[R] is AIC always 100% in evaluating a model?

[Frank E Harrell Jr]

Tal Galili wrote:
> Hello Frank,
> 
> Thank you for the extension and remarks. 
> The basic weakness of stepwise regression VS going through all-subsets 
> is very much agreed upon.  Although from what I gather there is one case 
> where all subsets will be a problem to implement, that is for very LARGE 
> datasets - especially in the sense of a lot of explanatory variables, 
> and also with regards to cases where we have more explanatory variables 
> then data points.
> In such cases I wonder if using stepwise regression could be found to be 
> more realistic to implement then all subsets checks.
> Then again, I imagine (although not from real experience) that shrinkage 
> methods (used with LARS) could be practical in those cases too.
> 
> 
> 
> I am looking forward to meeting you on Tuesday and taking your first 
> tutorial of the day,

I look forward to seeing you in Rennes.

> 
> With regard,
> Tal

All subsets regression is an especially bad form of stepwise regression. 
  It has terrible operating characteristics.

Cheers,
Frank

> 
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> 
> On Sat, Jul 4, 2009 at 4:22 PM, Frank E Harrell Jr 
> <f.harrell at vanderbilt.edu <mailto:f.harrell at vanderbilt.edu>> wrote:
> 
>     sed for one variable at a time variable selection. AIC is just a
>     restatement of the P-value, and as such, doesn't solve the severe
>     problems with stepwise v
> 
> 
> 
> 
> -- 
> ----------------------------------------------
> 
> 
> My contact information:
> Tal Galili
> Phone number: 972-50-3373767
> FaceBook: Tal Galili
> My Blogs:
> http://www.r-statistics.com/
> http://www.talgalili.com
> http://www.biostatistics.co.il
> 
> 


-- 
Frank E Harrell Jr   Professor and Chair           School of Medicine
                      Department of Biostatistics   Vanderbilt University