Thanks Thierry, thanks Ben!

I agree that the model needs more thoughts.

Best,

Julia

2014-07-16 20:11 GMT+02:00 Ben Bolker <bbolker@gmail.com>:

> ONKELINX, Thierry <Thierry.ONKELINX@...> writes:
>
> >
> > Dear Julia,
>
> > Note that the smallest category of the response (samemate == 0) has
> > 12 observations. According to a rule of thumb, you need 10
> > observations in each response category per parameter in the
> > model. Hence a model with one (if you're lucky two) parameters is
> > doable.
>
> > Complete separation appears when for a combination of covariates all
> > responses are 0 or 1. Adding complexity to the model, increases the
> > probability of complete separation because you have more possible
> > combination and a lower number of observations per
> > combination. Adding a random effect with 59 levels to a dataset of
> > 66 observations guarantees complete separation...
>
> > The separation of the simple glm(samemate ~ success) is not that
> > bad. But glm(samemate ~ success + type) will give (quasi-)complete
> > separation. The glm model gives implicit warnings (see the last
> > model in your first email): type has a very large effect size and a
> > huge standard error.
>
> There is a short example of how to deal with complete separation
> by setting weakly informative priors at
> http://rpubs.com/bbolker/glmmchapter
> (search for "complete separation"), via the blme or MCMCglmm packages,
> but I would second Thierry's opinion that you may be overcomplicating
> things.
>
>   Ben Bolker
>
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