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. 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: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Leaf Sun > Sent: Sunday, November 06, 2005 3:11 AM > To: r-help@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 > > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html