"Logistic Regression: Missing cut-off and intercept settings?"

jonny1648jonny1648 Member Posts: 1 Contributor I
edited June 2019 in Help
Hi all

I am posting this without an example because it holds for any application of the myKLR-based Logistic Regression operator, in my opinion.

I am aware of the fact that kernel logistic regression does not follow the usual maximum likelihood training approach of PASW, SAS, R, etc. Nevertheless, there appears to be a convention of what reviewers in many academic journals expect to see when Logistic Regression models are reported, and I am wondering how to obtain these results in RM. I am specifically not referring to any goodness-of-fit / pseudo R-squared values as I know these are not implemented on purpose. Specifically, I am interested in

- the cut-off value, which sets a threshold on confidence values to obtain class labels. I believe these are used to obtain the ROC curve in the performance operator, but I do not see how to set / read them.
- the intercept, or constant in the logistic regression equation. In my opinion, the intercept is a crucial parameter which largely determines the sample selection bias of a logistic regression classifier. For some applications it has to be fixed to zero.

Furthermore, many reviewers demand a forward / backward variable selection based on significance (p-values) and NOT based on overall model performance, and it would be helpful to know whether these (or a substitute) is obtainable in RM.

Thanks for any ideas / suggestions!
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