[R-meta] Random-effect specification in rma.mv() for multiple sources?
Dear Tim I will leave it to the experts to check your structure but one thing which immediately strikes me is that you are going to need a very large dataset to be able to estimate all those random effects with any precision especially the ones with limited replicates. If you do get the model to converge it would be mandatory to look at diagnostics like the profile likelihoods. Michael
On 29/06/2021 03:39, Timothy MacKenzie wrote:
Dear all, I noticed some errors in the copy-pasted data structure in my previous post (https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2021-June/002953.html). Below is my correct data structure. From left to right, one can see the hierachical structure in my dataset: study > sample > outcome > time > control > obs Q: Would the following random-effect structure account for all the above sources (as a first step to then drop the ones that are insignificant)? random = list(~ sample | study, ~ time | interaction(study,sample,outcome), ~ 1 | control, ~ 1 | obs) Thanks, Tim study sample outcome time control obs 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2 1 3 1 1 2 2 1 4 1 2 1 1 1 5 1 2 2 1 1 6 1 2 1 2 1 7 1 2 2 2 1 8 2 1 1 2 1 9 2 1 2 2 1 10 2 1 1 2 2 11 2 1 2 2 2 12 3 1 1 3 1 13 3 1 1 3 2 14 3 2 1 3 1 15 3 2 1 3 2 16 [[alternative HTML version deleted]]
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