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

[Ben Pelzer]

Dear Hans,

You could try Ben's suggestion for nAGQ = 5 in Stata. I believe that the 
routine"s "xtlogit" and "xtmelogit"  (in Stata 13 they are named 
differently, I don't remember the names right now)  are able do work 
with nested random effects and more than 1 quadr. point. These routines 
are however pretty time-consuming, may be even more so than glmer. Best 
regards,

Ben.



On 4-4-2015 20:15, Hans Ekbrand wrote:
> On Sat, Apr 04, 2015 at 09:10:35PM +1100, Ken Beath wrote:
>> One of the problems is that you have a relatively high random effects
>> variance. A standard deviation of the intercept of 3 is a huge amount, it
>> means that there is massive variation in the random effect value needed to
>> model each cluster, to the point that some clusters will be all zeros and
>> some will be all ones. In this situation the assumption of approximate
>> normality of the likelihood around the nodes which is required for using
>> Laplace's method is very far from met.
> Thanks for your advice, I really appreciate it!
>
> I tried nAGQ=5, but met with:
>
> ## Error: nAGQ > 1 is only available for models with a single, scalar
> random-effects term
>
> As you point out, some clusters will be all zeros (and some will be
> all ones). While my data is on the individual level,
>
> a) the variable I'm mainly interested in, KilledPerMillion5Log, varies
> only at the country level, &
>
> b) I currently have no variables in the model that vary at the
> individual level
>
> So, perhaps I could aggregate the individual level data to the cluster
> level, and do without the random term for cluster? I mean, calculate
> the proportions of yes in each cluster and use that as the dependent
> variable.
>
> This would, I assume, require that each cluster was given a weight
> that corresponded to the number of individuals in it - or I would not
> be able to say anything about probabilities at the individual level,
> right?
>
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