Re:logistic regression 2005-02-08 - By 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@(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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