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

[W Robert Long]

Hi Ben

Does the output I posted in my earlier email help ?

Thanks
Rob

On 02/04/2014 20:25, W Robert Long wrote:
> Hi Ben
>
> Thanks for your reply. The code you posted generates the following:
>
>   Min.   1st Qu.    Median     Mean   3rd Qu.     Max.
> 0.001474 0.023920 0.045420 0.255600 0.068600 2.114000
>
> This model was fitted with the raw data (not standardised continuous
> data) and without removing small clusters.
>
> Thanks again
> Robert Long
>
>
>
>
> On 02/04/2014 14:05, Ben Bolker wrote:
>>
>>    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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