# Prediction when using orthogonal polynomials in regression Classic List Threaded 3 messages Open this post in threaded view
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## Prediction when using orthogonal polynomials in regression

 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 + 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  [hidden email]                             http://ajayshahblog.blogspot.com<*(:-? - wizard who doesn't know the answer. ______________________________________________ [hidden email] mailing list https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide! http://www.R-project.org/posting-guide.html