Hello,
I am currently using a mixed effect meta-regression to explore the effects of different environmental variables on fish densities in coastal habitats.
For this, I am constructing a different model for individual fish species, in which my goal is to identify important predictor variables separately for each species by using aicc-based model selection (glmulti).
For each fish dataset, I usually identify 3-4 potentially important predictor variables ? which include both categorical and continuous variables ? based on a priori hypothesis and the statistical test of omnibus test (of each individual predictor variable).
I am only testing the main effects and not the interaction of multiple variables being included in the final models.
The problem I am running into is the small number of studies being available for many species, with the number of study response (effect size, not independent study) ranging from 6 to 100 for different fish species.
So my question is:
Is there a commonly used threshold for the multiple meta-regression using rma.mv to be reliable and avoid false positive or negative relationship?