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I am largely inexperienced in the area of logistic regression. I was wondering if there were a good way to transform a continuous covariate in a logistic regression into a discrete one by subdividing the support of this continuous covariate in a smart way.

Any reference is welcome if the question is too general.

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    $\begingroup$ The good way is to not do this. Why do you want to? $\endgroup$
    – Peter Flom
    Commented Jun 10, 2013 at 16:11
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    $\begingroup$ @PeterFlom is right. Although written in a different context, I discuss categorizing continuous variables here: how-to-choose-between-anova-and-ancova-in-a-designed-experiment, especially after the update. It may help you to read it. $\endgroup$
    – gung - Reinstate Monica
    Commented Jun 10, 2013 at 16:15
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    $\begingroup$ @gung I think your answer is a little extreme (but I will grant that might be to make a point). Discretizing a continuous variable can be a good way to find and characterize nonlinear relationships, for instance (although perhaps a better way to go about this process would be with a CART or a random forest). $\endgroup$
    – whuber ♦
    Commented Jun 10, 2013 at 16:19
  • $\begingroup$ @all : Thank's for your comments, and to sum up I got that you don't recommand to do it, but now what if you had to do it, what would be the best way ? (or the least worst) Regards $\endgroup$
    – TheBridge
    Commented Jun 10, 2013 at 19:17
  • $\begingroup$ Many further references on the problems with this approach are here - in that case for regression, but the issues are relvant. As for 'best' - best for optimizing what? Why do you want to do it, and what are you trying to get out of it? $\endgroup$
    – Glen_b
    Commented Jun 11, 2013 at 1:46

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