Timeline for Select polynomial order for continuous variables in mixed model step-wise backward selection
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Sep 18 at 18:02 | 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. | |
| May 20 at 15:03 | 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. | |
| Dec 19, 2022 at 15:53 | answer | added | EdM | timeline score: 1 | |
| Dec 19, 2022 at 14:13 | comment | added | cccnrc | Hi @Dave and EdM and thanks for your reply! I was just wondering on what basis do you select the polynomial order to use for each continuous variable. Do you base this on the number of continuous variables you have in the model? On some specific characteristic of each variable (and in case which one), etc | |
| Dec 19, 2022 at 13:51 | comment | added | EdM | Are you proposing to use a single polynomial fit instead of a flexible cubic regression spline? I'm not familiar with that package's syntax, but if that's what you're doing you are likely to get led astray with a single polynomial fit. You can set up a regression spline and, for example, use elimination to choose the number of knots/flexibility of the continuous-variable models. | |
| Dec 19, 2022 at 12:20 | comment | added | Dave | Isn’t the whole point of stepwise selection that you throw a bunch of variables at the problem and make the stepwise procedure pick some? While our Alexis has posted that stepwise regression in general is pants, if that’s what you’re going to do, what’s the problem with letting it do it’s thing and select the variables? | |
| Dec 19, 2022 at 12:09 | history | asked | cccnrc | CC BY-SA 4.0 |