Bert Gunter <gunter.berton <at> gene.com> writes: > > If I understand you correctly, it sounds like you need to do some reading. > > ?lm and ?formula tell you how to specify linear models for glm or glmnet. > However, if you do not have sufficient statistical background, It probably > will be incomprehensible, in which case you should consult your local > statistician. > > For glmnet, go to the linked references given in the Help file.There is no > such thing as AIC for these models, as they are penalized fits (with users > choosing the penalization tradeoff). Again, consult your local statistician
Let me second Bert's concern, but in the meantime, if what you want are *all two-way interactions among variables, you can follow this example: > d <- data.frame(y=runif(100),x1=runif(100),x2=runif(100),x3=runif(100)) > gg <- lm(y~(.)^2,data=d) > names(coef(gg)) [1] "(Intercept)" "x1" "x2" "x3" "x1:x2" [6] "x1:x3" "x2:x3" I have done the example with continuous variables and with lm() here, but it should generalize easily to (1) a mixture of categorical and continuous variables and (2) other R modeling functions ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
