This object is included for illustrative purposes. It is a result of a
5-hour run using MCmcmc, with n.iter=100000.
The format is a MCmcmc object.
The data are the venous measurements from the hba1c dataset,
using the day of analysis as replicate. Measurements are taken to be linked
within replicate (=day of analysis).
data(hba.MC)
attr(hba.MC,"mcmc.par")
#> $n.chains
#> [1] 4
#>
#> $n.iter
#> [1] 50000
#>
#> $n.burnin
#> [1] 25000
#>
#> $n.thin
#> [1] 25
#>
#> $dim
#> [1] 4000 39
#>
# print.MCmcmc(hba.MC)
# One of the chains is really fishy (it's the first one)
# trace.MCmcmc(hba.MC)
# trace.MCmcmc(hba.MC,"beta")
# Try to have a look, excluding the first chain
# hba.MCsub <- subset.MCmcmc(hba.MC,chains=-1)
# trace.MCmcmc(hba.MCsub)
# trace.MCmcmc(hba.MCsub,"beta")
# A MCmcmc object also has class mcmc.list, so we can use the
# coda functions for covergence diagnostics:
# acfplot( subset.MCmcmc(hba.MC, subset="sigma"))