[R] Extreme AIC or BIC values in glm(), logistic regression
tlumley at u.washington.edu
Thu Mar 19 08:57:49 CET 2009
On Thu, 19 Mar 2009, Maggie Wang wrote:
> Dear Thomas,
> Thank you very much for the answering!
> Yet why the situation happens only on some model, not all models? -
> that is, why for other model it can drop some variables but for this
> one it can't?
Presumably the other models don't have perfect separation. If you don't have enough data for reliable estimation you will get many models that predict poorly and a few that predict extremely well, just by chance.
> Best regards,
> On Wed, Mar 18, 2009 at 3:38 PM, Thomas Lumley <tlumley at u.washington.edu> wrote:
>> 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.
>> On Wed, 18 Mar 2009, Maggie Wang wrote:
>>> Dear R-users,
>>> I use glm() to do logistic regression and use stepAIC() to do stepwise
>>> The common AIC value comes out is about 100, a good fit is as low as
>>> 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,
>>> we expect some would be dropped. This situation is not uncommon.
>>> A summary output like this:
>>> 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 ***
>>> 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,
>>> [[alternative HTML version deleted]]
>> Thomas Lumley Assoc. Professor, Biostatistics
>> tlumley at u.washington.edu University of Washington, Seattle
Thomas Lumley Assoc. Professor, Biostatistics
tlumley at u.washington.edu University of Washington, Seattle
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