[R] nls: how do you know if the model is significant?

[Nerak]

Hi all,
I'm struggling with nls. How do you know if your model is significant? For a
lm, you get a p-value, but you don't get it for a nls. Is there a way to
calculate it?

For a lm I use this:
  a<-summary(lm(model ~obs))
  f.stat<-a$fstatistic
  p.value<-1-pf(f.stat["value"],f.stat["numdf"],f.stat["dendf"])

Is there something similar for a nls?


The kind of output that I get is:


Formula: y ~ exp.f(x, a, b)

Parameters:
   Estimate Std. Error t value Pr(>|t|)    
a 1.381e+02  1.192e+01  11.583 3.19e-08 ***
b 1.790e-02  2.459e-03   7.279 6.19e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 13.21 on 13 degrees of freedom

Number of iterations to convergence: 6 
Achieved convergence tolerance: 9.123e-06 


I know that my parameters are significant but I need to say something about
the whole model.

Many thanks,
Nerak




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