Return the number of posterior samples
Examples
## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 20x more warmup & iter in practice
.user_mc_options <- options(RBesT.MC.warmup=50, RBesT.MC.iter=100,
RBesT.MC.chains=2, RBesT.MC.thin=1)
set.seed(34563)
map_AS <- gMAP(cbind(r, n - r) ~ 1 | study,
family = binomial,
data = AS,
tau.dist = "HalfNormal", tau.prior = 1,
beta.prior = 2
)
#> Assuming default prior location for beta: 0
#> Warning: The largest R-hat is 1.31, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
#> Warning: Maximal Rhat > 1.1. Consider increasing RBesT.MC.warmup MCMC parameter.
#> Final MCMC sample equivalent to less than 1000 independent draws.
#> Please consider increasing the MCMC simulation size.
nsamples(map_AS)
#> [1] 100
## Recover user set sampling defaults
options(.user_mc_options)