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

W Robert Long longrob604 at gmail.com
Thu Apr 10 10:38:13 CEST 2014

Hi Ben

For my model, I get

 >   max(abs(relgrad))
[1] 1.081706

Does this help ?


On 10/04/2014 03:33, Ben Bolker wrote:
> Ben Bolker <bbolker at ...> writes:
>> On 14-04-06 04:31 AM, Tibor Kiss wrote:
>>> 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
>>    It means that on the scale of the _standard deviations_ of the
>> parameters, the estimated gradients at the MLE (or restricted MLE) are
>> not large.  I was surprised in Rob's case that these scaled gradients
>> were not that small; much smaller than without the scaling, but not
>> small enough to make me think  really understand what's going on.
>>    To recapitulate: the appearance of all of these new messages in the
>> latest version of lme4 is **not** due to a degradation or change in the
>> optimization or fitting procedure -- it's due to a new set of
>> convergence tests that we implemented, that we think are giving a lot of
>> false positives.  You can easily shut them off yourself, or raise the
>> tolerance for the warnings (see ?lmerControl/?glmerControl).  As
>> developers, we're a bit stuck now because we don't want to turn the
>> warnings _off_ until we understand the circumstances that are triggering
>> them better, and that takes more time and effort than we've been able to
>> muster so far.
>    [much context snipped]
>    Just to follow up on this: more technical discussion is going on at
> https://github.com/lme4/lme4/issues/120 ... at present, it is looking
> like scaling the gradient by the hessian is going to solve a lot of
> problems.  If you are experiencing convergence warnings about
> max|grad| that you suspect are false positives, it would be a great
> help if you could try
>    relgrad <- with(fitted_model at optinfo$derivs,solve(Hessian,gradient))
>    max(abs(relgrad))
> check if the result is a small number (e.g. <0.001) and report **one
> way or the other** on this list, or at the Github url above, or
> (least preferred) by e-mailing lme4-authors at lists.r-forge.r-project.org
> We also hope that this test *will* pick up the cases where people have
> reported problems with Nelder-Mead not working properly ...
>    Ben Bolker
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