[R-sig-ME] Avoid errors in pwrssUpdate ?

[Pierre Morel]

OK, thanks for the suggestion of glmmADMB, I will look that up.
I also found out that I could get things working with nAGQ=0.
However the doc says it's less exact, and profile() gives a "pp$setTheta(theta) : theta size mismatch" error in this case.

Pierre Morel


Le 13 juin 2012 à 10:54, Ben Bolker a écrit :

> Pierre Morel <pier.morel at ...> writes:
> 
>> I am getting a lot of pwrssUpdate errors when trying to model my
>> data with gmler (using the most recent version from svn... I don't
>> know if the previous versions were affected).
> 
>> These errors are "PIRLS step failed" or "pwrssUpdate did not
>> converge in 30 iterations". I understand that these means that the
>> algorithm does not manage to work with my data, but there is a
>> peculiar behavior, and my data doesn't seem too unreasonable to fit
>> with the model I want to use, so I am wondering if the problem is on
>> my side !
> 
> [snip to make gmane happier]
> 
>> Here is the model I want to fit, which doesn't seem unreasonable
>> given the figure (random slopes and intercepts for subjects) :
> 
>> model<-glmer(cbind(RuleReach,NTrials-RuleReach)~RuleWeight+
>> (RuleWeight|Subject),data=rewardalldirsub,family=binomial)
> 
>> However this gives me the "pwrssUpdate did not converge in 30
>> iterations" error.  What is surprinsing, is that if I do not use the
>> rightmost points (RuleWeight of 1), the model converges, even though
>> there are less datapoints and the remaining points are the noisiest
>> (subjects follow the rule quite reliably when it has a weight of 1
>> as you can see).
> 
>> Removing the correlation in the random effects works sometimes (but
>> not on all my sub data sets), and having a random intercept only
>> (which is obviously not correct) is the only thing that seems to
>> work in all cases.
> 
>> Centering RuleWeight (ie having it between -1 and 1 instead of 0 and
>> 1) doesn't work.  Any ideas on why this doesn't work / how to make
>> it work ?
> 
>  Thanks for the report: this is an issue the developers are
> (painfully) aware of, and working on.  The issue arises mostly when
> the predictions for some observations are very close to 0 or 1 (which
> explains why using the rightmost points helps ...)  You have tried all
> the obvious things I know of.  I would additionally try (1) setting
> starting values by hand and/or (2) trying out glmmADMB ...
> 
>  Ben Bolker
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models