Hi, Josh, What we are doing is, we have a microarray data set with 2000 genes and roughly 60 samples split 2:1 cancer:normal. So we essentially have one binary response and 2000 continuous predictors. We want to use this to develop an ensemble-based classifier method in which the members of the ensemble are all gene pairs. To this end, we want to use the Leaps and Bounds algorithm to obtain the K=200, 500, or 1000 best-performing subsets of Size=2 Genes to feed into our ensemble. We had partial success doing this in SAS, as follows:
1. the SAS Logistic Procedure (the natural choice for our binary outcome, because it does logistic regression) would include only the first 60 genes into the Leaps and Bounds search, and print for each of the remaining genes a message saying it was a linear combination of the first 60 genes & was therefore being excluded. 2. However, the SAS Reg Procedure (not the natural choice for our binary outcome, because it does linear regression) would include all 2000 genes into the Leaps and Bounds search, and not be bothered by the linear dependencies. And it gave results that held up quite well in subsequent analyses. So, first we want to replicate in R what we did in SAS with the linear regression, i.e., use the Leaps and Bounds algorithm to obtain the K=200, 500, or 1000 best-performing linear-regression models of Size=2 Genes from our list of 2000 genes, and not have it exclude genes for being a linear combination of the basis set. Then we want to use R to try and do what SAS could not: get logistic regression to do the same thing and not have it exclude genes for being a linear combination of the basis set. Thanks. -- View this message in context: http://r.789695.n4.nabble.com/Does-R-have-function-package-works-similar-to-SAS-s-PROC-REG-tp2965657p2967295.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.