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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