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

From: Roel de Jong <dejongroel_at_gmail.com>
Date: Tue 20 Dec 2005 - 22:45:48 EST

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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