[R] variable selectin---reduce the numbers of initial variable

Ricardo Gonçalves Silva ricardogs at terra.com.br
Thu Nov 5 19:55:20 CET 2009


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 Gonçalves 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 Gonçalves 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 Gonçalves 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.
>>>>>
>>>>> ______________________________________________
>>>>> R-help at 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.
>>>>>
>>>>
>>>>
>>>>
>>>>>
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>>>>
>>>> ______________________________________________
>>>> R-help at 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.
>>>>
>>>>
>>>
>>> -- 
>>> View this message in context:
>>> http://old.nabble.com/variable-selectin---reduce-the-numbers-of-initial-variable-tp26195345p26207750.html
>>>
>>> Sent from the R help mailing list archive at Nabble.com.
>>>
>>> __________________
> -- 
> Frank E Harrell Jr   Professor and Chair           School of Medicine
>                      Department of Biostatistics   Vanderbilt University
>



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