[R-meta] rma.mv results issue

Tue, Dec 15, 2020 11:15 PM

Dear Eilysh,

Fo me to give a more informed answer, you would have to ideally provide a fully reproducible example that shows where you think things go wrong or at least show the output of the models.

One thing that I see though in your rma.mv() call is: random = ~1|Paper. This assumes that effects within papers are homogenous, which is a strong and often incorrect assumption. You should add random effects at the paper level (as you have done) and for estimates within papers. See:

http://www.metafor-project.org/doku.php/analyses:konstantopoulos2011

and especially the "A Common Mistake in the Three-Level Model" section.

So, do:

abiotic.data$Estimate <- 1:nrow(abiotic.data)

abiotic.m1 <- rma.mv(yi, V = vi, mods = ~ Soil.attribute -1, random = ~1|Paper/Estimate, method = "REML", data = abiotic.data)

This already might 'fix' the issue you are seeing.

Best,
Wolfgang

-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On Behalf Of EILYSH THOMPSON
Sent: Wednesday, 16 December, 2020 7:52
To: r-sig-meta-analysis at r-project.org
Subject: [R-meta] rma.mv results issue

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 th is 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

[R-meta] rma.mv results issue

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