Re: [R] Regression on non linear model

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Subject: Re: [R] Regression on non linear model
From: Bill Venables (William.Venables@cmis.CSIRO.AU)
Date: Thu 14 Oct 1999 - 16:58:00 EST

Message-Id: <199910140700.RAA23011@snowy.nsw.cmis.CSIRO.AU>

> >
> > The preferred way to do this is to use the I() function to protect
> > the ^2 and ^3 from being evaluated as part of the linear model
> > formula. That is, write the call to lm with
> >
> > formula = Response ~ Var1 + I(Var1^2) + I(Var1^3)
> I was doing this in a practical class yesterday. There is another way,
> Response ~ poly(Var1, 3)
> which fits the same cubic model by using orthogonal polynomials, and
> that has a number of numerical and statistical advantages. The fitted
> values will be the same, but the coefficients are those of the orthogonal
> polynomials, not the terms in the polynomial. As I was telling my
> students, you might like to compare the two approaches.

One further advantage of doing it this way (in S-PLUS at least) is that you can
plot that component of the fitted curve very simply using plot.gam. Now I know
we don't have a gam() in R yet, but I hope we plan to do so sometime and my
suggestion would be to start with a plot.gam and release that first. It could
be done in a wet weekend (but I regret to say the weekends here are simply
beautiful.... :-)

Bill Venables, Statistician, CMIS Environmetrics Project.

Physical address: Postal address: CSIRO Marine Laboratories, PO Box 120, 233 Middle St, Cleveland, Queensland Cleveland, Qld, 4163 AUSTRALIA AUSTRALIA

Telephone: +61 7 3826 7251 Email: Fax: +61 7 3826 7304

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