Timeline for Does BoxCox transformation work for logistic regression?
Current License: CC BY-SA 4.0
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| Dec 2, 2021 at 19:38 | answer | added | kjetil b halvorsen♦ | timeline score: 2 | |
| Jun 28, 2021 at 11:29 | comment | added | whuber♦ | At stats.stackexchange.com/a/14501/919 I supplied a practical answer to this question. | |
| Jun 27, 2021 at 23:29 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
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| Jan 21, 2019 at 12:45 | history | edited | kjetil b halvorsen♦ |
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| Jan 21, 2019 at 12:43 | comment | added | kjetil b halvorsen♦ | 1) the confusion matrix might be informative, but is is based on accuracy which is not a proper score function. You should use a proper score function. 2) Model the continuous predictors with splines. | |
| Jan 21, 2019 at 3:02 | comment | added | Sebastian | Update: stats.stackexchange.com/questions/388305/… | |
| Jan 21, 2019 at 2:41 | comment | added | Glen_b | The logit of the outcome is not observed (or rather, it is, but they're all $\pm\infty$), and you can't rely on a fitted model's correctness while you're constructing a diagnostic check for its correctness. If you want to ask how to perform diagnostic checks on a logistic regression, again that's a whole new question. | |
| Jan 21, 2019 at 2:38 | comment | added | Sebastian | I misspoke. I meant to say - how do you suggest checking for linearity between the logit of the outcome and each predictor? My understanding is that is what gets assumed in logistic regression | |
| Jan 21, 2019 at 2:37 | comment | added | Glen_b | That would be a question of its own | |
| Jan 21, 2019 at 2:32 | comment | added | Sebastian | How do you suggest checking for linearity between predictors and a response? | |
| Jan 21, 2019 at 2:24 | comment | added | Glen_b | 1. Monotonic transformations cannot make non-monotonic relationships linear. 2. Your response is 0-1, so the logits should all be -infinity or plus infinity. If you're looking at logits of some fitted model, that's useless if the model is badly wrong. 3. Your plots seems to be flipped around; you're not trying to predict x's from the response but the other way around; how are these curves useful? | |
| Jan 21, 2019 at 2:10 | history | asked | Sebastian | CC BY-SA 4.0 |