Choose post-warumps samples by position

Choose post-warumps samples by their ordinal position from each parameter in a stanfit object

stan_slice(object, ..., inc_warmup = TRUE)

Arguments

object	stanfit object
...	Integer samples values
inc_warmup	logical, include warmup in output, Default: TRUE

Value

stanfit object

See also

Other filtering: stan_filter(), stan_thin_n()

Examples

# \donttest{
rats <- rats_example(nCores = 1)

rats%>%
  stan_select(mu_alpha)%>%
  stan_slice(1:30)
#> Inference for Stan model: rats.
#> 4 chains, each with iter=1030; warmup=1000; thin=1; 
#> post-warmup draws per chain=30, total post-warmup draws=120.
#> 
#>            mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
#> mu_alpha 242.34    0.14 2.27 238.35 240.75 242.24 243.87 247.16   250    1
#> 
#> Samples were drawn using  at Mon Aug 31 11:07:31 2020.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at 
#> convergence, Rhat=1).

rats%>%
  stan_select(mu_alpha)%>%
  stan_slice(1:30,inc_warmup = FALSE)
#> Inference for Stan model: rats.
#> 4 chains, each with iter=30; warmup=0; thin=1; 
#> post-warmup draws per chain=30, total post-warmup draws=120.
#> 
#>            mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
#> mu_alpha 242.34    0.14 2.27 238.35 240.75 242.24 243.87 247.16   250    1
#> 
#> Samples were drawn using  at Mon Aug 31 11:07:31 2020.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at 
#> convergence, Rhat=1).
# }