On Thu, 26 Jan 2006 22:10:23 +0530 Ajay Narottam Shah wrote:

> 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")

fitted(d) is usually the preferred way of accessing the fitted values

(although equivalent in this particular case).

> 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?

Use predict:

predict(d, data.frame(x = xnew))

which is pretty evocative.

Best,

Z

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