On Wed, 2005-06-29 at 18:19 +0200, Andreas Neumann wrote: > Dear all, > > I am using poly() in lm() in the following form. > > 1> DelsDPWOS.lm3 <- lm(DelsPDWOS[,1] ~ poly(DelsPDWOS[,4],3)) > > 2> DelsDPWOS.I.lm3 <- lm(DelsPDWOS[,1] ~ poly(I(DelsPDWOS[,4]),3)) > > 3> DelsDPWOS.2.lm3 <- > lm(DelsPDWOS[,1]~DelsPDWOS[,4]+I(DelsPDWOS[,4]^2)+I(DelsPDWOS[,4]^3)) > > 1 and 2 lead to identical but wrong results. 3 is correct. Surprisingly > (to me) the residuals are the same (correct) in all cases although the > coefficients of 1 (and 2) are wrong and do not in any way match the > stated regression polynomial. (summaries below) > > QUESTION: > Is there a correct way to use poly() in lm() since version 3 becomes quite > tedious if used more often especially with higher order polynomials? >
The coefficients using 1 and 2 are not incorrect. poly() defines orthogonal polynomials, whereas your DelsPDWOS[,4]+I(DelsPDWOS[,4]^2)+I(DelsPDWOS[,4]^3 contruct defines an ordinary polynomial. You should be able to recover version 3 coefficients from 1 and 2. See help(poly) > x <- runif(10) > x [1] 0.1878 0.2415 0.5834 0.6556 0.4112 0.3399 0.8144 0.1134 0.7360 0.0463 > model.matrix(~ poly(x, 2)) (Intercept) poly(x, 2)1 poly(x, 2)2 1 1 -0.27648 -0.0452 2 1 -0.21052 -0.1899 3 1 0.20937 -0.2708 4 1 0.29799 -0.1021 5 1 -0.00212 -0.4117 6 1 -0.08970 -0.3621 7 1 0.49297 0.4968 8 1 -0.36790 0.2148 9 1 0.39672 0.1620 10 1 -0.45033 0.5082 attr(,"assign") [1] 0 1 1 > model.matrix(~ x + I(x^2)) (Intercept) x I(x^2) 1 1 0.1878 0.03528 2 1 0.2415 0.05834 3 1 0.5834 0.34040 4 1 0.6556 0.42982 5 1 0.4112 0.16911 6 1 0.3399 0.11554 7 1 0.8144 0.66320 8 1 0.1134 0.01286 9 1 0.7360 0.54169 10 1 0.0463 0.00214 attr(,"assign") [1] 0 1 2 > Regards, -- Markus Jantti Abo Akademi University [EMAIL PROTECTED] http://www.iki.fi/~mjantti ______________________________________________ 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
