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

[Thierry Onkelinx]

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
>
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>
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