Dear list,

I'm running a set of models in glmer(), some of which return the 'false
convergence' error (cvg=8). I'm trying to understand why.

My models all have the same basic structure: glmer(P ~ Year + (1|Site),
binomial), where P is a vector of 0s and 1s. The Year is centered on zero,
which I've found to greatly reduce the incidence of false convergences in
the past. There are ~100,000 observations and 9000 sites.

I've tried a couple of fixes, including the .Call("mer_optimize",...) hack
as well as an observation-level random effect. the former has no impact on
the parameter estimates and the latter still returns the false convergence
warning.

I'm using verbose=T argument. I've noticed previously that false
convergence is characterised by just 1 or 2 iterations being completed and
variances on the random effects that are either close to zero or
astronomically huge. But my models are running to dozens of iterations with
sensible looking variances that change moderately among iterations but then
stabilise during the last few iterations. And the parameter estimates look
sensible.

In other words, the models do not show any evidence of failure, except the
warning message. So which should I believe: the verbose trace or the
warning message?

Perhaps someone can give me further insight into why glmer() thinks the
model has not properly converged

Best wishes, Nick

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