[R] OLS variables

[Kjetil Brinchmann halvorsen]

John Fox wrote:
> Dear Leaf,
> 
> I assume that you're using lm() to fit the model, and that you don't really
> want *all* of the interactions among 20 predictors: You'd need quite a lot
> of data to fit a model with 2^20 terms in it, and might have trouble
> interpreting the results. 
> 
> If you know which interactions you're looking for, then why not specify them
> directly, as in lm(y ~  x1*x2 + x3*x4*x5 + etc.)? On the other hand, it you
> want to include all interactions, say, up to three-way, and you've put the
> variables in a data frame, then lm(y ~ .^3, data=DataFrame) will do it.  

This is nice with factors, but with continuous variables, and need of a
response-surface type, of model, will not do. For instance, with 
variables x, y, z in data frame dat
    lm( y ~ (x+z)^2, data=dat )
gives a model mwith the terms x, z and x*z, not the square terms.
There is a need for a semi-automatic way to get these, for instance,
use poly() or polym() as in:

lm(y ~ polym(x,z,degree=2), data=dat)

Kjetil

> There are many terms in this model, however, if not quite 2^20.
> 
> The introductory manual that comes with R has information on model formulas
> in Section 11.
> 
> I hope this helps,
>  John 
> 
> --------------------------------
> John Fox
> Department of Sociology
> McMaster University
> Hamilton, Ontario
> Canada L8S 4M4
> 905-525-9140x23604
> http://socserv.mcmaster.ca/jfox 
> -------------------------------- 
> 
>> -----Original Message-----
>> From: r-help-bounces at stat.math.ethz.ch 
>> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Leaf Sun
>> Sent: Sunday, November 06, 2005 3:11 AM
>> To: r-help at stat.math.ethz.ch
>> Subject: [R] OLS variables
>>
>> Dear all,
>>
>> Is there any simple way in R that can I put the all the 
>> interactions of the variables in the OLS model?
>>
>> e.g.
>>
>> I have a bunch of variables, x1,x2,.... x20... I expect then 
>> to have interaction (e.g. x1*x2, x3*x4*x5... ) with some 
>> combinations(2 way or higher dimensions). 
>>
>> Is there any way that I can write the model simpler?
>>
>> Thanks!
>>
>> Leaf
>>
>>
> 
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