[R] need an R-squared from a nls logistic sigmoid fit

[Gabor Grothendieck]

On 6/5/05, James Salsman <james at bovik.org> wrote:
> Why doesn't nls() produce any kind of R-squared value?  In the absence
> of such information, how are we supposed to compare one fit to another
> when the y-axis scale changes?
> 
> > sm <- nls(y ~ SSfpl(x, miny, maxy, midx, grad))
> > summary(sm)
> 
> Formula: y ~ SSfpl(x, miny, maxy, midx, grad)
> 
> Parameters:
>      Estimate Std. Error t value Pr(>|t|)
> miny  -0.5845     4.6104  -0.127  0.90524
> maxy   7.2680     1.5512   4.686  0.00941 **
> midx  16.9187     2.2340   7.573  0.00163 **
> grad   1.7283     1.9150   0.903  0.41782
> ---
> Signif. codes:  0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
> 
> Residual standard error: 1.13 on 4 degrees of freedom
> 
> Correlation of Parameter Estimates:
>         miny    maxy    midx
> maxy -0.6654
> midx  0.8936 -0.3221
> grad -0.9068  0.8477 -0.6865
> 

One uses anova (which has an anova.nls method) to compare two
nls models.