lm coefficients
The function "poly" produces orthogonal polynomials, and those
depend on the exact combinations of levels of X in "d". Consider the
following:
> round(poly(1:3, 2), 2)
1 2
[1,] -0.71 0.41
[2,] 0.00 -0.82
[3,] 0.71 0.41
> round(poly(1:4, 2), 2)
1 2
[1,] -0.67 0.5
[2,] -0.22 -0.5
[3,] 0.22 -0.5
[4,] 0.67 0.5
Does this answer your question?
spencer graves
Timur Elzhov wrote:
Dear R experts,
Excuse me if my question will be stupid...
I'd like to fit data with x^2 polynomial:
d <- read.table(file = "Oleg.dat", head = TRUE)
d
X T
3720.00 4.113
3715.00 4.123
3710.00 4.132
...
out <- lm(T ~ poly(X, 4), data = d)
out
Call:
lm(formula = T ~ poly(X, 2), data = d)
Coefficients:
(Intercept) poly(X, 2)1 poly(X, 2)2
9.803 -108.075 51.007
So, d$T best fitted with function
9.803 -108.075 * X + 51.007 * X^2,
yes?
T1 <- 9.803 -108.075 * d$X + 51.007 * d$X^2
T1
705453240
703557595
701664500
699773956
...
So, T1 obviosly gets non-sensible values.. :( Why?
Thanks a lot!
--
WBR,
Timur.
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