[R-sig-ME] lme4, failure to converge with a range of optimisers, trust the fitted model anyway?

[Ken Beath]

Yes, the model and so the likelihood is exactly the same, just that there
is a lot less effort in calculating it for the grouped data. Hopefully this
results in less numerical problems.

On 5 April 2015 at 23:53, Hans Ekbrand <hans.ekbrand at gmail.com> wrote:

> On Sun, Apr 05, 2015 at 07:31:25PM +1000, Ken Beath wrote:
> > You also still need a random effect for the cluster.
>
> I think I've just stumbled into something that may deepen my
> understanding of mixed-models, thanks to you.
>
> I took your advice on how to create the dependent variable
>
> cbind(y, n-y)
>
> and included the random term for cluster,
>
> Formula: cbind(Deprived, Not.deprived) ~ (1 | Country) + (1 | ClusterID) +
> QoG + GDPLog + Rural * KilledPerMillion5Log
> Data: my.small.df
>
> and fitted the model to an aggregated version of the original data
> set. However, while so doing I thought: "this will be exactly like the
> model that glmer could not fit without warnings, I'll have exactly the
> same warnings again".
>
> In a sense it is the same model, the beta-coefficients are exactly the
> same, but in another sense it apparently is not, the warnings are gone
> :-)
>
> I guess the difference is that glmer does not have to care about
> residuals at the individual level anymore.
>
> I now understand this model as a kind of repeated measures model,
> where each cluster is measured repeatedly, once for each individual in
> the cluster. While that tecnically does not describe how the data was
> generated, it is a clever shortcut to get what I need. Thanks again!
>
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>



-- 

*Ken Beath*
Lecturer
Statistics Department
MACQUARIE UNIVERSITY NSW 2109, Australia

Phone: +61 (0)2 9850 8516

Building E4A, room 526
http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/

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