[R] glmmADMB: Generalized Linear Mixed Models using AD Model Builder
Roel de Jong
dejongroel at gmail.com
Tue Dec 20 12:45:48 CET 2005
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.
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?
> Bert Gunter
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