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

[Thierry Onkelinx]

36 observations per level is still very little to fit a 10 parameter random
effect

Op wo 9 okt. 2019 18:07 schreef Andrea Céspedes <ancelis.07 using gmail.com>:

> 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
>>
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>> To call in the statistician after the experiment is done may be no more
>> than asking him to perform a post-mortem examination: he may be able to say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of data.
>> ~ John Tukey
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>> <https://www.inbo.be>
>>
>>
>> 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]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>

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