An error in fitting a non linear regression
Hi Peter, You are totally right and it was a miscalculating and misunderstanding from me. Regarding the R-squared calculation of non linear model (question 2), is there any way to do that? Regards Saeed
Petr Pikal wrote:
Hi r-help-bounces at r-project.org napsal dne 24.02.2009 11:31:22:
Hi, Thank you for the reply and suggestions. I have two questions? 1) If I want to use log, it seems that I have to take log from both
sides of
the model which will lead to lm(log(q)~log(-depth)). What is
tehdifference
between this syntax and lm(log(q) ~ I(-depth))?
If you have y = a*exp(-b*x) then log of this equation is log(y) = log(a) - b * x at least I was told that by my teacher back at school some decades ago that log(exp(x)) = x. You can prove it by log(exp(whatever)) interactively in R Regards Petr
2) How can I calculate the R-squared of a fitted non linear model? Regards Saeed Christian Ritz-3 wrote:
Hi Saeed, one approach is to try out several initial value combinations for a
and b.
It often helps to find initial values of the same order of magnitude
and
of the same sign as the final estimates. To get such initial values, you could linearize the model: lm(log(q) ~ I(-depth)) and supply the estimated coefficients from the linear regression as starting values: nreg <- nls(q ~ a*exp(-b*depth), start = list(a = 0.76168, b =
-0.08484))
summary(nreg) Christian
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