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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