized linear models, with a suitably specified model matrix. The primary purpose of the BradleyTerry2 package [@turn:12], implemented in the R statistical computing environment [@ihak:gent:96;@r], is to facilitate the specification and fitting of such models and some extensions.
The BradleyTerry2 package supersedes the earlier BradleyTerry package [@firt:05], providing a more flexible user interface to allow a wider range of models to be fitted. In particular, BradleyTerry2 allows the inclusion of simple random effects so that the ability parameters can be related to available explanatory variables through a linear predictor of the form
$$\lambda_i=\sum_{r=1}^p\beta_rx_{ir} + U_i. (#eq:autonumber2) $$
The inclusion of the prediction error $U_i$ allows for variability between players with equal covariate values and induces correlation between comparisons with a common player. BradleyTerry2 also allows for general contest-specific effects to be included in the model and allows the logit link to be replaced, if required, by a different symmetric link function (probit or cauchit).
The remainder of the paper is organised as follows. Section \@ref(sec:BTmodel) demonstrates how to use the BradleyTerry2 package to fit a standard (i.e., unstructured) Bradley-Terry model, with a separate ability parameter estimated for each player, including the use of bias-reduced estimation for such models. Section \@ref(sec:covariates) considers variations of the standard model, including the use of player-specific variables to model ability and allowing for contest-specific effects such as an order effect or judge effects. Sections \@ref(sec:ability) and \@ref(sec:residuals) explain how to obtain important information about a fitted model, in particular the estimates of ability and their standard errors, and player-level residuals, whilst Section \@ref(sec:model) notes the functions available to aid model search. Section \@ref(sec:data) explains in more detail how set up data for use with the BradleyTerry2 package, Section \@ref(sec:functions) lists the functions provided by the package and finally Section \@ref(sec:finalremarks) comments on two directions for further development of the software.
Standard Bradley-Terry model {#sec:BTmodel}
Example: Analysis of journal citations {#sec:citations}
The following data come from page 448 of @agre:02,
extracted from the larger table of @stig:94. The data are
counts of citations among four prominent journals of statistics and are
included the BradleyTerry2 package as the data set citations:
``` {r LoadBradleyTerry2} library("BradleyTerry2")
``` {r CitationData}
data("citations", package = "BradleyTerry2")
``` {r CitationData2} citations
Thus, for example, *Biometrika* was cited 498 times by papers in *Journal of the American Statistical Association* (JASA) during the period under study. In order to fit a Bradley-Terry model to these data using `BTm` from the **BradleyTerry2** package, the data must first be converted to binomial frequencies. That is, the data need to be organised into pairs (`player1`, `player2`) and corresponding frequencies of wins and losses for `player1` against `player2`. The **BradleyTerry2** package provides the utility function `countsToBinomial` to convert a contingency table of wins to the format just described: ``` {r countsToBinomial} citations.sf <- countsToBinomial(citations) names(citations.sf)[1:2] <- c("journal1", "journal2") citations.sf
Note that the self-citation counts are ignored -- these provide no information on the ability parameters, since the abilities are relative rather than absolute quantities. The binomial response can then be modelled by the difference in player abilities as follows:
``` {r citeModel} citeModel <- BTm(cbind(win1, win2), journal1, journal2, ~ journal, id = "journal", data = citations.sf) citeModel
The coefficients here are maximum likelihood estimates of $\lambda_2, \lambda_3, \lambda_4$, with $\lambda_1$ (the log-ability for *Biometrika*) set to zero as an identifying convention. The one-sided model formula ``` r ~ journal
specifies the model for player ability, in this case the 'citeability'
of the journal. The id argument specifies that "journal" is the name
to be used for the factor that identifies the player -- the values of
which are given here by journal1 and journal2 for the first and
second players respectively. Therefore in this case a separate
citeability parameter is estimated for each journal.
If a different 'reference' journal is required, this can be achieved
using the optional refcat argument: for example, making use of
update to avoid re-specifying the whole model,
``` {r citeModelupdate} update(citeModel, refcat = "JASA")
-- the same model in a different parameterization.
The use of the standard Bradley-Terry model for this application might
perhaps seem rather questionable -- for example, citations within a
published paper can hardly be considered independent, and the model
discards potentially important information on self-citation. @stig:94
provides arguments to defend the model's use despite such
concerns.
### Bias-reduced estimates {#sec:bias-reduced}
Estimation of the standard Bradley-Terry model in `BTm` is by default
computed by maximum likelihood, using an internal call to the `glm`
function. An alternative is to fit by bias-reduced maximum likelihood
[@firt:93]: this requires additionally the **brglm** package
[@kosm:07], and is specified by the optional argument
`br = TRUE`. The resultant effect, namely removal of first-order
asymptotic bias in the estimated coefficients, is often quite small. One
notable feature of bias-reduced fits is that all estimated coefficients
and standard errors are necessarily finite, even in situations of
'complete separation' where maximum likelihood estimates take infinite
values [@hein:sche:02].
For the citation data, the parameter estimates are only very slightly
changed in the bias-reduced fit:
``` {r citeModelupdate2}
update(citeModel, br = TRUE)
Here the bias of maximum likelihood is small because the binomial counts are fairly large. In more sparse arrangements of contests -- that is, where there is less or no replication of the contests -- the effect of bias reduction would typically be more substantial than the insignificant one seen here.
Abilities predicted by explanatory variables {#sec:covariates}
'Player-specific' predictor variables {#sec:player-specific}
In some application contexts there may be 'player-specific' explanatory variables available, and it is then natural to consider model simplification of the form
$$\lambda_i=\sum_{r=1}^p\beta_rx_{ir} + U_i, (#eq:autonumber3) $$
in which ability of each player $i$ is related to explanatory variables
$x_{i1},\ldots,x_{ip}$ through a linear predictor with coefficients
$\beta_1,\ldots,\beta_p$; the ${U_i}$ are independent errors.
Dependence of the player abilities on explanatory variables can be
specified via the formula argument, using the standard S-language
model formulae. The difference in the abilities of player $i$ and player
$j$ is modelled by
$$\sum_{r=1}^p\beta_rx_{ir} - \sum_{r=1}^p\beta_rx_{jr} + U_i - U_j, \label{eq:structured} (#eq:structured)$$
where $U_i \sim N(0, \sigma^2)$ for all $i$. The Bradley-Terry model is
then a generalized linear mixed model, which the BTm function
currently fits by using the penalized quasi-likelihood algorithm of
@bres:93.
As an illustration, consider the following simple model for the
flatlizards data, which predicts the fighting ability of Augrabies
flat lizards by body size (snout to vent length):
``` {r lizModel} options(show.signif.stars = FALSE) data("flatlizards", package = "BradleyTerry2") lizModel <- BTm(1, winner, loser, ~ SVL[..] + (1|..), data = flatlizards)
Here the winner of each fight is compared to the loser, so the outcome
is always 1. The special name '`..`' appears in the formula as the
default identifier for players, in the absence of a user-specified `id`
argument. The values of this factor are given by `winner` for the
winning lizard and `loser` for the losing lizard in each contest. These
factors are provided in the data frame `contests` that is the first
element of the list object `flatlizards`. The second element of
`flatlizards` is another data frame, `predictors`, containing
measurements on the observed lizards, including `SVL`, which is the
snout to vent length. Thus `SVL[..]` represents the snout to vent length
indexed by lizard (`winner` or `loser` as appropriate). Finally a random
intercept for each lizard is included using the bar notation familiar to
users of the **lme4** package [@bate:11]. (Note that a random intercept is the only random effect structure
currently implemented in **BradleyTerry2**.)
The fitted model is summarized below:
``` {r summarize_lizModel}
summary(lizModel)
The coefficient of snout to vent length is weakly significant; however, the standard deviation of the random effect is quite large, suggesting that this simple model has fairly poor explanatory power. A more appropriate model is considered in the next section.
Missing values {#sec:missing}
The contest data may include all possible pairs of players and hence
rows of missing data corresponding to players paired with themselves.
Such rows contribute no information to the Bradley-Terry model and are
simply discarded by BTm.
Where there are missing values in player-specific predictor (or explanatory) variables which appear in the formula, it will typically be very wasteful to discard all contests involving players for which some values are missing. Instead, such cases are accommodated by the inclusion of one or more parameters in the model. If, for example, player $1$ has one or more of its predictor values $x_{11},\ldots,x_{1p}$ missing, then the combination of Equations \@ref(eq:unstructured) and \@ref(eq:structured) above yields
$$\mathop{\rm logit}[\mathop{\rm pr}(1\ \mathrm{beats}\ j)]=\lambda_1 - \left(\sum_{r=1}^p\beta_rx_{jr} + U_j\right), (#eq:autonumber5) $$
for all other players $j$. This results in the inclusion of a 'direct' ability parameter for each player having missing predictor values, in addition to the common coefficients $\beta_1,\ldots,\beta_p$ -- an approach which will be appropriate when the missingness mechanism is unrelated to contest success. The same device can be used also to accommodate any user-specified departures from a structured Bradley-Terry model, whereby some players have their abilities determined by the linear predictor but others do not.
In the original analysis of the flatlizards data [@whit:06],
the final model included the first and third principal components
of the spectral reflectance from the throat (representing brightness and
UV intensity respectively) as well as head length and the snout to vent
length seen in our earlier model. The spectroscopy data was missing for
two lizards, therefore the ability of these lizards was estimated
directly. The following fits this model, with the addition of a random
intercept as before:
``` {r lizModel2} lizModel2 <- BTm(1, winner, loser, ~ throat.PC1[..] + throat.PC3[..] + head.length[..] + SVL[..] + (1|..), data = flatlizards) summary(lizModel2)
Note that `BTm` detects that lizards 96 and 99 have missing values in
the specified predictors and automatically includes separate ability
parameters for these lizards. This model was found to be the single best
model based on the principal components of reflectance and the other
predictors available and indeed the standard deviation of the random
intercept is much reduced, but still highly significant. Allowing for
this significant variation between lizards with the same predictor
values produces more realistic (i.e., larger) standard errors for the
parameters when compared to the original analysis of @whit:06.
Although this affects the significance of the morphological
variables, it does not affect the significance of the principal
components, so in this case does not affect the main conclusions of the
study.
### Order effect {#sec:order}
In certain types of application some or all contests have an associated
'bias', related to the order in which items are presented to a judge or
with the location in which a contest takes place, for example. A natural
extension of the Bradley-Terry model (Equation \@ref(eq:unstructured))
is then
$$\mathop{\rm logit}[\mathop{\rm pr}(i\ \mathrm{beats}\ j)]=\lambda_i-\lambda_j + \delta z,
(\#eq:autonumber6) $$
where $z=1$ if $i$ has the supposed advantage and $z=-1$ if $j$ has it.
(If the 'advantage' is in fact a disadvantage, $\delta$ will be
negative.) The scores $\lambda_i$ then relate to ability in the absence
of any such advantage.
As an example, consider the baseball data given in @agre:02, page 438:
``` {r baseball}
data("baseball", package = "BradleyTerry2")
head(baseball)
The data set records the home wins and losses for each baseball team
against each of the 6 other teams in the data set. The head function
is used to show the first 6 records, which are the Milwaukee home games.
We see for example that Milwaukee played 7 home games against Detroit
and won 4 of them. The 'standard' Bradley-Terry model without a
home-advantage parameter will be fitted if no formula is specified in
the call to BTm:
``` {r baseballModel} baseballModel1 <- BTm(cbind(home.wins, away.wins), home.team, away.team, data = baseball, id = "team") summary(baseballModel1)
The reference team is Baltimore, estimated to be the weakest of these seven, with Milwaukee and Detroit the strongest. In the above, the ability of each team is modelled simply as ` team` where the values of the factor `team` are given by `home.team` for the first team and `away.team` for the second team in each game. To estimate the home-advantage effect, an additional variable is required to indicate whether the team is at home or not. Therefore data frames containing both the team factor and this new indicator variable are required in place of the factors `home.team` and `away.team` in the call to `BTm`. This is achieved here by over-writing the `home.team` and `away.team` factors in the `baseball` data frame: ``` {r baseballDataUpdate} baseball$home.team <- data.frame(team = baseball$home.team, at.home = 1) baseball$away.team <- data.frame(team = baseball$away.team, at.home = 0)
The at.home variable is needed for both the home team and the away
team, so that it can be differenced as appropriate in the linear
predictor. With the data organised in this way, the ability formula can
now be updated to include the at.home variable as follows:
``` {r baseballModelupdate} baseballModel2 <- update(baseballModel1, formula = ~ team + at.home) summary(baseballModel2)
This reproduces the results given on page 438 of @agre:02: the home team has an estimated odds-multiplier of $\exp(0.3023) = 1.35$ in its favour. ### More general (contest-specific) predictors {#sec:CEMS} The 'home advantage' effect is a simple example of a contest-specific predictor. Such predictors are necessarily interactions, between aspects of the contest and (aspects of) the two 'players' involved. For more elaborate examples of such effects, see `?chameleons` and `?CEMS`. The former includes an 'experience' effect, which changes through time, on the fighting ability of male chameleons. The latter illustrates a common situation in psychometric applications of the Bradley-Terry model, where *subjects* express preference for one of two *objects* (the 'players'), and it is the influence on the results of subject attributes that is of primary interest. As an illustration of the way in which such effects are specified, consider the following model specification taken from the examples in `?CEMS`, where data on students' preferences in relation to six European management schools is analysed. ``` {r CEMSmodel} data("CEMS", package = "BradleyTerry2") table8.model <- BTm(outcome = cbind(win1.adj, win2.adj), player1 = school1, player2 = school2, formula = ~ .. + WOR[student] * LAT[..] + DEG[student] * St.Gallen[..] + STUD[student] * Paris[..] + STUD[student] * St.Gallen[..] + ENG[student] * St.Gallen[..] + FRA[student] * London[..] + FRA[student] * Paris[..] + SPA[student] * Barcelona[..] + ITA[student] * London[..] + ITA[student] * Milano[..] + SEX[student] * Milano[..], refcat = "Stockholm", data = CEMS)
This model reproduces results from Table 8 of @ditt:01
apart from minor differences due to the
different treatment of ties. Here the outcome is the binomial frequency
of preference for school1 over school2, with ties counted as half a
'win' and half a 'loss'. The formula specifies the model for school
'ability' or worth. In this formula, the default label '..' represents
the school (with values given by school1 or school2 as appropriate)
and student is a factor specifying the student that made the
comparison. The remaining variables in the formula use
[R]{.sans-serif}'s standard indexing mechanism to include
student-specific variables, e.g., WOR: whether or not the student was
in full-time employment, and school-specific variables, e.g., LAT:
whether the school was in a 'Latin' city. Thus there are three types of
variables: contest-specific (school1, school2, student),
subject-specific (WOR, DEG, ...) and object-specific (LAT,
St.Gallen, ...). These three types of variables are provided in three
data frames, contained in the list object CEMS.
Ability scores {#sec:ability}
The function BTabilities extracts estimates and standard errors for
the log-ability scores $\lambda_1, \ldots,\lambda_K$. These will either
be 'direct' estimates, in the case of the standard Bradley-Terry model
or for players with one or more missing predictor values, or
'model-based' estimates of the form
$\hat\lambda_i=\sum_{r=1}^p\hat\beta_rx_{ir}$ for players whose ability
is predicted by explanatory variables.
As a simple illustration, team ability estimates in the home-advantage
model for the baseball data are obtained by:
``` {r BTabilities} BTabilities(baseballModel2)
This gives, for each team, the estimated ability when the team enjoys no
home advantage.
Similarly, estimates of the fighting ability of each lizard in the
`flatlizards` data under the model based on the principal components of
the spectral reflectance from the throat are obtained as follows:
``` {r BTabilities2}
head(BTabilities(lizModel2), 4)
The ability estimates in an unstructured Bradley-Terry model are particularly well suited to presentation using the device of quasi-variances [@firt:04]. The qvcalc package [@firt:10,version 0.8-5 or later] contains a function of the same name which does the necessary work:
> library("qvcalc") > baseball.qv <- qvcalc(BTabilities(baseballModel2)) > plot(baseball.qv, + levelNames = c("Bal", "Bos", "Cle", "Det", "Mil", "NY", "Tor"))
knitr::include_graphics(c("baseball-qvplot.png"))
The 'comparison intervals' as shown in Figure \@ref(fig:figqvplot) are based on 'quasi standard errors', and can be interpreted as if they refer to independent estimates of ability for the journals. This has the advantage that comparison between any pair of journals is readily made (i.e., not only comparisons with the 'reference' journal). For details of the theory and method of calculation see @firt:04.
Residuals {#sec:residuals}
There are two main types of residuals available for a Bradley-Terry model object.
First, there are residuals obtained by the standard methods for models
of class "glm". These all deliver one residual for each contest or
type of contest. For example, Pearson residuals for the model
lizModel2 can be obtained simply by
``` {r residuals} res.pearson <- round(residuals(lizModel2), 3) head(cbind(flatlizards$contests, res.pearson), 4)
More useful for diagnostics on the linear predictor $\sum\beta_rx_{ir}$ are 'player'-level residuals, obtained by using the function `residuals` with argument `type = "grouped"`. These residuals can then be plotted against other player-specific variables. ``` {r BTresiduals} res <- residuals(lizModel2, type = "grouped") # with(flatlizards$predictors, plot(throat.PC2, res)) # with(flatlizards$predictors, plot(head.width, res))
These residuals estimate the error in the linear predictor; they are
obtained by suitable aggregation of the so-called 'working' residuals
from the model fit. The weights attribute indicates the relative
information in these residuals -- weight is roughly inversely
proportional to variance -- which may be useful for plotting and/or
interpretation; for example, a large residual may be of no real concern
if based on very little information. Weighted least-squares regression
of these residuals on any variable already in the model is null. For
example:
``` {r residualWLS} lm(res ~ throat.PC1, weights = attr(res, "weights"), data = flatlizards$predictors) lm(res ~ head.length, weights = attr(res, "weights"), data = flatlizards$predictors)
As an illustration of evident *non-null* residual structure, consider the unrealistically simple model `lizModel` that was fitted in Section \@ref(sec:covariates) above. That model lacks the clearly significant predictor variable `throat.PC3`, and the plot shown in Figure \@ref(fig:figresiduals) demonstrates this fact graphically: ``` r lizModel.residuals <- residuals(lizModel, type = "grouped") plot(flatlizards$predictors$throat.PC3, lizModel.residuals)
knitr::include_graphics(c("residuals.png"))
The residuals in the plot exhibit a strong, positive regression slope in
relation to the omitted predictor variable throat.PC3.
Model search {#sec:model}
In addition to update() as illustrated in preceding sections, methods
for the generic functions add1(), drop1() and anova() are
provided. These can be used to investigate the effect of adding or
removing a variable, whether that variable is contest-specific, such as
an order effect, or player-specific; and to compare the fit of nested
models.
Setting up the data {#sec:data}
Contest-specific data {#sec:contest}
The outcome argument of BTm represents a binomial response and can
be supplied in any of the formats allowed by the glm function. That
is, either a two-column matrix with the columns giving the number of
wins and losses (for player1 vs. player2), a factor where the first
level denotes a loss and all other levels denote a win, or a binary
variable where 0 denotes a loss and 1 denotes a win. Each row represents
either a single contest or a set of contests between the same two
players.
The player1 and player2 arguments are either factors specifying the
two players in each contest, or data frames containing such factors,
along with any contest-specific variables that are also player-specific,
such as the at.home variable seen in Section \@ref(sec:order). If
given in data frames, the factors identifying the players should be
named as specified by the id argument and should have identical
levels, since they represent a particular sample of the full set of
players.
Thus for the model baseballModel2, which was specified by the
following call:
``` {r baseballModel2_call} baseballModel2$call
the data are provided in the `baseball` data frame, which has the
following structure:
``` {r str_baseball}
str(baseball, vec.len = 2)
In this case home.team and away.team are both data frames, with the
factor team specifying the team and the variable at.home specifying
whether or not the team was at home. So the first comparison
``` {r first_comparison} baseball$home.team[1,] baseball$away.team[1,]
is Milwaukee playing at home against Detroit. The outcome is given by
``` {r first_outcome}
baseball[1, c("home.wins", "away.wins")]
Contest-specific variables that are not player-specific -- for
example, whether it rained or not during a contest -- should only be
used in interactions with variables that are player-specific,
otherwise the effect on ability would be the same for both players and
would cancel out. Such variables can conveniently be provided in a
single data frame along with the outcome, player1 and player2
data.
An offset in the model can be specified by using the offset argument
to BTm. This facility is provided for completeness: the authors have
not yet encountered an application where it is needed.
To use only certain rows of the contest data in the analysis, the
subset argument may be used in the call to BTm. This should either
be a logical vector of the same length as the binomial response, or a
numeric vector containing the indices of rows to be used.
Non contest-specific data {#sec:non-contest}
Some variables do not vary by contest directly, but rather vary by a factor that is contest-specific, such as the player ID or the judge making the paired comparison. For such variables, it is more economical to store the data by the levels of the contest-specific factor and use indexing to obtain the values for each contest.
The CEMS example in Section \@ref(sec:CEMS) provides an illustration
of such variables. In this example student-specific variables are
indexed by student and school-specific variables are indexed by ..,
i.e., the first or second school in the comparison as appropriate. There
are then two extra sets of variables in addition to the usual
contest-specific data as described in the last section. A good way to
provide these data to BTm is as a list of data frames, one for each
set of variables, e.g.,
``` {r str_CEMS} str(CEMS, vec.len = 2)
The names of the data frames are only used by `BTm` if they match the names specified in the `player1` and `player2` arguments, in which case it is assumed that these are data frames providing the data for the first and second player respectively. The rows of data frames in the list should either correspond to the contests or the levels of the factor used for indexing. Player-specific offsets should be included in the formula by using the `offset` function. ### Converting data from a 'wide' format {#sec:wide} The `BTm` function requires data in a 'long' format, with one row per contest, provided either directly as in Section \@ref(sec:contest) or via indexing as in Section \@ref(sec:non-contest). In studies where the same set of paired comparisons are made by several judges, as in a questionnaire for example, the data may be stored in a 'wide' format, with one row per judge. As an example, consider the `cemspc` data from the **prefmod** package [@hatz:12], which provides data from the CEMS study in a wide format. Each row corresponds to one student; the first 15 columns give the outcome of all pairwise comparisons between the 6 schools in the study and the last two columns correspond to two of the student-specific variables: `ENG` (indicating the student's knowledge of English) and `SEX` (indicating the student's gender). The following steps convert these data into a form suitable for analysis with `BTm`. First a new data frame is created from the student-specific variables and these variables are converted to factors: ``` {r student-specific_data} library("prefmod") student <- cemspc[c("ENG", "SEX")] student$ENG <- factor(student$ENG, levels = 1:2, labels = c("good", "poor")) student$SEX <- factor(student$SEX, levels = 1:2, labels = c("female", "male"))
This data frame is put into a list, which will eventually hold all the
necessary data. Then a student factor is created for indexing the
student data to produce contest-level data. This is put in a new data
frame that will hold the contest-specific data.
``` {r student_factor} cems <- list(student = student) student <- gl(303, 1, 303 * 15) #303 students, 15 comparisons contest <- data.frame(student = student)
Next the outcome data is converted to a binomial response, adjusted for
ties. The result is added to the `contest` data frame.
``` {r binomial_response}
win <- cemspc[, 1:15] == 0
lose <- cemspc[, 1:15] == 2
draw <- cemspc[, 1:15] == 1
contest$win.adj <- c(win + draw/2)
contest$lose.adj <- c(lose + draw/2)
Then two factors are created identifying the first and second school in each comparison. The comparisons are in the order 1 vs. 2, 1 vs. 3, 2 vs. 3, 1 vs. 4, ..., so the factors can be created as follows:
``` {r school_factors} lab <- c("London", "Paris", "Milano", "St. Gallen", "Barcelona", "Stockholm") contest$school1 <- factor(sequence(1:5), levels = 1:6, labels = lab) contest$school2 <- factor(rep(2:6, 1:5), levels = 1:6, labels = lab)
Note that both factors have exactly the same levels, even though only five of the six players are represented in each case. In other words, the numeric factor levels refer to the same players in each case, so that the player is unambiguously identified. This ensures that player-specific parameters and player-specific covariates are correctly specified. Finally the `contest` data frame is added to the main list: ``` {r cems_data} cems$contest <- contest
This creates a single data object that can be passed to the data
argument of BTm. Of course, such a list could be created on-the-fly as
in data = list(contest, student), which may be more convenient in
practice.
Converting data from the format required by the earlier BradleyTerry package {#sec:BradleyTerry}
The BradleyTerry package described in @firt:05 required
contest/comparison results to be in a data frame with columns named
winner, loser and Freq. The following example shows how xtabs
and countsToBinomial can be used to convert such data for use with the
BTm function in BradleyTerry2:
library("BradleyTerry") ## the /old/ BradleyTerry package ## load data frame with columns "winner", "loser", "Freq" data("citations", package = "BradleyTerry") ## convert to 2-way table of counts citations <- xtabs(Freq ~ winner + loser, citations) ## convert to a data frame of binomial observations citations.sf <- countsToBinomial(citations)
The citations.sf data frame can then be used with BTm as shown in
Section \@ref(sec:citations).
A list of the functions provided in BradleyTerry2 {#sec:functions}
The standard R help files provide the definitive reference. Here we simply list the main user-level functions and their arguments, as a convenient overview:
``` {r functions, echo = FALSE}
cf. prompt
options(width = 55) for (fn in getNamespaceExports("BradleyTerry2")) { name <- as.name(fn) args <- formals(fn) n <- length(args) arg.names <- arg.n <- names(args) arg.n[arg.n == "..."] <- "\dots" is.missing.arg <- function(arg) typeof(arg) == "symbol" && deparse(arg) == "" Call <- paste(name, "(", sep = "") for (i in seq_len(n)) { Call <- paste(Call, arg.names[i], if (!is.missing.arg(args[[i]])) paste(" = ", paste(deparse(args[[i]]), collapse = "\n"), sep = ""), sep = "") if (i != n) Call <- paste(Call, ", ", sep = "") } Call <- paste(Call, ")", sep = "") cat(deparse(parse(text = Call)[[1]], width.cutoff = 50), fill = TRUE) } options(width = 60) ```
Some final remarks {#sec:finalremarks}
A note on the treatment of ties {#sec:ties}
The present version of BradleyTerry2 provides no sophisticated
facilities for handling tied contests/comparisons; the well-known models
of @rao:kupp:67 and @davi:70 are
not implemented here. At present the BTm function requires a binary or
binomial response variable, the third ('tied') category of response is
not allowed.
In several of the data examples (e.g., ?CEMS, ?springall,
?sound.fields), ties are handled by the crude but simple device of
adding half of a 'win' to the tally for each player involved; in each of
the examples where this has been done it is found that the result is
very similar, after a simple re-scaling, to the more sophisticated
analyses that have appeared in the literature. Note that this device
when used with BTm typically gives rise to warnings produced by the
back-end glm function, about non-integer 'binomial' counts; such
warnings are of no consequence and can be safely ignored.
It is likely that a future version of BradleyTerry2 will have a more general method for handling ties.
A note on 'contest-specific' random effects {#sec:random-effects}
The current version of BradleyTerry2 provides facilities for fitting
models with random effects in 'player-specific' predictor functions, as
illustrated in Section \@ref(#sec:covariates). For more general,
'contest-specific' random-effect structures, such as random 'judge'
effects in psychological studies [e.g., @bock:01],
BradleyTerry2 provides (through BTm) the necessary user interface
but as yet no back-end calculation. It is hoped that this important
generalization can be made successfully in a future version of
BradleyTerry2.
Acknowledgments {#sec:acknowledgments .unnumbered}
This work was supported by the UK Engineering and Physical Sciences Research Council.
References {#sec:references .unnumbered}
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