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I am log-transforming my predictor variables to meet the linearity on the logit assumption for logistic regression. In so doing, I am adding a constant to ensure the minimum value is 1 .

My question is, if I then standardize my columns (e.g., min-max scaling), I will again end up with zeros that I will have to add a constant to, to ensure I can do more logistic regresison diagnostics like testing linearity on the logit, etc.

Is there any other way around having to add a constant twice, as described above?

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Yes. Rather than torture your data until it fits a model, use a model that does not make assumptions which your data violates.

One idea is to use a spline of the IVs. Another is a classification tree (or one of the offshoots of these). Yet another is a neural net.

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  • $\begingroup$ Thanks Peter - my limitation, however, is that my study is based on applying a logistic regression model. So unfortunately, data torture is the only way I see to get to the end goal. When violating model assumptions, I gather the main impact is on statistical inference capabilities, but machine learning prediction per se is not necessarily affected? $\endgroup$ Oct 9, 2023 at 0:52
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    $\begingroup$ @user19226726 You can spline your features and use a logistic regression. // If data torture is the only way to reach your ultimate goal, it is reasonable to wonder if you’re doing something that should be done. Always remember the “could/should” line in Jurassic Park. $\endgroup$
    – Dave
    Oct 9, 2023 at 1:06
  • $\begingroup$ That strikes me as a really silly notion -- to base a study on a particular method. But splines would be within logistic regression. $\endgroup$
    – Peter Flom
    Oct 9, 2023 at 11:00

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