Joe Nocera wrote:
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'.

That's also standard output in Design's lrm function (C index). But unlike R^2 comparing two models on the basis of ROC area is not very sensitive. -Frank



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 <[EMAIL PROTECTED]>:


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

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

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