Jonas, In statistical sense polynomial is a linear regression fit. The function that handles linear fitting is called lm. Here is how you can reproduce your results:
lm(y ~ x + I(x^2) + I(x^3)) Unless you are really after the polynomial coefficients it is probably better to use orthogonal polynomials. You can get this fit by doing lm(y ~ poly(x, 3)) Check out help pages for lm and poly. Hope this helps, Andy __________________________________ Andy Jaworski 518-1-01 Process Laboratory 3M Corporate Research Laboratory ----- E-mail: [EMAIL PROTECTED] Tel: (651) 733-6092 Fax: (651) 736-3122 "Jonas Malmros" <[EMAIL PROTECTED] ail.com> To Sent by: r-help@r-project.org [EMAIL PROTECTED] cc project.org Subject [R] Polynomial fitting 01/07/2008 09:16 AM I wonder how one in R can fit a 3rd degree polynomial to some data? Say the data is: y <- c(15.51, 12.44, 31.5, 21.5, 17.89, 27.09, 15.02, 13.43, 18.18, 11.32) x <- seq(3.75, 6, 0.25) And resulting degrees of polynomial are: 5.8007 -91.6339 472.1726 -774.2584 THanks in advance! -- Jonas Malmros Stockholm University Stockholm, Sweden ______________________________________________ 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. ______________________________________________ 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.