[R] Prediction when using orthogonal polynomials in regression

[Ajay Narottam Shah]

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  
ajayshah at mayin.org                             http://ajayshahblog.blogspot.com
<*(:-? - wizard who doesn't know the answer.