Hi i need to create a model from 250 + variables with high collinearity, and only 17 data points (p = 250, n = 750). I would prefer to use Cp, AIC, and/or BIC to narrow down the number of variables, and then use VIF to choose a model without collinearity (if possible). I realize that having a huge p and small n is going to give me extreme linear dependency problems, but I *think* these model selection criteria should still be useful?
I have currently been running regsubsets for over a week with no results. I have no idea if R is still working, or if the computer is hung. I ran regsubsets on a smaller portion of the data, also with linear dependency problems, and got results. However, the hourglass continues its endless spiraling with the full dataset. I am running the following on Windows 7 library(leaps) m_250<-regsubsets(Y~., data=model2, nbest=1, really.big=TRUE) (NOTE: The ~ is a tilda, not a dash, in the regression statement above: Y~.) Does anyone have any opinions on: 1) is R likely to still be running, even after a week, or should i just shut it down? 2) am i doing something wrong with regsubsets? 3) is there a better option than regsubsets, that will still allow me to narrow down parameters so i have explanatory power (ie i could develop a model using PLS, and keep all the variables, but also keep all the collinearity issues, and have good prediction but not explanatory power) 4) any other ideas? I am pretty new to R, so any newbie detail would be much appreciated! thanks in advance for any help! -- View this message in context: http://r.789695.n4.nabble.com/regsubsets-Leaps-tp4632083.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.
