Hi,
Nowdays there's a lot o new variable selection methods, specially using the
Bayes Paradigm.
For your problem, I think you could try the Bayesian Model Average BMA
package.
Or, you can reduce your data dimension by PCA, which also permits you see
the weight of
each variable in the PC.
HTH
Rick
--------------------------------------------------
From: "bbslover" <dlu...@yeah.net>
Sent: Wednesday, November 04, 2009 10:23 AM
To: <r-help@r-project.org>
Subject: [R] variable selectin---reduce the numbers of initial variable
hello,
my problem is like this: now after processing the varibles, the remaining
160 varibles(independent) and a dependent y. when I used PLS method, with
10
components, the good r2 can be obtained. but I donot know how can I
express
my equation with the less varibles and the y. It is better to use less
indepent varibles. that is how can I select my indepent varibles. Maybe
GA is good method, but now I donot gasp it. and can you give me more good
varibles selection's methods. and In R, which method can be used to
select
the potent varibles . and using the selected varibles to model a equation
with higher r2, q2,and less RMSP.
thank you!
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