Hi R users
I'm trying to fit a model y=ax^b. I know if I made ln(y)=ln(a)+bln(x) this is a linear regression. But I obtain differente results with nls() and lm() My commands are: nls(CV ~a*Est^b, data=limiares, start =list(a=100,b=0), trace = TRUE) for nonlinear regression and : lm(ln_CV~ln_Est, data=limiares) for linear regression Nonlinear regression model: a=738.2238151 and b=-0.3951013 Linear regression: Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 7.8570224 0.0103680 757.8 <2e-16 *** ln_Est -0.5279412 0.0008658 -609.8 <2e-16 *** I think it should be a=exp("(Intercept) ") = exp(7.8570224) = 2583.815 and b=ln_Est Probably I'm wrong, but why?? Thanks in advance. Ana Quiterio [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html