Timeline for Are $R^2$ for GLMM useful for modelers but not necessarily for readers?
Current License: CC BY-SA 3.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 8, 2021 at 4:55 | answer | added | Ghoti657 | timeline score: 1 | |
| Sep 11, 2019 at 5:01 | 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. | |
| Aug 8, 2019 at 7:32 | answer | added | mkt | timeline score: 5 | |
| Dec 8, 2017 at 12:02 | comment | added | mkt | I second the above comment. Withholding potentially useful information because readers may not be statistically savvy is a bad idea. Additionally, your discussion of the meaning of these metrics does not seem like a weakness at all. It also does not really violate a reasonable interpretation of 'goodness of fit'. So I think your option #1 is by far the best choice; your explanation here of the metrics is a good one, so I think a version of this would fit well within the paper. | |
| May 4, 2017 at 4:02 | history | tweeted | twitter.com/StackStats/status/859981448224403456 | ||
| May 3, 2017 at 17:17 | comment | added | Kodiologist | I don't know much about this particular issue, but in general, I think that anything useful to the analyst as a modeling diagnostic will be useful to the reader, too, to help the reader decide if you made good modeling decisions. Model evaluation is an important part of data analysis and people ought to be much more willing to discuss it in their publications. | |
| May 3, 2017 at 16:15 | history | asked | N Brouwer | CC BY-SA 3.0 |