Dear all,
I am trying to analyze some non-linear data to which I have fit a curve of
the following form:

dum <- nls(y~(A + (B*x)/(C+x)), start = list(A=370,B=100,C=23000))

I am wondering if there is any way to determine meaningful quality of fit
statistics from the nls function?

A summary yields highly significant p-values, but it is my impression that
these are questionable at best given the iterative nature of the fit:

> summary(dum)

Formula: y ~ (A + (B * x)/(C + x))

Parameters:
   Estimate Std. Error t value Pr(>|t|)
A   388.753      4.794  81.090  < 2e-16 ***
B   115.215      5.006  23.015  < 2e-16 ***
C 20843.832   4646.937   4.485 1.12e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18.25 on 245 degrees of freedom

Number of iterations to convergence: 4
Achieved convergence tolerance: 2.244e-06


Is there any other means of determining the quality of the curve fit? I
have tried applying confidence intervals using confint(dum), but these
curves seem unrealistically narrow. Thanks so much for your help!
-Max

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