Hi, I'm very new to R. I am most interested in the variable importance measures that result from randomForest, but many of my predictors are highly correlated. My first question is:
1. do highly correlated variables render variable importance measures in randomForest invalid? and 2. I know that cforest is robust to highly correlated variables, however, I do not have enough space on my machine to run cforest. I used the keep.forest = false option when using randomForest and that solved my space issue. Is there a similar option for cforest (besides savesplitstats = FALSE, which isn't helping) below is my code and error message Thanks in advance! > fit <- cforest(formula = y ~ x1 + x2+ x3+ x4+ x5+ + x6+ x7+ x8+ x9+ x10, data=data, control= cforest_unbiased(savesplitstats = FALSE, ntree = 50, mtry = 5) 1: In mf$data <- data : Reached total allocation of 3955Mb: see help(memory.size) 2: In mf$data <- data : Reached total allocation of 3955Mb: see help(memory.size) -- View this message in context: http://r.789695.n4.nabble.com/cforest-keep-forest-false-option-tp3675921p3675921.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.
