The $metadata() method returns a list of information gathered
from the CSV output files, including the CmdStan configuration used when
fitting the model. See Examples and read_cmdstan_csv().
metadata()# \dontrun{
fit_mcmc <- cmdstanr_example("logistic", method = "sample")
str(fit_mcmc$metadata())
#> List of 40
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
#> $ start_datetime : chr "2023-12-13 17:04:42 UTC"
#> $ method : chr "sample"
#> $ save_warmup : num 0
#> $ thin : num 1
#> $ gamma : num 0.05
#> $ kappa : num 0.75
#> $ t0 : num 10
#> $ init_buffer : num 75
#> $ term_buffer : num 50
#> $ window : num 25
#> $ algorithm : chr "hmc"
#> $ engine : chr "nuts"
#> $ metric : chr "diag_e"
#> $ stepsize_jitter : num 0
#> $ num_chains : num 1
#> $ id : num [1:4] 1 2 3 4
#> $ init : num [1:4] 2 2 2 2
#> $ seed : num 1.08e+09
#> $ refresh : num 100
#> $ sig_figs : num -1
#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-79f552.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
#> $ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
#> $ step_size_adaptation: num [1:4] 0.723 0.882 0.802 0.749
#> $ model_name : chr "logistic_model"
#> $ adapt_engaged : num 1
#> $ adapt_delta : num 0.8
#> $ max_treedepth : num 10
#> $ step_size : num [1:4] 1 1 1 1
#> $ iter_warmup : num 1000
#> $ iter_sampling : num 1000
#> $ threads_per_chain : num 1
#> $ time :'data.frame': 4 obs. of 4 variables:
#> ..$ chain_id: num [1:4] 1 2 3 4
#> ..$ warmup : num [1:4] 0.022 0.022 0.022 0.022
#> ..$ sampling: num [1:4] 0.069 0.065 0.07 0.068
#> ..$ total : num [1:4] 0.091 0.087 0.092 0.09
#> $ stan_variable_sizes :List of 4
#> ..$ lp__ : num 1
#> ..$ alpha : num 1
#> ..$ beta : num 3
#> ..$ log_lik: num 100
#> $ stan_variables : chr [1:4] "lp__" "alpha" "beta" "log_lik"
#> $ model_params : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
fit_mle <- cmdstanr_example("logistic", method = "optimize")
str(fit_mle$metadata())
#> List of 31
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
#> $ start_datetime : chr "2023-12-13 17:04:44 UTC"
#> $ method : chr "optimize"
#> $ algorithm : chr "lbfgs"
#> $ init_alpha : num 0.001
#> $ tol_obj : num 1e-12
#> $ tol_rel_obj : num 10000
#> $ tol_grad : num 1e-08
#> $ tol_rel_grad : num 1e+07
#> $ tol_param : num 1e-08
#> $ history_size : num 5
#> $ jacobian : num 0
#> $ iter : num 2000
#> $ save_iterations : num 0
#> $ id : num 1
#> $ init : num 2
#> $ seed : num 9.12e+08
#> $ refresh : num 100
#> $ sig_figs : num -1
#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-7b3d9a.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics: chr(0)
#> $ variables : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
#> $ model_name : chr "logistic_model"
#> $ threads : num 1
#> $ time :'data.frame': 0 obs. of 0 variables
#> $ stan_variable_sizes:List of 4
#> ..$ lp__ : num 1
#> ..$ alpha : num 1
#> ..$ beta : num 3
#> ..$ log_lik: num 100
#> $ stan_variables : chr [1:4] "lp__" "alpha" "beta" "log_lik"
#> $ model_params : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
fit_vb <- cmdstanr_example("logistic", method = "variational")
str(fit_vb$metadata())
#> List of 29
#> $ stan_version_major : num 2
#> $ stan_version_minor : num 33
#> $ stan_version_patch : num 0
#> $ start_datetime : chr "2023-12-13 17:04:44 UTC"
#> $ method : chr "variational"
#> $ algorithm : chr "meanfield"
#> $ iter : num 50
#> $ grad_samples : num 1
#> $ elbo_samples : num 100
#> $ eta : num 1
#> $ tol_rel_obj : num 0.01
#> $ eval_elbo : num 100
#> $ output_samples : num 1000
#> $ id : num 1
#> $ init : num 2
#> $ seed : num 3.89e+08
#> $ refresh : num 100
#> $ sig_figs : num -1
#> $ profile_file : chr "/var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-profile-202312131004-1-645608.csv"
#> $ stanc_version : chr "stanc3 v2.33.1"
#> $ sampler_diagnostics: chr(0)
#> $ variables : chr [1:106] "lp__" "lp_approx__" "alpha" "beta[1]" ...
#> $ model_name : chr "logistic_model"
#> $ adapt_engaged : num 1
#> $ threads : num 1
#> $ time :'data.frame': 0 obs. of 0 variables
#> $ stan_variable_sizes:List of 5
#> ..$ lp__ : num 1
#> ..$ lp_approx__: num 1
#> ..$ alpha : num 1
#> ..$ beta : num 3
#> ..$ log_lik : num 100
#> $ stan_variables : chr [1:5] "lp__" "lp_approx__" "alpha" "beta" ...
#> $ model_params : chr [1:106] "lp__" "lp_approx__" "alpha" "beta[1]" ...
# }