Timeline for Assessing the effect of related variables using lmer in R
Current License: CC BY-SA 3.0
7 events
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| Jul 18, 2020 at 12:39 | history | edited | Robert Long |
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| Jul 12, 2020 at 10:54 | answer | added | Robert Long | timeline score: 1 | |
| Jul 28, 2014 at 0:13 | comment | added | Iceberg Slim | Right on, will take a look at lsmeans. Thanks. Incidentally, happy to mark this as answered but something got bulloxed up with my accounts and I don't seem to be recognized as the user who posted the question. | |
| Jul 28, 2014 at 0:02 | comment | added | Russ Lenth | I understand the concern, but the coefficients estimates in a regression model (including a mixed model) are the differential effects of each variable, subject to the others being held constant. So you do have the appropriate adjustment here. To interpret the results further, I (of course, since I wrote it) suggest the lsmeans package, which computes and compares predictions and equally-weighted averages thereof. | |
| Jul 27, 2014 at 22:45 | comment | added | Iceberg Slim | I (sort of) know the syntax, I'm just not clear on what the appropriate model is. For example, the one you proposed: will that adequately separate the effect of the two different factors (thresh and season)? My concern is that because they are related (water table is above the threshold much more in the fall) that what seems to be the effect of season will instead be the effect of threshold. | |
| Jul 27, 2014 at 21:16 | comment | added | Russ Lenth |
Is the question just on how to specify the model? The simplest one would be a random-intercepts model: lmer(rate ~ thresh * season + (1 | watershed), data = ...)
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| Jul 27, 2014 at 20:34 | history | asked | Iceberg Slim | CC BY-SA 3.0 |