[R-meta] rma.mv results issue

[EILYSH THOMPSON]

I am currently undertaking a meta-analysis on the impacts of large invasive ungulates and have noticed something strange happening with the results when I run a rma.mv model for a section of the data. I noticed that one of my soil predictors (litter cover) had a mean effect size that was slightly positively correlated but knowing the data I'd expect it to be negatively correlated. All effect sizes for this predictor are negative hence I'd expect an overall negative correlation. I removed an outlier, it shifted it slightly in the negative direction. I removed some of my predictors as I realised I didn't have enough df in the model and that shifted it slightly further in a negative direction. However It wasn't until I removed the predictor (bare ground) that was the most significantly positively correlated that I got the result I was expecting with my other predictor. It was as if that one predictor was dragging the results in a positive direction. Is there any explanation as to why this would be happening? Interestingly when I ran a basic random model without specifying my random effect and a mcmcglmm model with the exact same structure as the problem rma.mv model I got results closer to what I was expecting.


When I run this model where I specify the random effect it is positively correlated (not significantly):
abiotic.m1 <- rma.mv(yi, V = vi, mods = ~ Soil.attribute -1, random = ~1|Paper, method = "REML", data = abiotic.data)


When I run a basic random model it is negatively correlated (significantly):
random_am <- rma(yi = yi, vi = vi,mods = ~ factor(Soil.attribute) -1, method = "REML", data = abiotic.data)


When I run this mcmcglmm model It is negatively correlated (significantly):
soil.m0 = MCMCglmm(fixed=yi~Soil.attribute-1, random=~Paper, mev=abiotic.data$vi, data=abiotic.data,prior=prior1,                  verbose=FALSE,nitt=40000, burnin=10000, thin=300)

Eilysh

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