Hi Guys,
Of course, a backward, forward, or other methods can be used directly. But
concerning BMA, the model interpretation is far simple:
"Bayesian Model Averaging accounts for the model uncertainty inherent in the
variable selection problem by averaging over the best models in the model
class according to approximate posterior model probability."
If you want to learn a few more before continue, that a look at the BMA
homepage:
http://www2.research.att.com/~volinsky/bma.html
But of course, you must do what you think is better for your problem.
By the way what is the dimension of your problem?
HTH,
Rick
--------------------------------------------------
From: "Frank E Harrell Jr" <f.harrell at vanderbilt.edu>
Sent: Thursday, November 05, 2009 4:12 PM
To: "Ricardo Gon?alves Silva" <ricardogs at terra.com.br>
Cc: "bbslover" <dluthm at yeah.net>; <r-help at r-project.org>
Subject: Re: [R] variable selectin---reduce the numbers of initial variable
Ricardo Gon?alves Silva wrote:
Yes, right. But I still prefer using BMA.
Best,
Rick
If you are entertaining only one model family, them BMA is a long,
tedious, complex way to obtain shrinkage and the resulting averaged
model is very difficult to interpret. ?Consider a more direct approach.
Frank
--------------------------------------------------
From: "bbslover" <dluthm at yeah.net>
Sent: Wednesday, November 04, 2009 11:28 PM
To: <r-help at r-project.org>
Subject: Re: [R] variable selectin---reduce the numbers of initial
variable
thank you . I can try bayesian. PCA method that I used to is can get
some
pcs, but I donot know how can i use the original variables in that
equation,
maybe I should select those have high weight ones,and delete that less
weight ones. right?
Ricardo Gon?alves Silva wrote:
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" <dluthm at yeah.net>
Sent: Wednesday, November 04, 2009 10:23 AM
To: <r-help at 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!
--
View this message in context:
http://old.nabble.com/variable-selectin---reduce-the-numbers-of-initial-variable-tp26195345p26195345.html
Sent from the R help mailing list archive at Nabble.com.