# 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.

• Bert

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)
> __________________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>

```--
Bert Gunter
Genentech Nonclinical Biostatistics
467-7374
http://devo.gene.com/groups/devo/depts/ncb/home.shtml

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