This object is included for illustrative purposes. It is a result of using
MCmcmc, with n.iter=20000.
The format is a MCmcmc object.
The data are the ox dataset, where measurements are linked
within replicate (=day of analysis).
data(ox.MC)
attr(ox.MC,"mcmc.par")
#> $n.chains
#> [1] 4
#>
#> $n.iter
#> [1] 20000
#>
#> $n.burnin
#> [1] 10000
#>
#> $n.thin
#> [1] 10
#>
#> $dim
#> [1] 4000 18
#>
if (FALSE) { # \dontrun{
print.MCmcmc(ox.MC)
trace.MCmcmc(ox.MC)
trace.MCmcmc(ox.MC,"beta")
post.MCmcmc(ox.MC)
post.MCmcmc(ox.MC,"beta") } # }
# A MCmcmc object also has class mcmc.list, so we can use the
# coda functions for covergence diagnostics:
if (FALSE) acfplot( subset.MCmcmc(ox.MC, subset="sigma")) # \dontrun{}