Find the prediction or the fitted values for an lm model
See in-line below.
On 11/28/13 20:50, jpm miao wrote:
Hi,
I would like to fit my data with a 4th order polynomial. Now I have only
5 data point, I should have a polynomial that exactly pass the five point
Then I would like to compute the "fitted" or "predict" value with a
relatively large x dataset. How can I do it?
BTW, I thought the model "prodfn" should pass by (0,0), but I just
wonder why the const is unequal to zero
Because poly() produces orthonormalized polynomials, Look at poly(x1,4).
It is not much like cbind(x1,x1^2,x1^3,x1^4), is it?
cheers,
Rolf Turner
x1<-c(0,3,4,5,8) y1<-c(0,1,4,7,8) prodfn<-lm(y1 ~ poly(x1, 4)) x<-seq(0,8,0.01) temp<-predict(prodfn,data.frame(x=x)) # This line does not work..
prodfn
Call:
lm(formula = y1 ~ poly(x1, 4))
Coefficients:
(Intercept) poly(x1, 4)1 poly(x1, 4)2 poly(x1, 4)3 poly(x1, 4)4
4.000e+00 6.517e+00 -4.918e-16 -2.744e+00 -8.882e-16
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