[R-sig-ME] lme4/glmer convergence warnings

[Ben Bolker]

  I think this is a false positive, caused by our recent introduction of
new convergence tests. There's been lots of discussion of this on the
list recently.

   I have a new trouble-shooting idea:

if g0 is your fitted model, can you see what happens if you scale the
estimated gradients by the curvature/standard errors?

gg <- g0 at optinfo$derivs$grad
hh <- g0 at optinfo$derivs$Hessian
vv <- sqrt(diag(solve(hh/2)))
summary(abs(gg*vv))


On 14-04-02 06:40 AM, W Robert Long wrote:
> I should perhaps also mention that of the 9 covariates, 3 are continous
> and I have tried standardising them. Of the remaining 6, 5 are binary
> and the last one is ordinal.
> 
> On 02/04/2014 11:28, W Robert Long wrote:
>> Hi all
>>
>> I am running a simple random intercepts model using lme4 on
>> approximately 70,000 observations, with 250 clusters. The code looks like
>>
>> glmer(Y~x1+x2+x3+x4+x5+x6+x7+x8+x9+(1|clusdID),
>>      data=dt1, family=binomial(link=logit))
>>
>> and I receive the following warnings:
>>
>> Warning messages:
>> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv,  :
>>    Model failed to converge with max|grad| = 4847.75 (tol = 0.001)
>> 2: In if (resHess$code != 0) { :
>>    the condition has length > 1 and only the first element will be used
>> 3: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv,  :
>>    Model is nearly unidentifiable: very large eigenvalue
>>   - Rescale variables?;Model is nearly unidentifiable: large eigenvalue
>> ratio
>>   - Rescale variables?
>>
>> There are some small clusters (<10 obs per cluster), but even removing
>> those, the warnings remain.
>>
>> Using Stata -xtmelogit- there are no warnings and the output is almost
>> identical to glmer() so this gives me some comfort, yet I still worry
>> about these warnings from glmer.
>>
>> I have tried setting nAGQ as high as 10, to no avail.
>>
>> Could anyone suggest what I can look for or change ? The data are
>> confidential so I can't easily make a reprodicible example.
>>
>> Thanks in advance
>> Robert Long
>>
>>
>>
> 
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