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From "Marios Michaelidis (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (MATH-857) Include a VIF and TOLERANCE check for a 2 dimensional double array, to determine variables that cause multi-colinearity issues and should be excluded from the models
Date Sun, 09 Sep 2012 11:35:07 GMT

     [ https://issues.apache.org/jira/browse/MATH-857?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Marios Michaelidis updated MATH-857:
------------------------------------

    Attachment: Multicolinearity.java

This is the latest Multicolinearity class file with the copyright note removed. 

Generally my background is more like in Risk and Statistics and therefore I find myself in
lack of the specific vocabulary (and generally the processes for software deploymnent) that
is being used here! I do apologise for that. I googled this "SVM diff" and it gives me various
links. Do you have a specific link for that?. As for the style and formatting, I though eclipse
was helping me for that... if not what should I do to improve it in an easy way?

Regards 
                
> Include a VIF and TOLERANCE check for a 2 dimensional double array, to determine variables
that cause multi-colinearity issues and should be excluded from the models
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: MATH-857
>                 URL: https://issues.apache.org/jira/browse/MATH-857
>             Project: Commons Math
>          Issue Type: New Feature
>    Affects Versions: 3.0
>         Environment: can apply to all operating systems
>            Reporter: Marios Michaelidis
>            Priority: Minor
>              Labels: build, test
>             Fix For: 3.1
>
>         Attachments: FOR TOLERANCE.rar, Multicolinearity.java
>
>   Original Estimate: 48h
>  Remaining Estimate: 48h
>
> Multicollinearity is a statistical phenomenon in which two or more predictor variables
in any multiple regression model are highly correlated. Tolerance and VIF are checks that
allows to avoid optimization failes due to "inability to converge". Most of the times, the
major packages (SAS, SPSS etc), have a check prior to running the model and they exclude variables
that might cause these kind of problems. It is quite a useful tool to be in common maths.

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