[R-sig-ME] Binomial generalised linear mixed model using JuliaCall.

Ben Bolker bbolker at gmail.com
Wed Apr 18 05:58:11 CEST 2018

Don't have time to dig into this right now (I'm not an expert), but the docs at



> Note that, in keeping with convention in the [`GLM` package](https://github.com/JuliaStats/GLM.jl), the distribution family for a binary (i.e. 0/1) response is the `Bernoulli` distribution.
The `Binomial` distribution is only used when the response is the
fraction of trials returning a positive, in which case the number of
trials must be specified as the case weights.

   ... which implies that you can do what you want.


seem to give 'weights' as the third argument to the LinearMixedModel
function ... ?

On Tue, Apr 17, 2018 at 8:16 PM, Rolf Turner <r.turner at auckland.ac.nz> wrote:
> Hi all.
> I seem to have managed (not quite sure how! :-) ) with considerable
> assistance from the maintainer of the JuliaCall package, to get that package
> running.
> I am however having a struggle to figure out how to fit the sort of model
> that I am interested in.  The vignette that Doug Bates provided a while back
> gives an example of fitting a linear mixed model, and I have managed to
> reproduce that example.  (My results differed from those that Doug's
> vignette showed, ever so slightly in the values of some of the variance
> components, but I'm not going to worry my pretty little head about that.)
> Now I want to proceed to my "real problem" which is to fit a *generalised*
> linear mixed model, of the binomial persuasion, and I cannot find my way to
> documentation on how to do this.
> The syntax for the sort of fit that I want would be, in the glmer() context,
> of the form:
> fit <- glmer(cbind(Dead,Alive) ~ (Trt+0)/prd + (prd | Rep),data=Dat,
>              family=binomial(link=logit))
> where "Trt" is a factor (fixed effect), "prd" is a numeric predictor,
> and "Rep" is a factor (random effect).
> Can anyone instruct me (in very simple terms please, I'm slow!) as to what
> the analogous syntax would be using the MixedModels package in Julia?  Or
> point me at an elementary tutorial on doing this?
> I have the impression, from one item that I saw on StackExchange, that
> MixedModels doesn't really handle general binomial models, only Bernoulli
> models.  Consequently one has to expand out one's data set
> creating one row for each success ("Dead" in my case) and one row for each
> failure ("Alive").  Is this correct, or is there an easier way to go about
> it?
> Thanks for any words of wisdom.
> cheers,
> Rolf Turner
> Technical Editor ANZJS
> Department of Statistics
> University of Auckland
> Phone: +64-9-373-7599 ext. 88276
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
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