I guess, I would somehow feel interested that someone out of my field is trying to use the tool I daily manipulate to widen his knowledge or dig further in his own field, though I might at some point recommend him to get back to the basis for some concrete concepts that he wouldn't understand sharply enough. I hope reading "Practical Regression and Anova using R" by J.J. Faraway will help sharpening out a bit my knowledge's. For sure though, I'd sound less scornful than you.
BTW, thank you Daniel for pointing out that I was using orthogonal polynomials instead of regular ones. Regards/Cordialement Benoit Boulinguiez -----Message d'origine----- De : Bert Gunter [mailto:gunter.ber...@gene.com] Envoyé : mardi 22 décembre 2009 18:26 À : 'Benoit Boulinguiez'; r-help@r-project.org Objet : RE: [R] use of lm() and poly() Get some statistical consulting help or read up on these topics -- any good textbook on regression should contain the necessary material. This has nothing to do with nonlinear regression, so you are confused about the basic ideas. It has nothing to do with R. If you don't understand how the statistical tools work, you shouldn't be using them (without help, anyway). Would you feel comfortable about me playing in your chemistry lab based on my year of college chemistry ~45 years ago? Bert Gunter Genentech Nonclinical Biostatistics -----Original Message----- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Benoit Boulinguiez Sent: Tuesday, December 22, 2009 9:10 AM To: r-help@r-project.org Subject: [R] use of lm() and poly() Hi all, I want to fit data called "metal" with a polynominal function as dP ~ a.0 + a.1 * U0 + a.2 * U0^2 + a.3 * U0^3 + a.4 * U0^4 The data set includes, the independant variable U0 and the dependant variable dP. I've seen that the combination of lm() and poly() can do that instead of using the nls() function. But I don't get how to interpret the results from the linear regression, as the coefficients do not match the ones from the nonlinear regression #data metal U0 dP 1 0.00 0 2 0.76 10 3 1.43 20 4 2.56 40 5 3.05 50 6 3.52 60 7 3.76 70 8 4.05 80 9 4.24 90 10 4.47 100 #linear d <- seq(0, 4, length.out = 200) for(degree in 1:4) { fm <- lm(dP ~ poly(U0, degree), data = metal) assign(paste("metal", degree, sep="."), fm) lines(d, predict(fm, data.frame(U0=d)), col = degree) } metal.4 Call: lm(formula = dP ~ poly(U0, degree), data = metal) Coefficients: (Intercept) poly(U0, degree)1 poly(U0, degree)2 poly(U0, degree)3 poly(U0, degree)4 52.000 100.612 19.340 7.101 2.628 #nonlinear fm<-nls (dP~ a.0 + a.1*U0 + a.2*U0^2 + a.3*U0^3 + a.4*U0^4, data=metal) Nonlinear regression model model: dP ~ a.0 + a.1 * U0 + a.2 * U0^2 + a.3 * U0^3 + a.4 * U0^4 data: metal a.0 a.1 a.2 a.3 a.4 0.02408 9.81452 5.54269 -2.24657 0.36737 residual sum-of-squares: 5.843 Number of iterations to convergence: 2 Achieved convergence tolerance: 1.378e-06 Regards/Cordialement ------------- Benoit Boulinguiez Ph.D student Ecole de Chimie de Rennes (ENSCR) Bureau 1.20 Equipe CIP UMR CNRS 6226 "Sciences Chimiques de Rennes" Avenue du Giniral Leclerc CS 50837 35708 Rennes CEDEX 7 Tel 33 (0)2 23 23 80 83 Fax 33 (0)2 23 23 81 20 <http://www.ensc-rennes.fr/> http://www.ensc-rennes.fr/ [[alternative HTML version deleted]] ______________________________________________ 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.