Abstract
In linear regression the mean surface in sample space is a plane; in non-linear regression it may be an arbitrary curved surface but in all other respects the models are same. Fortunately in practice the mean surface in most non-linear regression models will be approximately planar in the region of highest likelihood, allowing some good approximations based on linear regression techniques to be used, but non-linear regression models can still present tricky computational and inferential problems.
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In S-PLUS 3.4 and earlier (and possibly later!) there is a bug which may be avoided if the order in which the parameters appear in the start vector is the same as the order in which they first appear in the model. It is as if the names attribute were ignored.
Included in S-PLUS 3.4 and later, and also available from the statlib archive as a library.
If nlme is being used as a library under Unix, these will be in library nlmedata .
Professor Douglas Bates has kindly permitted us to include his predict . nls and anovanis methods in our MASS library.
and differently from the help page which has information.
For this rather complicated-looking model specification, using deriv3 to produce a model function with gradient and hessian attributes may cause memory overflow problems on some machines.
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© 1997 Springer Science+Business Media New York
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Venables, W.N., Ripley, B.D. (1997). Non-linear Models. In: Modern Applied Statistics with S-PLUS. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-2719-7_9
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DOI: https://doi.org/10.1007/978-1-4757-2719-7_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-2721-0
Online ISBN: 978-1-4757-2719-7
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