Dear all, I did a non-linear least square model fit
y ~ a * x^b (a) > nls(y ~ a * x^b, start=list(a=1,b=1)) to obtain the coefficients a & b. I did the same with the linearized formula, including a linear model log(y) ~ log(a) + b * log(x) (b) > nls(log10(y) ~ log10(a) + b*log10(x), start=list(a=1,b=1)) (c) > lm(log10(y) ~ log10(x)) I expected coefficient b to be identical for all three cases. Hoever, using my dataset, coefficient b was: (a) 0.912 (b) 0.9794 (c) 0.9794 Coefficient a also varied between option (a) and (b), 107.2 and 94.7, respectively. Is this supposed to happen? Which is the correct coefficient b? Regards, Wolfgang -- Laboratory of Animal Physiology Department of Biology University of Turku FIN-20014 Turku Finland ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
