[R] Extreme AIC or BIC values in glm(), logistic regression
Thomas Lumley
tlumley at u.washington.edu
Wed Mar 18 08:38:09 CET 2009
With 30 variables and only 55 residual degrees of freedom you probably have perfect separation due to not having enough data. Look at the coefficients -- they are infinite, implying perfect overfitting.
-thomas
On Wed, 18 Mar 2009, Maggie Wang wrote:
> Dear R-users,
>
> 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 ***
> g4373 -9.272e+14 6.534e+07 -14190077 <2e-16 ***
> g4583 -2.279e+15 1.223e+08 -18640563 <2e-16 ***
> g761:g2809 -5.101e+14 4.693e+08 -1086931 <2e-16 ***
> g761:g3106 -3.399e+16 6.923e+08 -49093218 <2e-16 ***
> g2809:g3106 3.016e+15 6.860e+08 4397188 <2e-16 ***
> g761:g4373 3.180e+15 4.595e+08 6920270 <2e-16 ***
> g2809:g4373 -5.184e+15 4.436e+08 -11685382 <2e-16 ***
> g3106:g4373 1.589e+16 2.572e+08 61788148 <2e-16 ***
> g761:g4583 -1.419e+16 8.199e+08 -17303033 <2e-16 ***
> g2809:g4583 -2.540e+16 8.151e+08 -31156781 <2e-16 ***
> ........
> (omit)
> ........
>
> f. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
> Null deviance: 120.32 on 86 degrees of freedom
> Residual deviance: 1009.22 on 55 degrees of freedom
> AIC: 1073.2
>
> Number of Fisher Scoring iterations: 25
>
> Could anyone suggest what does this mean? How can I perform a reliable
> logistic regression?
>
> Thank you so much for the help!
>
> Best Regards,
> Maggie
>
> [[alternative HTML version deleted]]
>
>
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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