Joel Fürstenberg-Hägg said: "Multiple linear regression [...] I would like to check every possible combination of factors, evalute the results based for instance on their p values, and then choose the best regression model."
By "every possible combination of factors", I assume you mean that for k factors, you want to consider all 2^k models where each factor is either present in the model or absent from it as a main effect, (or do you mean to include interactions as well?), selected by p-value? Speaking from a statistical practice point of view, this is probably not a good idea. If you really want to do a "best subset" type regression, there's the package bestglm at CRAN. http://cran.r-project.org/web/packages/bestglm/index.html - it doesn't do it by p-values and if your factors have more than two levels it does it via complete enumeration rather than using something efficient like the leaps and bounds algorithm, but it's close to the sort of thing you want. Doubtless there are other packages that also implement this sort of exhaustive model selection. -- View this message in context: http://n4.nabble.com/Multiple-regression-script-tp954567p954940.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
