[R-sig-ME] Help with multilevel model Poisson

[Andrea Céspedes]

Hi everyone

No, it´s 12 observations per day and it´s three days, so I have 36
observations per subject

Thanks


El mié., 9 oct. 2019 a las 9:04, Thierry Onkelinx (<thierry.onkelinx using inbo.be>)
escribió:

> Dear Andrea,
>
> How many observation per subject? 12? That is too few to fit a random
> slope model with 10 parameters.
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
> FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be
>
>
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>
>
> Op wo 9 okt. 2019 om 16:38 schreef Andrea Céspedes <ancelis.07 using gmail.com>:
>
>> Hello
>>
>>
>>
>> I am currently working the shrinkage phenomenon in multilevel models.
>>
>>
>>
>> I have a problem of convergence in the model when I add more random
>> coefficients to the models, after three coefficients,I have this error
>> message:
>>
>>
>>
>>  Warning message:
>>
>> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>>
>>   Model failed to converge with max|grad| = 0.0278398 (tol = 0.001,
>> component 1)
>>
>>
>>
>> I have a three-level model, with Poisson distribution, the structure is:
>> to
>> my subject (rat) I register the vocalizations emitted in four specific
>> moments of an experiment that is repeated for three days. And I have 31
>> subjects. All my variables are dichotomous.
>>
>>
>>
>> I tried several alternatives to solve that error, but the only effective
>> thing was to specify the optimizer = “bobyqa” and more iterations, for my
>> luck it worked,
>>
>> F.aleat.int <- glmer(y ~ 1+DIA2+DIA3+M1+M4+M5+M6+M9+M10+M11+ (1 |
>> ID_DIA:ID_SUJ) + (1+M1+M5+M11 | ID_SUJ), family=poisson, data=base,
>> control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e4)))
>>
>> But the estimates of random effects are much greater than the obtained
>> using other packages (MCMCglmm, glmmLasso, glmmsr and hglm),
>>
>> For example, for one variable I have 2.1 and in the other packages I have
>> 0.8
>>
>> How can I explain that:
>>
>> -        Due to the nature of the model? Or the optimizer as such?
>>
>> -        Would you appreciate it if you could tell me how I can solve
>> that?
>>
>> -        Is it because all my variables are dichotomous?
>>
>>
>>
>> Regards
>>
>>         [[alternative HTML version deleted]]
>>
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>

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