Partial match functions for stanfit objects

Partial match functions used in stan_select

stan_contains(match, ignore.case = TRUE, pars = peek_pars())

stan_starts_with(match, ignore.case = TRUE, pars = peek_pars())

stan_ends_with(match, ignore.case = TRUE, pars = peek_pars())

Arguments

match	character, pattern to search for
ignore.case	logical to ignore the case, Default: TRUE
pars	list of pars to search in, Default: peek_pars()

Value

character

Examples

# \donttest{
rats <- rats_example()

rats%>%
  stan_names()
#>  [1] "alpha"         "beta"          "mu_alpha"      "mu_beta"      
#>  [5] "sigmasq_y"     "sigmasq_alpha" "sigmasq_beta"  "sigma_y"      
#>  [9] "sigma_alpha"   "sigma_beta"    "alpha0"        "lp__"         

rats%>%
  stan_select(stan_contains('sq'))
#> Inference for Stan model: rats.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>                 mean se_mean    sd   2.5%    25%    50%    75%  97.5% n_eff
#> sigmasq_y      37.16    0.12  5.69  27.74  33.14  36.56  40.58  50.12  2366
#> sigmasq_alpha 218.39    1.06 63.89 126.08 173.31 208.62 251.30 372.24  3615
#> sigmasq_beta    0.27    0.00  0.10   0.13   0.21   0.26   0.32   0.52  3028
#>               Rhat
#> sigmasq_y        1
#> sigmasq_alpha    1
#> sigmasq_beta     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_contains('sq'))
#> Inference for Stan model: rats.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>                 mean se_mean    sd   2.5%    25%    50%    75%  97.5% n_eff
#> mu_alpha      242.47    0.05  2.76 236.95 240.61 242.50 244.38 247.70  3585
#> sigmasq_y      37.16    0.12  5.69  27.74  33.14  36.56  40.58  50.12  2366
#> sigmasq_alpha 218.39    1.06 63.89 126.08 173.31 208.62 251.30 372.24  3615
#> sigmasq_beta    0.27    0.00  0.10   0.13   0.21   0.26   0.32   0.52  3028
#>               Rhat
#> mu_alpha         1
#> sigmasq_y        1
#> sigmasq_alpha    1
#> sigmasq_beta     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(stan_starts_with('mu'))
#> Inference for Stan model: rats.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>            mean se_mean   sd   2.5%    25%    50%    75% 97.5% n_eff Rhat
#> mu_alpha 242.47    0.05 2.76 236.95 240.61 242.50 244.38 247.7  3585    1
#> mu_beta    6.19    0.00 0.11   5.98   6.12   6.19   6.25   6.4  4462    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(stan_starts_with('mu'),stan_contains('sq'))
#> Inference for Stan model: rats.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>                 mean se_mean    sd   2.5%    25%    50%    75%  97.5% n_eff
#> mu_alpha      242.47    0.05  2.76 236.95 240.61 242.50 244.38 247.70  3585
#> mu_beta         6.19    0.00  0.11   5.98   6.12   6.19   6.25   6.40  4462
#> sigmasq_y      37.16    0.12  5.69  27.74  33.14  36.56  40.58  50.12  2366
#> sigmasq_alpha 218.39    1.06 63.89 126.08 173.31 208.62 251.30 372.24  3615
#> sigmasq_beta    0.27    0.00  0.10   0.13   0.21   0.26   0.32   0.52  3028
#>               Rhat
#> mu_alpha         1
#> mu_beta          1
#> sigmasq_y        1
#> sigmasq_alpha    1
#> sigmasq_beta     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(stan_ends_with('0'))
#> Inference for Stan model: rats.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>          mean se_mean  sd  2.5% 25%    50%    75%  97.5% n_eff Rhat
#> alpha0 106.39    0.06 3.6 99.23 104 106.44 108.76 113.55  4122    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).

# free form regex in contains   
rats%>%
  stan_select(stan_contains('sq|mu'))
#> Inference for Stan model: rats.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#> 
#>                 mean se_mean    sd   2.5%    25%    50%    75%  97.5% n_eff
#> mu_alpha      242.47    0.05  2.76 236.95 240.61 242.50 244.38 247.70  3585
#> mu_beta         6.19    0.00  0.11   5.98   6.12   6.19   6.25   6.40  4462
#> sigmasq_y      37.16    0.12  5.69  27.74  33.14  36.56  40.58  50.12  2366
#> sigmasq_alpha 218.39    1.06 63.89 126.08 173.31 208.62 251.30 372.24  3615
#> sigmasq_beta    0.27    0.00  0.10   0.13   0.21   0.26   0.32   0.52  3028
#>               Rhat
#> mu_alpha         1
#> mu_beta          1
#> sigmasq_y        1
#> sigmasq_alpha    1
#> sigmasq_beta     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).
# }

Arguments

Value

See also

Examples