[R] Extreme AIC or BIC values in glm(), logistic regression
haitian at ust.hk
Thu Mar 19 07:25:27 CET 2009
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?
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
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