[R] non linear models
John Fox
jfox at mcmaster.ca
Mon Oct 1 00:15:06 CEST 2001
Dear Christian,
Polynomial-regression models are linear in the parameters, so they can be
fit by lm. There are several ways to do your example:
> x <- c(-1, 0, 1)
> y <- c(2, 1, 2)
> lm(y ~ I(x^2))
Call:
lm(formula = y ~ I(x^2))
Coefficients:
(Intercept) I(x^2)
1 1
> lm(y ~ x + I(x^2)) # this fits a term in x as well as x^2
Call:
lm(formula = y ~ x + I(x^2))
Coefficients:
(Intercept) x I(x^2)
1.00e+00 -7.85e-17 1.00e+00
> lm(y ~ poly(x, 2)) # this fits order-2 orthogonal polynomials
Call:
lm(formula = y ~ poly(x, 2))
Coefficients:
(Intercept) poly(x, 2)1 poly(x, 2)2
1.667e+00 -3.127e-16 8.165e-01
I hope that this helps,
John
At 11:59 AM 30/09/2001 -0400, Christian Endter wrote:
>Dear Members of the Help List,
>
>Honestly, I feel a little bit stupid - I would like to do something rather
>simple: fit a non linear model to existing data, to be more precise I wanted
>to start with simple higher order polynomials.
>
>Unfortunately, I do not quite understand the examples in the helpfiles for
>the nlm, nls and nlsModel commands.
>
>Could anyone please provide a simple example to get me started (i.e. y = p +
>x^2 fitted to x= -1 0 1 y = 2 1 2; a simple parabola p should turn out to be
>1). How do I do this and how do I do the same for something like y = a + bx
>+ cx^2 + dx^3 ??
-----------------------------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario, Canada L8S 4M4
email: jfox at mcmaster.ca
phone: 905-525-9140x23604
web: www.socsci.mcmaster.ca/jfox
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