MC_ranks {BayesMultMeta}

Computes the ranks within the pooled draws of Markov chains

Description

The function computes the ranks within the pooled draws of Markov chains. Average ranks are used for ties.

Usage

MC_ranks(MC)

Arguments

MC

An N \times M matrix with N draws in each of M constructed Markov chains.

Value

a matrix with the ranks from the MCMC procedure

Examples

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
}
# Generating M Markov chains for mu_1
M<-4 # number of chains
MC <-NULL
for (i in 1:M) {
chain <-  BayesMultMeta(X, U, 1e2, burn_in = 1e2,
                          likelihood = "t", prior="jeffrey",
                          algorithm_version = "mu",d=3)
  MC<- cbind(MC,chain$mu[1,])
}
ranks<-MC_ranks(MC)
id_chain <- 1
hist(ranks[,id_chain],breaks=25,prob=TRUE, labels = FALSE, border = "dark blue",
  col = "light blue", main = expression("Chain 1,"~mu[1]), xlab = expression(),
  ylab = expression(),cex.axis=1.2,cex.main=1.7,font=2)