Hi,

being somewhat nonplussed by similar messages, I also applied Ben's recent suggestion to one of my models to get:

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
1.343e-05 3.530e-05 5.756e-05 7.631e-05 9.841e-05 1.932e-04 

So following up on Rob's message: What does it mean?

With kind regards

Tibor



 

Am 04.04.2014 um 16:50 schrieb W Robert Long <longrob604@gmail.com>:

> 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@optinfo$derivs$grad
>>> hh <- g0@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
>>>>> 
>>>>> 
>>>>> 
>>>> 
>>>> _______________________________________________
>>>> R-sig-mixed-models@r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>> 
>>> _______________________________________________
>>> R-sig-mixed-models@r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>> 
> 
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