The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
This package implements the methods developed in [1]
To simulate samples from the posterior using the methods developed in the paper you simply need to run
set.seed(2021)
dataREM<-mvmeta::hyp
# Observation matrix X
X<-t(cbind(dataREM$sbp,dataREM$dbp))
p<-nrow(X) # model dimension
n<-ncol(X) # sample size
# Matrix U
U<-matrix(0,n*p,n*p)
for (i_n in 1:n) {
Use<-diag(c(dataREM$sbp_se[i_n],dataREM$dbp_se[i_n]))
Corr_mat<-matrix(c(1,dataREM$rho[i_n],dataREM$rho[i_n],1),p,p)
U[(p*(i_n-1)+1):(p*i_n),(p*(i_n-1)+1):(p*i_n)]<- Use%*%Corr_mat%*%Use
}
bmgmr_run <- BayesMultMeta(X, U, 1e4, burn_in = 100,
likelihood = "normal", prior="jeffrey",
algorithm_version = "A")
summary(bmgmr_run)[1] Olha Bodnar, Taras Bodnar (2021). Objective Bayesian meta-analysis based on generalized multivariate random effects model. Under revision in Bayesian analysis.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.