[R] Extreme AIC or BIC values in glm(), logistic regression

Maggie Wang haitian at ust.hk
Thu Mar 19 07:25:27 CET 2009


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?

Thanks!!

Best regards,
Maggie



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.
>
>      -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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