RE: st: Correcting Multicollinearity

[Cameron McIntosh <cnm100@hotmail.com>]

I might also suggest:
Friendly, M., & Kwan, E. (2009). Where's Waldo? Visualizing Collinearity Diagnostics. American Statistician, 63(1), 56-65.http://www.datavis.ca/papers/viscollin-web.pdf

Kwan, E., & Friendly, M. (January 2, 2012). Represents tables as semi-graphic displays: Package ‘tableplot’, Version 0.3-4.http://cran.r-project.org/web/packages/tableplot/index.html

Friendly, M. (January 2, 2012). Generalized ridge trace plots for ridge regression: Package ‘genridge’, Version 0.6-2.http://cran.r-project.org/web/packages/genridge/index.html
Hendrickx, J. (August 16, 2004). COLDIAG2: Stata module to evaluate collinearity in linear regression.http://ideas.repec.org/c/boc/bocode/s445202.html

Hendrickx, J. (January 2, 2012). Tools for evaluating collinearity: Package ‘perturb’, Version 2.04.http://cran.r-project.org/web/packages/perturb/index.html

Cam
> Subject: Re: st: Correcting Multicollinearity
> From: sjsamuels@gmail.com
> Date: Thu, 29 Mar 2012 22:08:01 -0500
> To: statalist@hsphsun2.harvard.edu
> 
> 
> Ridge regression, a family of methods for remediating multicollinearity, is implemented by -ridgereg- (SSC). A serious user should, at a minimum, study the following reference, given in the -help-.
> 
>  Evagelia, Mitsaki (2011) "Ridge Regression Analysis of Collinear Data", 
>   http://www.stat-athens.aueb.gr/~jpan/diatrives/Mitsaki/chapter2.pdf
> 
> Steve
> sjsamuels@gmail.com
> 
> 
> 
> On Mar 29, 2012, at 8:18 AM, Nick Cox wrote:
> 
> Another answer is that multicollinearity has computational,
> statistical and scientific sides.
> 
> It's (mostly) Stata's job to do the best with the computation it can
> given (tendencies to) multicollinearity among the predictors.
> 
> It is your job to think about choice of predictors.
> 
> But you can think about the statistical side by looking at the
> structure of interdependence among predictors statistically. One part
> of the statistical world is seemingly obsessed by the idea that this
> must mean some yes-or-no test yielding a P-value or some omnibus,
> factotum or portmanteau statistic quantifying how bad the problem is;
> their punishment is to miss what may be learned from looking at
> correlations and e.g. -graph matrix-.
> 
> Scientifically, what you know about the problem should guide revised
> idea about whether the predictor choice is overkill.
> 
> Nick
> 
> On Thu, Mar 29, 2012 at 12:33 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> > On Thu, Mar 29, 2012 at 1:22 PM, D. Demetriou wrote:
> >>> Is there any Stata tool to (partly) account for this
> >>> defect(multicollinearity) after this has been indicated by, for example, vif
> >>> or collin commands?
> > 
> > No, you can only do something about that before collecting your data
> > by choosing a specific design. After you have collected the data, the
> > correlations in the data are just what they are and you will just have
> > to learn to live with them.
> > 
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