---------- Forwarded message ----------
Date: Mon, 18 Jul 2011 10:17:00 -0700 (PDT)
From: KHOFF <kuph...@gmail.com>
To: r-help@r-project.org
Subject: [R] cforest - keep.forest = false option?
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?
that depends on your idea of "valid". A number of papers
published over the last years explore this question and
you should read the relevant literature first.
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)
no. party was designed as a flexible research tool and is
not optimized wrt speed or memory consumption.
Best,
Torsten
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)
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