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 > -- > 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 > ______________________________________________ [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
