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

[Spencer Graves]

	  I get upset when software dies and refuses to give me an answer.  I'd 
much rather have a routine give me a wrong answer -- with an error 
message -- than just an error message.  Maybe refuse to print standard 
errors when the hessian is singular, but at least give me a progress 
report with the singular hessian.  Without that, I have to program 
"optim" or something else separately to get the answers and the hessian 
in order to do my own diagnosis -- if I know enough to do that.

	  Just my 0.02 Euros.
	  spencer graves

Roel de Jong wrote:

> 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
>>
>>
> 
> 
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-- 
Spencer Graves, PhD
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