(x1 + x2 + x3) ^2 will give you the main effects and the interactions.
So this will shorten your equation to
ft <- lm(y ~ (x1 + x2 + x3) ^2 + I(x1 * x1)+ I(x2 * x2) + I(x3 * x3),
mydata)
HTH,
Thierry
------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
Thierry.Onkelinx op inbo.be
www.inbo.be
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data.
~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey
-----Oorspronkelijk bericht-----
Van: r-help-bounces op r-project.org [mailto:r-help-bounces op r-project.org]
Namens Jinsong Zhao
Verzonden: zondag 27 juli 2008 9:39
Aan: r-help op r-project.org
Onderwerp: [R] A easy way to write formula
Hi
I have a data frame, including x1, x2, x3, and y. I use lm() to fit
second-order linear model, like the following:
ft <- lm(y ~ x1 + x2 + x3 + I(x1 * x1) + I(x1 * x2) + I(x1 * x3) + I(x2
* x2) + I(x2 * x3) + I(x3 * x3), mydata)
if the independent variable number is large, the formula will be very
long. Is there a easy way to write formula like the above one? I have
read the R introduction, however, I don't find a easy way.
Any hints will be appreciated. Thanks,
Jinsong
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