Timeline for Correcting nonlinear relationship between continuous predictor and logit of dependent variable (Binary logistic regression)
Current License: CC BY-SA 3.0
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| when toggle format | what | by | license | comment | |
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| Jul 7, 2019 at 17:00 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Apr 6, 2018 at 20:17 | answer | added | AdamO | timeline score: 2 | |
| Apr 6, 2018 at 19:42 | history | edited | kjetil b halvorsen♦ | CC BY-SA 3.0 |
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| Apr 5, 2018 at 15:09 | comment | added | StasK | Why do you not want to use whatever transformation / nonlinear term(s) you would need to use to make the model fit? Usually, the interpretation of nonlinear terms is not that difficult: when the value of $x$ is -2, the effect of a unit increase (on the logit of the probability) is 0.5, while when the $x$ is +2, the effect is 1.5. The effect is localized, that's all. With nonlinear models, you will likely be better off reporting predictive margins, anyway. | |
| Apr 5, 2018 at 13:58 | history | migrated | from stackoverflow.com (revisions) | ||
| Apr 5, 2018 at 7:35 | history | asked | Lukas Preis | CC BY-SA 3.0 |