[R] Regarding nls()

[Spencer Graves]

      My summary of Bates' comments cited below is as follows: 

           1.  ANOVA is an excellent tool but requires nested models.  
You can do this fairly easily, but it is not so easily automated. 

           2.  The standard definition of R^2 loses its meaning with 
nonlinear models.  Adjusted R^2 is even worse. 

      Bates' condemnation of R^2 has merit, but I would not go as far as 
he did in the comment cited below (dated 13 Aug 2000).  A standard 
definition of R^2 is as follows: 

            R^2 = (1 - var(prediction error) / var(obs)). 

      I can name several different ways of getting a negative R^2 in 
this case.  When that happens, it says the model is worse than useless, 
and you would be better off using the training set mean. 

      If I have an audience who wants an R^2 in an application where it 
is not clear what it even means, I try to briefly explain some of the 
difficulties while asking what question they are trying to solve using 
R^2.  Their answers will help me make a recommendation, which may 
include selecting which of the possible generalizations of R^2 to use. 

      Hope this helps. 
      Spencer Graves

Dieter Menne wrote:
> Guru S <guru.rcom <at> rediffmail.com> writes:
>
>   
>> I have no problem performing the regression using R, and I successfully 
>> obtain the parameter estimates using the function nls(). However, how do I 
>> obtain the ANOVA output, r, r^2 and adj. r^2? 
>>     
>
> This is a feature, not a bug. See Douglas Bates's comments on 
>
> http://www.ens.gu.edu.au/ROBERTK/R/HELP/00B/0399.HTML
>
>
> Dieter
>
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