Nothing

```
PSRF <-
function(MCMCseqs,m){
# compute the potential scale reduction factor (PSRF) from multiple MCMC sequences (for a scalar or vector variable)
# INPUT:
# - MCMCseqs : a mn x k matrix where m is the number of MCMC sequences and k is the dimension of the variable whose distribution is sampled
# the m sequences are diplasyed one underneath the other
# - m: the number of MCMC sequences
# OUPUT: PSRF
# depends on: .
n=dim(MCMCseqs)[1]/m
posJ= seq(1,dim(MCMCseqs)[1],by=n)
phi_seq_bar = matrix(0, length(posJ), dim(MCMCseqs)[2])
s2_seq= matrix(0, length(posJ), dim(MCMCseqs)[2])
# compute within sequence mean phi_seq_bar & variance s2_seq
for (i in 1:(length(posJ))){
phi_seq_bar[i,]=apply(MCMCseqs[posJ[i]:(posJ[i]+n-1),],2,mean)
s2_seq[i,]=apply(MCMCseqs[posJ[i]:(posJ[i]+n-1),],2,var)
}
# compute between sequence mean phi_bar
phi_bar=apply(phi_seq_bar,2, mean)
# compute within sequence variance W
W=mean(apply(s2_seq,2,mean))
# compute between sequence variance B
B=sum((phi_seq_bar-t(matrix((phi_bar),dim(phi_seq_bar)[2],dim(phi_seq_bar)[1])))^2)*n/(dim(phi_seq_bar)[1]-1)/dim(phi_seq_bar)[2]
# unbiased estimated variancevarPhi
varPhi=(n-1)/n*W + B/n
# return PSRF
return (mean(varPhi)/mean(W))
}
```

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