Binomial generalised linear mixed model using JuliaCall.

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 ... ?
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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Binomial generalised linear mixed model using JuliaCall.

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