The only solution I can see is fitting all possib le 2 factor models enabling interactions and then assessing if interaction term is significant...
any more ideas? Milicic B. Marko wrote: > > I have a huge data set with thousands of variable and one binary > variable. I know that most of the variables are correlated and are not > good predictors... but... > > It is very hard to start modeling with such a huge dataset. What would > be your suggestion. How to make a first cut... how to eliminate most > of the variables but not to ignore potential interactions... for > example, maybe variable A is not good predictor and variable B is not > good predictor either, but maybe A and B together are good > predictor... > > Any suggestion is welcomed > > ______________________________________________ > 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. > > -- View this message in context: http://www.nabble.com/Logistic-regression-problem-tp19704948p19746846.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.