> 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 <
[hidden email]> 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:<
[hidden email]> Basics: ##.#. ##.#. Live
>>> Go...
>>> Live: OO#.. Dead: OO#.. Playing
>>> Research Engineer (Solar/Batteries O.O#. #.O#. with
>>> /Software/Embedded Controllers) .OO#. .OO#.
>>> rocks...1k
>>>
>>> ---------------------------------------------------------------------------
>>> Sent from my phone. Please excuse my brevity.
>>>
>>> Roberto <
[hidden email]> 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.
>>>>
>>>> ______________________________________________
>>>>
[hidden email] 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.
>>>
>> ______________________________________________
>>
[hidden email] 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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