I am baffled by this as well. I'm having the same issue. Using suse linux,
with 64 bit R2.8.1.
Thanks,
james
Zege, Andrew wrote:
I am unable to install package lme4, after several attempts to do so using
various repository URLs.
Just to make sure everything works fine with proxy,
(2.8.1). I needed to install Matrix
package first, which was also outdated.
R CMD INSTALL -l lib Matrix_0.999375-22.tar.gz
R CMD INSTALL -l lib lme4_0.999375-28.tar.gz
it loads now within R. I haven used it much yet.
jamesmcc wrote:
I am baffled by this as well. I'm having the same issue
Greetings tree and forest coders-
I'm interested in comparing randomforests and regression tree/ bagging tree
models. I'd like to propose a basis for doing this, get feedback, and
document this here. I kept it in this thread since that makes sense.
In this case I think it's appropriate to
I wanted to report this issue here so others may not find themselves alone
and as the author is apparently active on the list.
I havent done an exhaustive test by any means, cause I dont have time. But
here's a small example. Apparently the ns argument is the one that is
killing it. I've gotten
I have this *exact* same confusion.
Adding to this is the fact that Everitt and Hothorn in their book, HSAUR,
say that setting xval=100 gives 100 runs of 10-fold cross-validation (1st
ed., page 136).
Is this actually 1 run of 100-fold cross-validation?
For large xval, doing multiple
I think it's important to say why you're unhappy with your current measures?
Are they not capturing aspects of the data you understand?
I typically use several residual measures in conjunction, each has it's
benefits/drawbacks. I just throw them all in a table.
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