Re: [R] Coefficient of Determination for nonlinear function

From: Bert Gunter <gunter.berton_at_gene.com>
Date: Fri, 04 Mar 2011 08:20:36 -0800

The coefficient of determination, R^2, is a measure of how well your model fits versus a "NULL" model, which is that the data are constant. In nonlinear models, as opposed to linear models, such a null model rarely makes sense. Therefore the coefficient of determination is generally not meaningful in nonlinear modeling.

Yet another way in which linear and nonlinear models fundamentally differ.

On Fri, Mar 4, 2011 at 5:40 AM, Uwe Wolfram <uwe.wolfram_at_uni-ulm.de> wrote:
> Dear Subscribers,
>
> I did fit an equation of the form 1 = f(x1,x2,x3) using a minimization
> scheme. Now I want to compute the coefficient of determination. Normally
> I would compute it as
>
> r_square = 1- sserr/sstot with sserr = sum_i (y_i - f_i) and sstot =
> sum_i (y_i - mean(y))
>
> sserr is clear to me but how can I compute sstot when there is no such
> thing than differing y_i. These are all one. Thus mean(y)=1. Therefore,
> sstot is 0.
>
> Thank you very much for your efforts,
>
> Uwe
> --
> Uwe Wolfram
> Dipl.-Ing. (Ph.D Student)
> __________________________________________________
> Institute of Orthopaedic Research and Biomechanics
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> Center of Musculoskeletal Research Ulm
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-- 
Bert Gunter
Genentech Nonclinical Biostatistics
467-7374
http://devo.gene.com/groups/devo/depts/ncb/home.shtml

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Received on Fri 04 Mar 2011 - 16:28:25 GMT

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