Timeline for Marginal interpretation of fixed effects in GLMM
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 2, 2019 at 7:55 | comment | added | Dimitris Rizopoulos | Yes, you could try this. | |
| Jan 2, 2019 at 7:38 | comment | added | Cuenco | Thanks. So I assume you recommend that item will be treated as a fixed effect? Then using GLMMadaptive? | |
| Jan 2, 2019 at 7:23 | comment | added | Dimitris Rizopoulos | Check here: stats.stackexchange.com/questions/7004/… . Of course, in mixed models you can gain information from the repeated measurements but still you need sufficient number of levels. | |
| Jan 2, 2019 at 6:31 | comment | added | Cuenco | Hmm... Do you have a reference for that? I learnt that 5-6 items are already enough. | |
| Jan 1, 2019 at 17:51 | comment | added | Dimitris Rizopoulos | Having 6 or 12 items sounds too few to consider them as a random effect, even if they come from a pull of items. Often you need more than15-20 levels in your grouping variable to get a stable estimate of the variance across the different levels. | |
| Jan 1, 2019 at 16:57 | comment | added | Cuenco | I have 6 or 12 items (two experiments). It makes a lot of sense to include them as random effects because they are really of little specific interest in by themselves, and are supposed to represent something much more general. | |
| Jan 1, 2019 at 15:16 | comment | added | Dimitris Rizopoulos | How many items do you have, and are you certain that you want to include them as a random effect? | |
| Jan 1, 2019 at 14:44 | history | asked | Cuenco | CC BY-SA 4.0 |