[R] Extreme AIC or BIC values in glm(), logistic regression
Dieter Menne
dieter.menne at menne-biomed.de
Wed Mar 18 08:30:31 CET 2009
Maggie Wang <haitian <at> ust.hk> writes:
> I use glm() to do logistic regression and use stepAIC() to do stepwise model
> selection.
>
> The common AIC value comes out is about 100, a good fit is as low as around
> 70. But for some model, the AIC went to extreme values like 1000. When I
> check the P-values, All the independent variables (about 30 of them)
> included in the equation are very significant, which is impossible, because
> we expect some would be dropped. This situation is not uncommon.
>
> A summary output like this:
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 4.883e+14 1.671e+07 29217415 <2e-16 ***
> g761 -5.383e+14 9.897e+07 -5438529 <2e-16 ***
> g2809 -1.945e+15 1.082e+08 -17977871 <2e-16 ***
> g3106 -2.803e+15 9.351e+07 -29976674 <2e-16 ***
I suspect that you have specified your target variables incorrectly.
Note that there three method to define the variables which is better explained
in MASS, chapter Binomial data in the budworm context.
Try to extract a few of your data and post these here in a self running
example.
Dieter
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