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