Folks,
I'm doing fine with using orthogonal polynomials in a regression context:
# We will deal with noisy data from the d.g.p. y = sin(x) + e
x <- seq(0, 3.141592654, length.out=20)
y <- sin(x) + 0.1*rnorm(10)
d <- lm(y ~ poly(x, 4))
plot(x, y, type="l"); lines(x, d$fitted.values, col="blue") # Fits great!
all.equal(as.numeric(d$coefficients[1] + m %*% d$coefficients[2:5]),
as.numeric(d$fitted.values))
What I would like to do now is to apply the estimated model to do
prediction for a new set of x points e.g.
xnew <- seq(0,5,.5)
We know that the predicted values should be roughly sin(xnew). What I
don't know is: how do I use the object `d' to make predictions for
xnew?
--
Ajay Shah http://www.mayin.org/ajayshah
[EMAIL PROTECTED] http://ajayshahblog.blogspot.com
<*(:-? - wizard who doesn't know the answer.
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
