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

Max Kuhn mxkuhn at gmail.com
Thu Nov 5 20:47:37 CET 2009

```There is also a sparse PLS model in the spls package. It uses
lasso-like regularization to reduce the number of variables. I've had
a lot of success with it.

Max

2009/11/5 Ricardo Gonçalves Silva <ricardogs at terra.com.br>:
> 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
>>>>> 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
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>>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>>>
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>>>>>
>>>>> ______________________________________________
>>>>> R-help at r-project.org mailing list
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>>>>> and provide commented, minimal, self-contained, reproducible code.
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>>>>
>>>> --
>>>> 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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