Dear list,
I am trying to run a random effects meta-analysis in MCMCglmm. I have a data set of 960 effect size estimates and their standard errors for eight traits (120 estimates per trait). 
What I want to know is if there are any differences among traits in both mean effect size and in the variance of their effect sizes.
So I have created a diagonal matrix of the standard errors (SE) and used singular value decomposition to create a model matrix (Z) which I have then fit using idv(Z) whilst fixing the variance to 1. I have also estimated an effect of trait as random, and then estimated a separate residual variance for each trait. My coding is below:

Rmat<-matrix(0,nrow(data),nrow(data))
diag(Rmat)<-data$SE
Rsvd<-svd(Rmat)
Rsvd<-Rsvd$v%*%(t(Rsvd$u)*sqrt(Rsvd$d))
data$row<-1:nrow(data)
data$row<-factor(data$row)
Z<-model.matrix(~row-1, data)%*%Rsvd
data$Z<-Z
prior=list(R=list(V=diag(8), nu=7), G=list(G1=list(V=diag(1),nu=1,alpha.mu=rep(0,1),alpha.V=diag(1)*1000),G2=list(V=1, fix=1)))
m1<-MCMCglmm(estimate ~ 1, random=~Trait + idv(Z), rcov=~ idh(Trait):units, data=data, prior=prior, family="gaussian", pr=TRUE, burnin=40000, thin=100, nitt=140000)


I first wanted to check if this seemed sensible to people?  

One issue is that all of the estimates are quite small numbers, ranging from 0 to 0.14. I have tried multiplying all of my data (effect sizes and their SE) by 10 and then re-running the exact same model and this seems to make the posterior traces look much better. So secondly I wanted to get an opinion on whether this is a sensible thing to do?

Many thanks in advance for any advice anyone can provide.

Best wishes,
Matt


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Dr. Matt Robinson
NERC Research Fellow
Department of Animal and Plant Science
University of Sheffield
Alfred Denny Building, Western Bank
Sheffield, S10 2TN, UK

matthew.r.robinson@sheffield.ac.uk

tel:  +44 (0)114 222 4707
fax: +44 (0)114 222 0002
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