[R] glmmADMB: Generalized Linear Mixed Models using AD Model Builder

[Roel de Jong]

Of course it is generally possible to generate datasets for a perfectly 
well-defined model that are hard to fit, but in this particular case I 
feel it should be possible. In my observations, glmm.admb is far more 
numerically stable fitting GLMM's than other software I've seen. Further 
, I don't think the data I generated come from a model that is 
overparameterized, severely contaminated with outliers, has no noise, or 
is nonlinear. But I encourage anyone to run a simulation study with 
generated data they think are acceptable and compare the robustness of 
several methods. I leave it at this.

Best regards,
	Roel de Jong

Berton Gunter wrote:
> May I interject a comment?
> 
> 
>>When data is generated from a specified model with reasonable 
>>parameter 
>>values, it should be possible to fit such a model successful, 
>>or is this 
>>me being stupid?
> 
> 
> Let me take a turn at being stupid. Why should this be true? That is, why
> should it be possible to easily fit a model that is generated ( i.e. using a
> pseudo-random number generator) from a perfectly well-defined model? For
> example, I can easily generate simple linear models contaminated with
> outliers that are quite difficult to fit (e.g. via resistant fitting
> methods). In nonlinear fitting, it is quite easy to generate data from
> oevrparameterized models that are quite difficult to fit or whose fit is
> very sensitive to initial conditions. Remember: the design (for the
> covariates) at which you fit the data must support the parameterization.
> 
> The most dramatic examples are probably of simple nonlinear model systems
> with no noise which produce chaotic results when parameters are in certain
> ranges. These would be totally impossible to recover from the "data."
> 
> So I repeat: just because you can generate data from a simple model, why
> should it be easy to fit the data and recover the model? 
> 
> Cheers,
> 
> Bert Gunter
> Genentech
> 
>