From: Ajay Narottam Shah <ajayshah_at_mayin.org>

Date: Fri 27 Jan 2006 - 03:40:23 EST

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]),

Date: Fri 27 Jan 2006 - 03:40:23 EST

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. ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Fri Jan 27 21:53:20 2006

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