I am currently in the process of trying to run a multivariate meta-analysis model in metafor with random effects. I have no previous experience working with meta-analysis models and have read many posts/inquiries to try and get my bearings.
Here is the model structure I am currently hoping to proceed with:
acclimation_model<-rma.mv(yi, vi, data=dat_acclim_ES, mods = ~flux_range * mean_temp_reared + exp_age + size + org_level + exposure_temp, random = ~1 | study_id/ experiment_id/ response_id, method="REML")
My current questions are:
- Is the nested structure correct here (i.e. accounting for collinearity with multiple responses from within the same experiment from within the same study?) I have read different opinions on have a crossed effect for response_id as opposed to nesting within but am not sure.
- Is REML the proper estimation metric as opposed to ML when thinking about comparing AIC values? I have read that with REML AIC values are not comparable?
- Is it possible to calculate an R2 or I2 statistic to describe the model fit and success at describing heterogeneity? If so, how?
- Are there any other metrics I should be reporting to justify the use of this particular model? If so, which ones and are there resources you can point me to?