[R] Re:logistic regression

[Joe Nocera]

Helene - 

In addition to some of the excellent suggestions already posited (e.g. examining AIC,
pseudo R^2 in the Design package), you might want to consider another tool to assess
logistic regression model accuracy: the area-under-curve (AUC) from a
receiver-operating characteristic (ROC).

The ROC curve describes the relationship between the number of true positives observed
(sensitivity) to false positives, and also for negatives.  The AUC is the probability
that a model can correctly distinguish between the two.  This is an appealling
alternative to some of the known issues of citing only a pseudo-R^2 (like Nagelkerke's
for instance) to describe 'fit'.

Check out the ROC functions available at the Bioconductor website.  There was also some
code sent around on the list a few months back for calculating trapeziodal AUC, se's
from ROC, and comparing two ROC curves...search the archives if interested, or I can
probably dig them out for you offline...

Cheers,
Joe

Quoting Frank E Harrell Jr <f.harrell at vanderbilt.edu>:

> Vito Ricci wrote:
> > Hi,
> > 
> > I don't know if a pseudo squared R for glm exists in
> > any R package, but I find some interesting functions
> > in S mailing list:
> 
> It is included in lrm in the Design package.  But note that this is not 
> for checking fit but rather for quantifying predictive discrimination.
> 
> .....
> 
> -- 
> Frank E Harrell Jr   Professor and Chair           School of Medicine
>                       Department of Biostatistics   Vanderbilt University
> 
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Joseph J. Nocera
Ph.D. Candidate
NB Coop. Fish & Wildlife Research Unit
Biology Department - Univ. New Brunswick
Fredericton, NB
Canada   E3B 6E1
tel: (902) 679-5733

"Why does it have to be spiders?  Why can't it be 'follow the butterflies'"?!
    - Ron Weasley, Harry Potter & The Chamber of Secrets