Extract metadata from CmdStan CSV files

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()

Examples

# \dontrun{
fit_mcmc <- cmdstanr_example("logistic", method = "sample")
#> Model executable is up to date!
str(fit_mcmc$metadata())
#> List of 33
#>  $ stan_version_major  : num 2
#>  $ stan_version_minor  : num 25
#>  $ stan_version_patch  : num 0
#>  $ 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
#>  $ id                  : num [1:4] 1 2 3 4
#>  $ init                : num [1:4] 2 2 2 2
#>  $ seed                : num [1:4] 7.77e+07 2.10e+09 1.87e+09 1.79e+09
#>  $ refresh             : num 100
#>  $ sig_figs            : num -1
#>  $ sampler_diagnostics : chr [1:6] "accept_stat__" "stepsize__" "treedepth__" "n_leapfrog__" ...
#>  $ model_params        : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
#>  $ step_size_adaptation: num [1:4] 0.729 0.751 0.632 0.796
#>  $ 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
#>  $ stan_variable_dims  :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"

fit_mle <- cmdstanr_example("logistic", method = "optimize")
#> Model executable is up to date!
str(fit_mle$metadata())
#> List of 24
#>  $ stan_version_major : num 2
#>  $ stan_version_minor : num 25
#>  $ stan_version_patch : num 0
#>  $ 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
#>  $ iter               : num 2000
#>  $ save_iterations    : num 0
#>  $ id                 : num 1
#>  $ init               : num 2
#>  $ seed               : num 1.1e+09
#>  $ refresh            : num 100
#>  $ sig_figs           : num -1
#>  $ sampler_diagnostics: chr(0) 
#>  $ model_params       : chr [1:105] "lp__" "alpha" "beta[1]" "beta[2]" ...
#>  $ model_name         : chr "logistic_model"
#>  $ stan_variable_dims :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"

fit_vb <- cmdstanr_example("logistic", method = "variational")
#> Model executable is up to date!
str(fit_vb$metadata())
#> List of 23
#>  $ stan_version_major : num 2
#>  $ stan_version_minor : num 25
#>  $ stan_version_patch : num 0
#>  $ 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 2.03e+09
#>  $ refresh            : num 100
#>  $ sig_figs           : num -1
#>  $ sampler_diagnostics: chr(0) 
#>  $ model_params       : chr [1:106] "lp__" "lp_approx__" "alpha" "beta[1]" ...
#>  $ model_name         : chr "logistic_model"
#>  $ adapt_engaged      : num 1
#>  $ stan_variable_dims :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" ...
# }

See also

Examples