Hi all,
I'm trying to do a simple curve fit and coming up with some interesting
results I would like to get comment on.
So as shown below, tsR is my explanatory and response is... well... my
response.
This same data in gnumeric gets fitted with the curve "response=10078.4 +
1358.67 * ln (explanatory - 2009.07)
So I'm using nls with the start values supplied by gnumeric.
in "First Time Through" I'm very close to the values gnumeric found, I get
NaNs and Infinities.
In "Second Time Through" I use the "exact" values given by gnumeric... and
it all come together fine.
The difference between start value in First and Second time through is
practically insignificant and gnumeric was able to fit this without effort.
At this rate R is basically useless to me -- what can I change here to set R
up better for this non-linear fit?
What a shame... such a beautiful curve :-)
**********************Data*******************************
> response
[1] 7062.93 7608.92 8168.12 8500.33 8447.00 9171.61 9496.28 9712.28
[9] 9712.73 10344.84 10428.05 10067.33 10325.26 10856.63 11008.61 10136.63
[17] 9774.02 10465.94 10319.95
> tsR
[1] 2009.167 2009.250 2009.333 2009.417 2009.500 2009.583 2009.667 2009.750
[9] 2009.833 2009.917 2010.000 2010.083 2010.167 2010.250 2010.333 2010.417
[17] 2010.500 2010.583 2010.667
***********************First Time Through*******************************
> reFit <- nls(response ~ A + B * log(tsR - C),
start=c(A=10000,B=1350,C=2000))
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
In addition: Warning message:
In log(tsR - C) : NaNs produced
***********************Second Time Through*******************************
> reFit <- nls(response ~ A + B *
log(tsR-C),start=c(A=10078.4,B=1358.67,C=2009.07))
> summary(reFit)
Formula: response ~ A + B * log(tsR - C)
Parameters:
Estimate Std. Error t value Pr(>|t|)
A 1.008e+04 1.427e+02 70.628 < 2e-16 ***
B 1.359e+03 2.997e+02 4.534 0.000339 ***
C 2.009e+03 7.314e-02 27468.499 < 2e-16 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 388.8 on 16 degrees of freedom
Number of iterations to convergence: 6
Achieved convergence tolerance: 6.394e-06
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