thin n post-warumps samples from a stanfit object

This is a wrapper around stan_slice() to make it easy to thin samples from each parameter in a stanfit object.

stan_thin_n(object, size, inc_warmup = TRUE)

stan_thin_frac(object, size, inc_warmup = TRUE)

Arguments

object	stanfit object
size	numeric, for stan_thin_n size of thin, for stan_thin_frac fraction of samples to sample.
inc_warmup	logical, include warmup in output, Default: TRUE

Value

stanfit

See also

Other filtering: stan_filter(), stan_slice()

Examples

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

rats%>%
  stan_select(mu_alpha)%>%
  stan_thin_n(30)
#> Inference for Stan model: rats.
#> 4 chains, each with iter=1034; warmup=1000; thin=1; 
#> post-warmup draws per chain=34, total post-warmup draws=136.
#> 
#>            mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
#> mu_alpha 242.46    0.22 2.84 237.15 240.47 242.58 243.98 247.55   160    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_thin_frac(0.5)
#> Inference for Stan model: rats.
#> 4 chains, each with iter=1500; warmup=1000; thin=1; 
#> post-warmup draws per chain=500, total post-warmup draws=2000.
#> 
#>            mean se_mean   sd   2.5%    25%   50%   75%  97.5% n_eff Rhat
#> mu_alpha 242.49    0.07 2.75 237.12 240.59 242.5 244.4 247.69  1635    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_thin_n(30,inc_warmup = FALSE)
#> Inference for Stan model: rats.
#> 4 chains, each with iter=34; warmup=0; thin=1; 
#> post-warmup draws per chain=34, total post-warmup draws=136.
#> 
#>            mean se_mean   sd   2.5%    25%    50%    75%  97.5% n_eff Rhat
#> mu_alpha 242.46    0.22 2.84 237.15 240.47 242.58 243.98 247.55   160    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).
# }