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]]
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