[R] Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works?

[Andrew Robinson]

Dave,

that's an interesting start for a comparison.  Let me point out some
ways that you might construct a compelling argument.  Of course, these
aren't exhaustive, and others may well provide further depth.

1) If I understand correctly, you're trying to estimate parameters
   from a real dataset.  Why not try a simulated dataset, where you
   know exactly what the true values (and parameter distributions)
   are?

2) Furthermore, an argument from one dataset isn't very
   convincing. The sample size for inference is too small.  Why not
   repeat this procedure many times, sampling from the same base
   model? 

3) Then, you could also vary the structure of the underlying model
   systematically, and assess the comparison of fits as a function of
   the underlying model/dataset nexus.

4) Next, a problem with the example (as I understand it) is that
   although you've computed what you call exact MLE's, I think that
   they're exact when conditioned on the model.  Are they very robust
   to model misspecification?  (I mean beyond large-sample theory).

5) Finally, of course, then making the scripts available for forsenic
   investigations.

Cheers,

Andrew

On Thu, Oct 13, 2005 at 01:19:24PM -0700, dave fournier wrote:
>            Do Users of Nonlinear Mixed Effects Models Know
> 
>                   Whether Their Software Really Works?
> 

-- 
Andrew Robinson
Senior Lecturer in Statistics                       Tel: +61-3-8344-9763
Department of Mathematics and Statistics            Fax: +61-3-8344-4599
University of Melbourne, VIC 3010 Australia
Email: a.robinson at ms.unimelb.edu.au    Website: http://www.ms.unimelb.edu.au