Timeline for Transforming a continuous covariate into a discrete one in logistic regression [duplicate]
Current License: CC BY-SA 3.0
12 events
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| Jul 14, 2022 at 13:49 | history | duplicates list edited | kjetil b halvorsen♦ | duplicates list edited from Binning raw data prior to building a logistic regression model to Binning raw data prior to building a logistic regression model, Should we bin continuous variables? | |
| Jul 14, 2022 at 13:47 | history | closed | kjetil b halvorsen♦ regression Users with the regression badge or a synonym can single-handedly close regression questions as duplicates and reopen them as needed. | Duplicate of Binning raw data prior to building a logistic regression model | |
| Jun 11, 2013 at 11:11 | comment | added | TheBridge | Ok let's try this minimizing the log-likelihood difference of the "continuous covariate model" and the "discretized one" (such a criteria is often used in variable selection I think) Wouldn't this mininmize the damages (in terms of likelihood) caused by discretizing the continuous covariate ? Regards | |
| Jun 11, 2013 at 7:59 | comment | added | Glen_b | I can't define your criteria for you. "Least damage" to what end? | |
| Jun 11, 2013 at 7:13 | comment | added | TheBridge | @Glen_b : Thank's again for all your references, to the question "why do I need to do it" I'll have to answer "because I have to". I know that sounds stupid but this really the very reason for me to do that. The fact that it is not a recommended thing to do is another matter. Anyway I understand your point that by doing this I will inevitably lose the useful information in the model, so capatilising on this remark; my aim now is doing so with the least damage. At last defining the meaning the "best" or "least worst" way is part of my question if not the most important part. Regards. | |
| Jun 11, 2013 at 1:50 | history | edited | Glen_b | CC BY-SA 3.0 |
removed unneeded phrase with spelling error and apparently misplaced word, improved grammar, clarity
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| Jun 11, 2013 at 1:46 | comment | added | Glen_b | 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? | |
| Jun 10, 2013 at 19:17 | comment | added | TheBridge | @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 | |
| Jun 10, 2013 at 16:19 | comment | added | whuber♦ | @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). | |
| Jun 10, 2013 at 16:15 | comment | added | gung - Reinstate Monica | @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. | |
| Jun 10, 2013 at 16:11 | comment | added | Peter Flom | The good way is to not do this. Why do you want to? | |
| Jun 10, 2013 at 16:07 | history | asked | TheBridge | CC BY-SA 3.0 |