[R] Package to remove collinear variables

[John C Frain]

For background have a look at http://en.wikipedia.org/wiki/Multicollinearity.

I have also used

Regression Diagnostics: Identifying Influential Data and Sources of
Collinearity (Wiley Series in Probability and Statistics) by David A.
Belsley, Edwin Kuh and Roy E. Welsch

Sections 1.9 to 1.12 of
Hands-On Intermediate Econometrics Using R: Templates for Extending
Dozens of Practical Examples [With CDROM] by Hrishikesh D. Vinod
(2008)

Basically how you proceed depends a lot on what you are trying to achieve.

Best Regards John

On 5 August 2012 23:04, Roberto Moscetti <rmoscetti at unitus.it> wrote:
> Hi,
> thank you for your help. I know, I need to learn enough statistics to
> understand how to process my data. The reason because of I write on this
> forum is to ask to people a way to learn.
> I am a postharvest researcher and statistic is not my main field, so I try
> to do my best.
>
> Do you know a book (or literature) than can help me?
>
> Thank you very much for your time and suggestions.
>
> Best regards,
> Roberto
>
> Il 05/08/2012 12:55, Jeff Newmiller ha scritto:
>
>> There is no "magic bullet" (package) for your problem. You must either
>> learn enough statistics to understand how to analyze your data, or consult
>> with someone who does.
>>
>> FWIW collinearity is not in general amenable to automatic removal.
>> However, you can identify which inputs are collinear with each other, and
>> omit the redundant ones next iteration of your analysis, using (for example)
>> the approach suggested by Uwe.  Deciding WHICH of the redundant inputs is
>> most appropriate to keep is the part computers are not so good at... that is
>> where you must be smarter or more creative than the computer.
>>
>> Also, it would help you get responses if you included the context (earlier
>> discussion) in your replies.. most people do not use Nabble here. Reading
>> and following the requests in the footer of every message will also help.
>>
>> ---------------------------------------------------------------------------
>> Jeff Newmiller                        The     .....       .....  Go
>> Live...
>> DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live
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>> /Software/Embedded Controllers)               .OO#.       .OO#.
>> rocks...1k
>>
>> ---------------------------------------------------------------------------
>> Sent from my phone. Please excuse my brevity.
>>
>> Roberto <rmoscetti at unitus.it> wrote:
>>
>>> I do not know, because I tried to use rfe function (Backwards Feature
>>> Selection, Caret Package) to select wavelengths useful for a prediction
>>> model. Otherwise, rfe function give me back a lot of warning messages
>>> about
>>> collinearity between variables.
>>>
>>> So, I do not know if your script can be useful.
>>> I tried to use VIF-Regression to select variables, but rfe function
>>> advise
>>> me with the same warning messages again.
>>>
>>> What do you think about that?
>>>
>>> Thank you very much for your help.
>>>
>>> Best,
>>> Roberto
>>>
>>>
>>>
>>> --
>>> View this message in context:
>>>
>>> http://r.789695.n4.nabble.com/Package-to-remove-collinear-variables-tp4639200p4639226.html
>>> Sent from the R help mailing list archive at Nabble.com.
>>>
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>>> http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.



-- 
John C Frain
Economics Department
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj at tcd.ie
mailto:frainj at gmail.com