data(sbp.MC)
# How was the data generated
attr(sbp.MC,"mcmc.par")
# Traceplots
trace.MCmcmc(sbp.MC)
trace.MCmcmc(sbp.MC,"beta")
# A MCmcmc object also has class mcmc.list, so we can use the
# standard coda functions for convergence diagnostics:
acfplot( subset.MCmcmc(sbp.MC,subset="sigma") )
# Have a look at the correlation between the 9 variance parameters
pairs.MCmcmc( sbp.MC )
# Have a look at whether the MxI variance components are the same between methods:
## Not run:
# pairs.MCmcmc( sbp.MC, subset=c("mi"), eq=TRUE,
# panel=function(x,y,...)
# {
# abline(0,1)
# abline(v=median(x),h=median(y),col="gray")
# points(x,y,...)
# }
# ) ## End(Not run)
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