Hi @SteveDenham,
I did try using weight and am still getting the warning message. While I can confirm there is no collinearity, it is possible there are some sort of quasi-separation issue in the Class variables like you suggested. Questions are,
Why removing the offset or weight option, the warning message goes away? I read somewhere if it is quasi-separation, the model is still ok because only maximum likelihood cannot be computed and we can ignore the warning. I think I am fine with this as long as the model has good AUC, low misclassification, and the differences between these metrics of the train and test sets are not too big e.g. < 5% diff.
I suppose with the use of offset or weight option in Proc Logistic, and to get the adjusted prediction for each observation, I will still have to use priorevent=&pi1 in the score statement? Example below (&pi1 is the prior event value),
ods graphics on;
proc logistic
Data = work.train_stdize
outmodel=work.mymodel
outest=work.mdl_betas
namelen=32;
class &class_var. / param=ref;
model responder(event='1') = &class_var. &num_var. / stb lackfit ctable pprob=0.5 offset=off;
score data=work.train_stdize fitstat out=work.trainpred outroc=work.troc priorevent=&pi1;
score data=work.validate_stdize fitstat out=work.validpred outroc=work.vroc priorevent=&pi1;
score data=work.osd_stdize fitstat out=work.osdpred outroc=work.oroc priorevent=&pi1;
run;
ods graphics off;
Thanks,
Lobbie
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