They have different coefficients because their model matrices are different but they both lead to the same predictions:
> fitted(lm(y~1+x+I(x^2))) 1 2 3 4 5 6 7 8 9 10 1 4 9 16 25 36 49 64 81 100 > fitted(lm(y~poly(x,2))) 1 2 3 4 5 6 7 8 9 10 1 4 9 16 25 36 49 64 81 100 On Wed, Jun 10, 2009 at 1:41 AM, Ning Ma<pnin...@gmail.com> wrote: > hi, > > I want to do a polynomial regression of y on x of degree 2, as following > >> x<-1:10 >> y<-x^2 >> lm(y~poly(x,2)) > > Call: > lm(formula = y ~ poly(x, 2)) > > Coefficients: > (Intercept) poly(x, 2)1 poly(x, 2)2 > 38.50 99.91 22.98 > > Which is not what i had expected. > > If I wrote the expression in an explicit form, y~1+x+I(x^2), I could > get the expected result: > >> lm(y~1+x+I(x^2)) > > Call: > lm(formula = y ~ 1 + x + I(x^2)) > > Coefficients: > (Intercept) x I(x^2) > 0 0 1 > > What is the diff between them? > > ______________________________________________ > 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.
