Bradley Terry GLMM in R ?

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 at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be


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Op vr 7 okt. 2022 om 15:00 schreef Shira Mitchell <shiraqotj at 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

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