On Oct 25, 2011, at 11:16 AM, Ben Bolker wrote: > 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
There is a difference with glmnet however vis-à-vis its handling of factors. There is a recent discussion here: https://stat.ethz.ch/pipermail/r-help/2011-August/285905.html which covers the topic. Be sure to read the replies, including Martin's. HTH, Marc Schwartz ______________________________________________ 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.
