[R-meta] Categorical mixed effect models and interpretation of results
Viechtbauer, Wolfgang (SP)
wolfg@ng@viechtb@uer @ending from m@@@trichtuniver@ity@nl
Tue Jun 19 11:31:52 CEST 2018
Yes, in principle this is right. If 'Treatment' only has two levels, then the QM-test and the test of the treatment coefficient are identical, so you can also just look at the latter.
However, I think your random effects structure is too simple given what you wrote. At the least, it should be something like:
random = ~ 1 | Study_id / Exp_id
where Exp_id is, as the name implies, the experiment id. See:
and esp. the "A Common Mistake in the Three-Level Model" section.
You might also want to consider adding random effects for species. For example:
random = list(~ 1 | Study_id / Exp_id, ~ 1 | Species)
would add species random effects (as a crossed random effect, not nested within study and/or experiment).
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Alexander Sullivan (BIO - Student)
Sent: Tuesday, 19 June, 2018 10:58
To: r-sig-meta-analysis using r-project.org
Subject: [R-meta] Categorical mixed effect models and interpretation of results
In my meta-analysis I have [say] 100 experiments, from 30 studies, across 12 species. These experiments can be equally divided into two treatment levels [low and high]. If I want to determine if there is a significant difference in effect sizes between these two treatment levels would this be the right code:
res <- rma.mv(yi, vi, method = "REML", data = mydata, mods = ~factor(Treatment), random = ~1|Study_id)
... and then from the output of this model if the Qm statistic is significant I could say there is a significant difference in effect sizes between the two treatments?
Thank you for your time,
MSc Ecology and Conservation student
University of East Anglia, UK
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