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

[Ben Bolker]

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

https://github.com/dmbates/MixedModels.jl/blob/master/docs/jmd/constructors.jmd#L104


say:

> 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.

https://github.com/dmbates/MixedModels.jl/blob/fd22daf226938a4f98ce3d4b228e5a99437e3218/src/pls.jl#L61

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
>
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