Fit models for use in examples
cmdstanr_example(
example = c("logistic", "schools", "schools_ncp"),
method = c("sample", "optimize", "laplace", "variational", "pathfinder", "diagnose"),
...,
quiet = TRUE,
force_recompile = getOption("cmdstanr_force_recompile", default = FALSE)
)
print_example_program(example = c("logistic", "schools", "schools_ncp"))(string) The name of the example. The currently available examples are
"logistic": logistic regression with intercept and 3 predictors.
"schools": the so-called "eight schools" model, a hierarchical
meta-analysis. Fitting this model will result in warnings about
divergences.
"schools_ncp": non-centered parameterization of the "eight schools"
model that fixes the problem with divergences.
To print the Stan code for a given example use
print_example_program(example).
(string) Which fitting method should be used? The default is
the "sample" method (MCMC).
Arguments passed to the chosen method. See the help pages for
the individual methods for details.
(logical) If TRUE (the default) then fitting the model is
wrapped in utils::capture.output().
Passed to the $compile() method.
The fitted model object returned by the selected method.
# \dontrun{
print_example_program("logistic")
#> data {
#> int<lower=0> N;
#> int<lower=0> K;
#> array[N] int<lower=0, upper=1> y;
#> matrix[N, K] X;
#> }
#> parameters {
#> real alpha;
#> vector[K] beta;
#> }
#> model {
#> target += normal_lpdf(alpha | 0, 1);
#> target += normal_lpdf(beta | 0, 1);
#> target += bernoulli_logit_glm_lpmf(y | X, alpha, beta);
#> }
#> generated quantities {
#> vector[N] log_lik;
#> for (n in 1 : N) {
#> log_lik[n] = bernoulli_logit_lpmf(y[n] | alpha + X[n] * beta);
#> }
#> }
fit_logistic_mcmc <- cmdstanr_example("logistic", chains = 2)
fit_logistic_mcmc$summary()
#> # A tibble: 105 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ -66.0 -65.6 1.43 1.22 -68.8 -64.3 1.00 963.
#> 2 alpha 0.376 0.373 0.220 0.221 0.0226 0.745 1.00 2237.
#> 3 beta[1] -0.671 -0.663 0.250 0.252 -1.10 -0.258 1.00 1930.
#> 4 beta[2] -0.262 -0.261 0.222 0.229 -0.629 0.0934 1.00 1801.
#> 5 beta[3] 0.674 0.681 0.264 0.263 0.242 1.10 1.00 1979.
#> 6 log_lik[1] -0.515 -0.509 0.0991 0.0964 -0.681 -0.363 1.00 2100.
#> 7 log_lik[2] -0.401 -0.381 0.147 0.138 -0.671 -0.197 1.00 2010.
#> 8 log_lik[3] -0.489 -0.456 0.215 0.202 -0.891 -0.207 1.00 1914.
#> 9 log_lik[4] -0.455 -0.435 0.154 0.152 -0.726 -0.242 1.00 1918.
#> 10 log_lik[5] -1.18 -1.16 0.283 0.282 -1.69 -0.749 1.00 2470.
#> # ℹ 95 more rows
#> # ℹ 1 more variable: ess_tail <dbl>
fit_logistic_optim <- cmdstanr_example("logistic", method = "optimize")
fit_logistic_optim$summary()
#> # A tibble: 105 × 2
#> variable estimate
#> <chr> <dbl>
#> 1 lp__ -63.9
#> 2 alpha 0.364
#> 3 beta[1] -0.632
#> 4 beta[2] -0.259
#> 5 beta[3] 0.648
#> 6 log_lik[1] -0.515
#> 7 log_lik[2] -0.394
#> 8 log_lik[3] -0.469
#> 9 log_lik[4] -0.442
#> 10 log_lik[5] -1.14
#> # ℹ 95 more rows
fit_logistic_vb <- cmdstanr_example("logistic", method = "variational")
fit_logistic_vb$summary()
#> # A tibble: 106 × 7
#> variable mean median sd mad q5 q95
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ -66.4 -65.9 1.84 1.52 -69.9 -64.3
#> 2 lp_approx__ -1.98 -1.65 1.42 1.25 -4.64 -0.331
#> 3 alpha 0.377 0.370 0.296 0.307 -0.116 0.869
#> 4 beta[1] -0.646 -0.648 0.241 0.234 -1.04 -0.241
#> 5 beta[2] -0.252 -0.257 0.201 0.191 -0.579 0.0845
#> 6 beta[3] 0.702 0.695 0.280 0.269 0.236 1.16
#> 7 log_lik[1] -0.523 -0.518 0.128 0.130 -0.747 -0.331
#> 8 log_lik[2] -0.398 -0.369 0.168 0.155 -0.716 -0.174
#> 9 log_lik[3] -0.480 -0.450 0.204 0.193 -0.859 -0.211
#> 10 log_lik[4] -0.455 -0.431 0.159 0.160 -0.739 -0.235
#> # ℹ 96 more rows
print_example_program("schools")
#> data {
#> int<lower=1> J;
#> vector<lower=0>[J] sigma;
#> vector[J] y;
#> }
#> parameters {
#> real mu;
#> real<lower=0> tau;
#> vector[J] theta;
#> }
#> model {
#> target += normal_lpdf(tau | 0, 10);
#> target += normal_lpdf(mu | 0, 10);
#> target += normal_lpdf(theta | mu, tau);
#> target += normal_lpdf(y | theta, sigma);
#> }
fit_schools_mcmc <- cmdstanr_example("schools")
#> Warning: 260 of 4000 (6.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
#> Warning: 1 of 4 chains had an E-BFMI less than 0.3.
#> See https://mc-stan.org/misc/warnings for details.
fit_schools_mcmc$summary()
#> # A tibble: 11 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ -58.0 -58.4 5.27 5.30 -66.3 -48.5 1.07 43.0 27.8
#> 2 mu 6.38 6.13 4.15 3.80 -0.237 13.5 1.01 627. 964.
#> 3 tau 5.31 4.49 3.57 3.28 1.10 12.2 1.07 37.6 21.2
#> 4 theta[1] 9.14 8.16 7.00 6.00 -0.685 21.7 1.01 966. 1427.
#> 5 theta[2] 6.73 6.51 5.64 4.88 -2.30 16.4 1.02 1127. 1732.
#> 6 theta[3] 5.08 5.36 6.58 5.64 -6.24 15.5 1.02 792. 1549.
#> 7 theta[4] 6.57 6.30 5.92 5.19 -2.79 16.1 1.01 1328. 1869.
#> 8 theta[5] 4.56 4.87 5.47 5.08 -5.00 13.1 1.02 707. 1473.
#> 9 theta[6] 5.31 5.53 6.01 5.11 -5.18 14.7 1.01 834. 1685.
#> 10 theta[7] 9.04 8.41 5.90 5.30 0.408 19.3 1.01 701. 1390.
#> 11 theta[8] 6.88 6.54 6.69 5.37 -4.06 18.2 1.02 1238. 2106.
print_example_program("schools_ncp")
#> data {
#> int<lower=1> J;
#> vector<lower=0>[J] sigma;
#> vector[J] y;
#> }
#> parameters {
#> real mu;
#> real<lower=0> tau;
#> vector[J] theta_raw;
#> }
#> transformed parameters {
#> vector[J] theta = mu + tau * theta_raw;
#> }
#> model {
#> target += normal_lpdf(tau | 0, 10);
#> target += normal_lpdf(mu | 0, 10);
#> target += normal_lpdf(theta_raw | 0, 1);
#> target += normal_lpdf(y | theta, sigma);
#> }
fit_schools_ncp_mcmc <- cmdstanr_example("schools_ncp")
fit_schools_ncp_mcmc$summary()
#> # A tibble: 19 × 10
#> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 lp__ -46.9 -4.66e+1 2.40 2.30 -51.2 -43.5 1.00 1569. 2358.
#> 2 mu 6.51 6.55e+0 4.22 4.14 -0.355 13.3 1.00 3009. 2465.
#> 3 tau 4.86 4.06e+0 3.67 3.43 0.444 12.0 1.00 1792. 1620.
#> 4 theta_r… 0.348 3.50e-1 0.956 0.960 -1.26 1.93 1.00 3527. 2677.
#> 5 theta_r… 0.0410 5.49e-2 0.889 0.880 -1.47 1.46 1.00 4110. 2991.
#> 6 theta_r… -0.142 -1.47e-1 0.953 0.945 -1.69 1.43 1.00 4686. 2441.
#> 7 theta_r… -0.0107 -9.40e-3 0.937 0.935 -1.53 1.53 1.00 4262. 2854.
#> 8 theta_r… -0.296 -2.93e-1 0.931 0.915 -1.83 1.24 1.00 3286. 2516.
#> 9 theta_r… -0.172 -1.84e-1 0.911 0.881 -1.69 1.36 1.00 3801. 2532.
#> 10 theta_r… 0.360 3.84e-1 0.933 0.904 -1.21 1.83 1.00 3900. 2825.
#> 11 theta_r… 0.0773 7.71e-2 0.982 1.00 -1.54 1.67 1.00 4239. 2766.
#> 12 theta[1] 9.01 8.06e+0 6.94 5.65 -0.546 22.1 1.00 3731. 3102.
#> 13 theta[2] 6.85 6.80e+0 5.61 5.13 -2.12 16.3 1.00 4538. 3046.
#> 14 theta[3] 5.47 5.82e+0 6.48 5.46 -6.06 15.3 1.00 4254. 3124.
#> 15 theta[4] 6.52 6.47e+0 5.74 5.18 -2.72 15.9 1.00 4625. 3465.
#> 16 theta[5] 4.79 5.10e+0 5.61 5.15 -4.87 13.3 1.00 4852. 3150.
#> 17 theta[6] 5.54 5.73e+0 5.60 5.11 -3.94 14.1 1.00 4021. 3062.
#> 18 theta[7] 8.95 8.27e+0 6.02 5.41 0.317 19.7 1.00 4087. 3495.
#> 19 theta[8] 7.00 7.00e+0 6.58 5.65 -3.39 17.8 1.00 3992. 3246.
# optimization fails for hierarchical model
cmdstanr_example("schools", "optimize", quiet = FALSE)
#> Initial log joint probability = -57.1999
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
#> 99 137.364 0.389882 2.12196e+10 0.1758 0.3216 199
#> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
#> 175 252.319 0.0285374 7.72538e+16 1e-12 0.001 386 LS failed, Hessian reset
#> Chain 1 Optimization terminated with error:
#> Chain 1 Line search failed to achieve a sufficient decrease, no more progress can be made
#> Warning: Fitting finished unexpectedly! Use the $output() method for more information.
#> Finished in 0.2 seconds.
#> Error: Fitting failed. Unable to print.
# }