[R-sig-ME] Bradley Terry GLMM in R ?

Sun Oct 16 23:49:00 CEST 2022

Update: Dr Heather Turner <https://www.heatherturner.net/> (author of
BradleyTerry2) suggested the hglm package, which unlike lme4 allows you to
specify generic design matrices (no longer constrained to lme4 formulas !)
Results look really similar to INLA so far. Yay !

On Sun, Oct 16, 2022 at 2:19 PM Shira Mitchell <shiraqotj using gmail.com> wrote:

> Super helpful !  Thank you so much !
>
> Out of curiosity, is there a way to fit this type of Bradley-Terry model
> in lme4 ? lme4 formulas include random effect via syntax:
> https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf
> "(expr | factor). The expression expr is evaluated as a linear model
> formula, producing a model matrix following the same rules used in standard
> R modeling functions (e.g., `lm` or `glm`). The expression factor is
> evaluated as an `R` factor. One way to think about the vertical bar
> operator is as a special kind of interaction between the model matrix and
> the grouping factor. This interaction ensures that the columns of the model
> matrix have different effects for each level of the grouping factor."
>
> So (expr | factor) is X_expr * alpha_factor.
>
> So naively writing ~ (1 | m_1) + (1 | m_2) is alpha_{m_1}^{(1)} +
> alpha_{m_2}^{(2)}, twice as many parameters as what we want which is
> alpha_{m_1} - alpha_{m_2}.
>
> But then see this stackexchange:
>
> https://stats.stackexchange.com/questions/483833/opposing-effects-in-lme4-formulae-bradley-terry-model
> "I could just make a design matrix, where player 1 gets the value 1, and
> player 2 gets the value −1. However, unless I'm missing a trick, this would
> require having a separate column for each player, and plugging each player
> column's name into the formula"
>
> But suppose we create columns for all m = 1,...,M messages:
>
> A_m = 1 if m = m_1
>            -1 if m = m_2
>             0 otherwise
>
> I think then ~ (A_1 + ... + A_M  | m_1) is alpha_{m_1}^{(m_1)} -
> alpha_{m_1}^{(m_2)}, also not what we would want.
>
> Back to INLA. Suppose we now want to add random message-specific slopes
> for variable X_i in addition to random message-specific intercepts:
>
> P[i chooses m_1] = logit^-1 (beta_0 + (alpha_{m_1} - alpha_{m_2}) +
> (beta_{m_1} - beta_{m_2})X_i)
>
> alpha_1,...,alpha_M ~ N(0,sigma_intercept)
> beta_1,...,beta_M ~ N(0,sigma_slope)
>
> I see some resources about this, but nothing super comprehensive. Any
> advice where to look for complete documentation ?
>
> https://groups.google.com/g/r-inla-discussion-group/c/iQELaQF8M9Q/m/q7f4-W8YQksJ
> (
> https://becarioprecario.bitbucket.io/inla-gitbook/ch-multilevel.html#multilevel-models-for-longitudinal-data
> https://rpubs.com/corey_sparks/431920
> https://avianecologist.com/2016/10/05/multilevel-models/
>
> Here is what we did:
>
> data$w_X = -data$X
> data$m_1_beta = data$m_1
> data$m_2_beta = data$m_2
>
> inla(depvar ~  f(m_1, model="iid", values = issues) +
>                            f(m_2, w, copy = "m_1") +
>                            f(m_1_beta, X, model="iid", values = issues) +
>                            f(m_2_beta, w_X, copy = "m_1_beta"),
>                          family="binomial",
>                          data=data)
>
>
>
>
> On Mon, Oct 10, 2022 at 4:24 AM Thierry Onkelinx <thierry.onkelinx using inbo.be>
> wrote:
>
>> Dear Shira,
>>
>> - in a formula object means remove that object from the formula. Use a
>> weight of -1 instead.
>>
>> f(home, model = "iid")) + f(away, w = -1, copy = "home")
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Statisticus / Statistician
>>
>> Vlaamse Overheid / Government of Flanders
>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
>> AND FOREST
>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>> thierry.onkelinx using inbo.be
>> Havenlaan 88 bus 73, 1000 Brussel
>> www.inbo.be
>>
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>> To call in the statistician after the experiment is done may be no more
>> than asking him to perform a post-mortem examination: he may be able to say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of data.
>> ~ John Tukey
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>> <https://www.inbo.be>
>>
>>
>> Op vr 7 okt. 2022 om 23:36 schreef Shira Mitchell <shiraqotj using gmail.com>:
>>
>>> Thanks so much, Thierry ! This is great.
>>>
>>> This works except that I cannot subtract because:
>>> f(home, model = "iid")) - f(away, copy = "home")
>>>
>>> just drops the second term. Apologies that I'm not super familiar with
>>> INLA syntax yet.
>>>
>>>
>>>
>>> On Fri, Oct 7, 2022 at 10:19 AM Thierry Onkelinx <
>>> thierry.onkelinx using inbo.be> wrote:
>>>
>>>> Hi Shira,
>>>>
>>>> I fit such models with the INLA package (https://www.r-inla.org/). The
>>>> trick is to define two random effects but force their parameter estimates
>>>> to be identical.
>>>>
>>>> The code would contain something like f(home, model = "iid")) + f(away,
>>>> copy = "home"). Meaning home ~ N(0, sigma_beta_i) and home[i] = away[i]
>>>>
>>>> Best regards,
>>>>
>>>> ir. Thierry Onkelinx
>>>> Statisticus / Statistician
>>>>
>>>> Vlaamse Overheid / Government of Flanders
>>>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
>>>> AND FOREST
>>>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>>>> thierry.onkelinx using inbo.be
>>>> Havenlaan 88 bus 73, 1000 Brussel
>>>> www.inbo.be
>>>>
>>>>
>>>> ///////////////////////////////////////////////////////////////////////////////////////////
>>>> To call in the statistician after the experiment is done may be no more
>>>> than asking him to perform a post-mortem examination: he may be able to say
>>>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>>>> The plural of anecdote is not data. ~ Roger Brinner
>>>> The combination of some data and an aching desire for an answer does
>>>> not ensure that a reasonable answer can be extracted from a given body of
>>>> data. ~ John Tukey
>>>>
>>>> ///////////////////////////////////////////////////////////////////////////////////////////
>>>>
>>>> <https://www.inbo.be>
>>>>
>>>>
>>>> Op vr 7 okt. 2022 om 15:00 schreef Shira Mitchell <shiraqotj using gmail.com
>>>> >:
>>>>
>>>>> We want to fit Bradley-Terry-style GLMM models in R. We looked into:
>>>>>
>>>>>
>>>>> https://cran.r-project.org/web/packages/BradleyTerry2/vignettes/BradleyTerry.pdf
>>>>> and
>>>>> http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html
>>>>>
>>>>> We have voter-specific variables x that influence which political
>>>>> message
>>>>> (i vs j) wins for them:
>>>>>
>>>>> logit[pr(i beats j | person with covariate x)] = lambda_i - lambda_j +
>>>>> (beta_i - beta_j) x
>>>>>
>>>>> We then model parameters as random effects:
>>>>> lambda_i ~ N(0, sigma_lambda)
>>>>> beta_i ~ N(0, sigma_beta)
>>>>>
>>>>> Is there a way to do this in R ? We do this in TensorFlow in Python by
>>>>> directly specifying design matrices with the 0,-1,1 or 0,-x,x entries.
>>>>> However, I do not see how to do this in R using lme4, BradleyTerry2,
>>>>> mgcv,
>>>>> etc.
>>>>>
>>>>> Thanks so much,
>>>>> Shira
>>>>>
>>>>>         [[alternative HTML version deleted]]
>>>>>
>>>>> _______________________________________________
>>>>> R-sig-mixed-models using r-project.org mailing list
>>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>>
>>>>

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