Select parameters by name

Choose variables from a stanfit object

stan_select(object, ...)

Arguments

object	a stanfit object
...	One or more unquoted expressions separated by commas

Value

stanfit object

Examples

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

rats%>%stan_select(mu_beta)
#> 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_beta 6.19       0 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(mu_beta,beta)
#> 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
#> beta[1]  6.06       0 0.24 5.59 5.91 6.07 6.22  6.53  5584    1
#> beta[2]  7.05       0 0.26 6.55 6.88 7.05 7.22  7.55  4936    1
#> beta[3]  6.48       0 0.24 6.02 6.32 6.48 6.65  6.97  4433    1
#> beta[4]  5.34       0 0.26 4.82 5.17 5.34 5.52  5.84  5458    1
#> beta[5]  6.57       0 0.24 6.09 6.41 6.57 6.73  7.05  5527    1
#> beta[6]  6.17       0 0.24 5.70 6.00 6.17 6.34  6.64  5028    1
#> beta[7]  5.97       0 0.24 5.50 5.81 5.97 6.14  6.44  5714    1
#> beta[8]  6.42       0 0.24 5.95 6.25 6.41 6.59  6.90  5518    1
#> beta[9]  7.05       0 0.25 6.54 6.89 7.05 7.22  7.54  5162    1
#> beta[10] 5.84       0 0.24 5.36 5.68 5.84 6.00  6.31  5171    1
#> beta[11] 6.80       0 0.25 6.31 6.63 6.80 6.97  7.28  5098    1
#> beta[12] 6.12       0 0.24 5.65 5.96 6.11 6.28  6.58  5552    1
#> beta[13] 6.16       0 0.25 5.65 6.01 6.16 6.32  6.66  5429    1
#> beta[14] 6.69       0 0.24 6.22 6.52 6.69 6.85  7.17  5107    1
#> beta[15] 5.42       0 0.25 4.94 5.25 5.41 5.59  5.91  4556    1
#> beta[16] 5.93       0 0.24 5.45 5.77 5.93 6.09  6.39  5506    1
#> beta[17] 6.28       0 0.24 5.82 6.12 6.28 6.44  6.74  5684    1
#> beta[18] 5.84       0 0.24 5.36 5.68 5.83 6.00  6.30  5159    1
#> beta[19] 6.40       0 0.24 5.93 6.23 6.40 6.56  6.85  5036    1
#> beta[20] 6.05       0 0.25 5.56 5.89 6.05 6.22  6.54  6193    1
#> beta[21] 6.40       0 0.24 5.93 6.24 6.40 6.56  6.86  6641    1
#> beta[22] 5.86       0 0.24 5.40 5.69 5.86 6.02  6.31  5890    1
#> beta[23] 5.75       0 0.24 5.27 5.59 5.75 5.91  6.23  6016    1
#> beta[24] 5.89       0 0.24 5.41 5.73 5.89 6.05  6.37  6260    1
#> beta[25] 6.91       0 0.25 6.42 6.74 6.90 7.07  7.40  4974    1
#> beta[26] 6.54       0 0.24 6.06 6.39 6.55 6.70  7.01  5722    1
#> beta[27] 5.90       0 0.24 5.41 5.73 5.90 6.06  6.38  5821    1
#> beta[28] 5.85       0 0.23 5.40 5.69 5.84 6.01  6.31  5740    1
#> beta[29] 5.68       0 0.25 5.20 5.51 5.67 5.84  6.17  5303    1
#> beta[30] 6.13       0 0.23 5.68 5.97 6.12 6.28  6.59  6428    1
#> mu_beta  6.19       0 0.11 5.98 6.12 6.19 6.25  6.40  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('mu_beta')
#> 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_beta 6.19       0 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).

tmp <- c('mu_beta','mu_alpha')

rats%>%stan_select(!!!rlang::syms(tmp))
#> 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).
# }

Arguments

Value

See also

Examples