Objects for use in examples, vignettes, and tests.
example_draws(example = "eight_schools")(string) The name of the example draws object. See
Details for available options.
A draws object.
The following example draws objects are available.
eight_schools: A draws_array object with 100 iterations
from each of 4 Markov chains obtained by fitting the eight schools model
described in Gelman et al. (2013) with Stan. The
variables are:
mu: Overall mean of the eight schools
tau: Standard deviation between schools
theta: Individual means of each of the eight schools
multi_normal: A draws_array object with 100 iterations from each of
the 4 Markov chains obtained by fitting a 3-dimensional multivariate normal
model to 100 simulated observations. The variables are:
mu: Mean parameter vector of length 3
Sigma: Covariance matrix of dimension 3 x 3
These objects are only intended to be used in demonstrations and tests. They contain fewer iterations and chains than recommended for performing actual inference.
Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC.
draws_eight_schools <- example_draws("eight_schools")
summarise_draws(draws_eight_schools)
#> # A tibble: 10 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu 4.18 4.16 3.40 3.57 -0.854 9.39 1.02 558. 322.
#> 2 tau 4.16 3.07 3.58 2.89 0.309 11.0 1.01 246. 202.
#> 3 theta[1] 6.75 5.97 6.30 4.87 -1.23 18.9 1.01 400. 254.
#> 4 theta[2] 5.25 5.13 4.63 4.25 -1.97 12.5 1.02 564. 372.
#> 5 theta[3] 3.04 3.99 6.80 4.94 -10.3 11.9 1.01 312. 205.
#> 6 theta[4] 4.86 4.99 4.92 4.51 -3.57 12.2 1.02 695. 252.
#> 7 theta[5] 3.22 3.72 5.08 4.38 -5.93 10.8 1.01 523. 306.
#> 8 theta[6] 3.99 4.14 5.16 4.81 -4.32 11.5 1.02 548. 205.
#> 9 theta[7] 6.50 5.90 5.26 4.54 -1.19 15.4 1.00 434. 308.
#> 10 theta[8] 4.57 4.64 5.25 4.89 -3.79 12.2 1.02 355. 146.
draws_multi_normal <- example_draws("multi_normal")
summarise_draws(draws_multi_normal)
#> # A tibble: 12 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 mu[1] 0.0514 0.0575 0.112 0.131 -0.130 0.225 1.01 677. 356.
#> 2 mu[2] 0.111 0.104 0.199 0.198 -0.208 0.449 1.00 566. 426.
#> 3 mu[3] 0.186 0.184 0.314 0.315 -0.322 0.715 1.02 650. 334.
#> 4 Sigma[1,1] 1.28 1.26 0.165 0.173 1.03 1.56 1.00 742. 369.
#> 5 Sigma[2,1] 0.525 0.502 0.200 0.173 0.227 0.874 1.01 454. 239.
#> 6 Sigma[3,1] -0.403 -0.393 0.282 0.267 -0.874 0.0432 1.01 468. 308.
#> 7 Sigma[1,2] 0.525 0.502 0.200 0.173 0.227 0.874 1.01 454. 239.
#> 8 Sigma[2,2] 3.67 3.62 0.447 0.433 3.02 4.40 1.01 529. 363.
#> 9 Sigma[3,2] -2.10 -2.11 0.480 0.469 -2.87 -1.39 1.02 434. 357.
#> 10 Sigma[1,3] -0.403 -0.393 0.282 0.267 -0.874 0.0432 1.01 468. 308.
#> 11 Sigma[2,3] -2.10 -2.11 0.480 0.469 -2.87 -1.39 1.02 434. 357.
#> 12 Sigma[3,3] 8.12 8.02 0.946 0.941 6.71 9.91 0.997 729. 324.