Hi Bill,
I've just spent a few months trying to fit a model to a dataset, and
it's not
easy. However, in my case, what appears to be recalcitrance on R's part
actually turns out to be well-founded warnings that the structure of the
model and the data are not permitting a clean, unambiguous fit.
I warmly recommend the Bates and Watts book on non-linear regression analysis,
and also Venables and Ripley MASS, with especial reference to the online
complements.
My strategy to learn more about my problem was: increase "tol" and
decrease
minFactor until I get a fit, and then examine the intrinsic and parameter
curvatures using the rms.curv() function in the MASS package. It showed
quite clearly that there was a real problem with the parameterization of the
model. Of course, using that particular fit was out of the question.
I recommend against trying another tool: instead, learn more about this one
and why it's behaving in that way. Good luck.
Andrew
On Thursday 01 April 2004 07:37, Bill Shipley wrote:> Hello. I am trying to fit a non-rectangular hyperbola function to data
> of photosynthetic rate vs. light intensity. There are 4 parameters that
> have to be estimated. I find the nls function very difficult to use
> because it often fails to converge and then gives out cryptic error
> messages. I have tried playing with the control parameters but this
> does not always help.
>
> Is there another non-linear regression function in R that I might try
> (other than regression smoothers, which won?t give the parameter
> estimates of the specified function)?
>
>
>
> Bill Shipley
>
> Subject Matter Editor, Ecology
>
> North American Editor, Annals of Botany
>
> D??partement de biologie, Universit?? de Sherbrooke,
>
> Sherbrooke (Qu??bec) J1K 2R1 CANADA
>
> Bill.Shipley at USherbrooke.ca
>
> <http://callisto.si.usherb.ca:8080/bshipley/>
> http://callisto.si.usherb.ca:8080/bshipley/
>
>
>
>
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>
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--
Andrew Robinson Ph: 208 885 7115
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