glmmADMB: Generalized Linear Mixed Models using AD Model Builder

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