[R] Find the prediction or the fitted values for an lm model

[Rolf Turner]

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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>
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