How to add weights parameter to Generalized Mixed Model

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I’ve been working on a RCall compatibility for MixedModels <=> lme4 in my package JellyMe4 (it’s on the Julia registry). The latest release won’t move glmer to GeneralizedMixedModel but should be able to move Bernoulli models in the other direction (and will preserve contrasts). The funny thing there is that I’m able to reproduce @laut 's fit that way:

julia> using RCall, JellyMe4
[ Info: Precompiling RCall [6f49c342-dc21-5d91-9882-a32aef131414]
[ Info: Precompiling JellyMe4 [19ac8677-9a15-4623-9afd-84acc6165ce7]

julia> model = (m, nonzero);
julia> @rput model;
[ Info: contrasts on interaction terms are assumed to decompose into products of contrasts on the first-order terms
[ Info: (if you don't know what that means, you're probably fine)
┌ Warning: Some accuracy is lost in translation for GLMMs. This is fine with plotting, but proceed with caution for inferences.
│ You can try letting the model "reconverge" in lme4 with
│     update(model, control=glmerControl(optimizer="nloptwrap", calc.derivs=FALSE)).
└ @ JellyMe4 ~/.julia/packages/JellyMe4/Hupt7/src/glmerMod.jl:144
[ Info: lme4 handles deviance for GLMMs differently than MixedModels.jl
[ Info: for the correct comparison, examine -2*logLik(RModel) and deviance(JuliaModel)

julia> R"summary(model)"
RObject{VecSxp}
Generalized linear mixed model fit by maximum likelihood (Laplace
  Approximation) [glmerMod]
 Family: binomial  ( logit )
Formula: Survived ~ 1 + Age + Sex + Age:Sex + (1 | Class)
   Data: jellyme4_data
Control: glmerControl(optimizer = "nloptwrap", optCtrl = list(maxeval = 1),  
    calc.derivs = FALSE)

     AIC      BIC   logLik deviance df.resid 
    49.0     54.9    -19.5     39.0       19 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-2.0631 -0.7030  0.5001  0.7373  2.1385 

Random effects:
 Groups Name        Variance Std.Dev.
 Class  (Intercept) 0.4006   0.6329  
Number of obs: 24, groups:  Class, 4

Fixed effects:
                 Estimate Std. Error z value Pr(>|z|)  
(Intercept)        1.2618     0.9218   1.369   0.1710  
AgeChild          -0.1235     1.4687  -0.084   0.9330  
SexMale           -2.6101     1.2214  -2.137   0.0326 *
AgeChild:SexMale   1.8948     1.9781   0.958   0.3381  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
            (Intr) AgChld SexMal
AgeChild    -0.552              
SexMale     -0.666  0.416       
AgChld:SxMl  0.411 -0.728 -0.617

julia> m
Generalized Linear Mixed Model fit by maximum likelihood (nAGQ = 1)
  Survived ~ 1 + Age + Sex + Age & Sex + (1 | Class)
  Distribution: Bernoulli{Float64}
  Link: LogitLink()

  Deviance: 2210.0511

Variance components:
         Column   Variance Std.Dev.
Class (Intercept)  37.72355 6.14195

 Number of obs: 24; levels of grouping factors: 4

Fixed-effects parameters:
─────────────────────────────────────────────────────────────────
                         Estimate  Std.Error     z value  P(>|z|)
─────────────────────────────────────────────────────────────────
(Intercept)              1.26179     3.29347   0.383119    0.7016
Age: Child              -0.123528    3.25191  -0.0379863   0.9697
Sex: Male               -2.61011     1.45291  -1.79647     0.0724
Age: Child & Sex: Male   1.89476     4.19518   0.451653    0.6515
─────────────────────────────────────────────────────────────────

One thing to note is that weights don’t work in the current JellyMe4 release, so those are getting lost in translation. This suggests that something is happening with the weights in your bad run…