[R-meta] rma.mv results issue
jepu@to @end|ng |rom gm@||@com
Wed Dec 16 17:18:23 CET 2020
To add to Wolfgang's comments, could you tell us about what the
Soil.attribute predictor represents? Is it a quantitative measure or a
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.
On Wed, Dec 16, 2020 at 1:16 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using 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:
> 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.
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org]
> >On Behalf Of EILYSH THOMPSON
> >Sent: Wednesday, 16 December, 2020 7:52
> >To: r-sig-meta-analysis using r-project.org
> >Subject: [R-meta] rma.mv results issue
> >I am currently undertaking a meta-analysis on the impacts of large
> >ungulates and have noticed something strange happening with the results
> >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
> >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.
> >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
> >random_am <- rma(yi = yi, vi = vi,mods = ~ factor(Soil.attribute) -1,
> >= "REML", data = abiotic.data)
> >When I run this mcmcglmm model It is negatively correlated
> >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)
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