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

[Andrea Céspedes]

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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