From: Berton Gunter <gunter.berton_at_gene.com>

Date: Tue 20 Dec 2005 - 11:11:34 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Dec 20 19:16:07 2005

Date: Tue 20 Dec 2005 - 11:11:34 EST

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> 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?
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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

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Dec 20 19:16:07 2005

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