[R-meta] rma.mv results issue
To add to Wolfgang's comments, could you tell us about what the Soil.attribute predictor represents? Is it a quantitative measure or a categorical one? If it is quantitative, then why are you subtracting the intercept from the model? (mods = ~ Soil.attribute - 1) If it is categorical, then you'll need to ensure that it is coded as a factor. If you don't metafor will assume that it is a quantitative predictor and so give you a single slope coefficient estimate, which won't make any sense at all. James On Wed, Dec 16, 2020 at 1:16 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:
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-sig-meta-analysis mailing list R-sig-meta-analysis at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis