[R] glmmADMB: Generalized Linear Mixed Models using AD Model Builder
spencer.graves at pdf.com
Tue Dec 20 17:49:48 CET 2005
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.
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
>>>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?
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Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
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spencer.graves at pdf.com
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