From: Achim Zeileis <Achim.Zeileis_at_wu-wien.ac.at>

Date: Fri 27 Jan 2006 - 22:31:41 EST

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.html Received on Fri Jan 27 23:19:44 2006

Date: Fri 27 Jan 2006 - 22:31:41 EST

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
**> ajayshah@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.html
**>
*

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.html Received on Fri Jan 27 23:19:44 2006

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