Re: [R] Non-linear "linear" models?

From: Doran, Harold <HDoran_at_air.org>
Date: Mon 25 Jul 2005 - 23:21:10 EST


Paul:

Even when the model includes polynomial terms as you have below, it is still a linear model because it is linear in the parameters. It is your coefficients that are not linear. There are other functions in R for non-linear models.

help.search('non linear')

-----Original Message-----
From: r-help-bounces@stat.math.ethz.ch
[mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of eph1v3t8-rhls6783@mailblocks.com
Sent: Saturday, July 23, 2005 10:24 AM
To: r-help@stat.math.ethz.ch
Subject: [R] Non-linear "linear" models?

Hi,

    I'm new to R (though I have spent hours trying to learn how to use it) and also not very knowledgeable
about statistics, so I hope you will excuse what may seem like a very basic question. I'm trying to use R to do an ANOVA analysis for some data with an unbalanced design, and while I was trying to figure that out, I got confused about the purpose of the "lm". All definitions I can find of "linear model" are of the
form:
y = a + b * x + e

  In other words, y is only linear in the dependent variable(s) x. However, the lm model seems to support higher order polynomials, e.g.:

> lm(dist ~ speed + I(speed^2)+I(speed^3), cars)

  Is there some sense in which that model is "linear", or is R's lm() providing extra functionality?

Thanks,
--Paul



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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Mon Jul 25 23:28:55 2005

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