For MCMC, the $output() method returns the stdout and stderr of all chains as a list of character vectors if id=NULL. If the id argument is specified it instead pretty prints the console output for a single chain.

For optimization and variational inference $output() just pretty prints the console output.

output(id = NULL)

Arguments

id

(integer) The chain id. Ignored if the model was not fit using MCMC.

See also

Examples

# \dontrun{ fit_mcmc <- cmdstanr_example("logistic", method = "sample")
#> Model executable is up to date!
fit_mcmc$output(1)
#> #> method = sample (Default) #> sample #> num_samples = 1000 (Default) #> num_warmup = 1000 (Default) #> save_warmup = 0 (Default) #> thin = 1 (Default) #> adapt #> engaged = 1 (Default) #> gamma = 0.050000000000000003 (Default) #> delta = 0.80000000000000004 (Default) #> kappa = 0.75 (Default) #> t0 = 10 (Default) #> init_buffer = 75 (Default) #> term_buffer = 50 (Default) #> window = 25 (Default) #> algorithm = hmc (Default) #> hmc #> engine = nuts (Default) #> nuts #> max_depth = 10 (Default) #> metric = diag_e (Default) #> metric_file = (Default) #> stepsize = 1 (Default) #> stepsize_jitter = 0 (Default) #> id = 1 #> data #> file = /private/var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpOUu4d5/temp_libpath1f1e474979aa/cmdstanr/logistic.data.json #> init = 2 (Default) #> random #> seed = 984374382 #> output #> file = /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpGraDrG/logistic-202012171338-1-74a0f0.csv #> diagnostic_file = (Default) #> refresh = 100 (Default) #> sig_figs = -1 (Default) #> #> #> Gradient evaluation took 2.8e-05 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds. #> Adjust your expectations accordingly! #> #> #> Iteration: 1 / 2000 [ 0%] (Warmup) #> Iteration: 100 / 2000 [ 5%] (Warmup) #> Iteration: 200 / 2000 [ 10%] (Warmup) #> Iteration: 300 / 2000 [ 15%] (Warmup) #> Iteration: 400 / 2000 [ 20%] (Warmup) #> Iteration: 500 / 2000 [ 25%] (Warmup) #> Iteration: 600 / 2000 [ 30%] (Warmup) #> Iteration: 700 / 2000 [ 35%] (Warmup) #> Iteration: 800 / 2000 [ 40%] (Warmup) #> Iteration: 900 / 2000 [ 45%] (Warmup) #> Iteration: 1000 / 2000 [ 50%] (Warmup) #> Iteration: 1001 / 2000 [ 50%] (Sampling) #> Iteration: 1100 / 2000 [ 55%] (Sampling) #> Iteration: 1200 / 2000 [ 60%] (Sampling) #> Iteration: 1300 / 2000 [ 65%] (Sampling) #> Iteration: 1400 / 2000 [ 70%] (Sampling) #> Iteration: 1500 / 2000 [ 75%] (Sampling) #> Iteration: 1600 / 2000 [ 80%] (Sampling) #> Iteration: 1700 / 2000 [ 85%] (Sampling) #> Iteration: 1800 / 2000 [ 90%] (Sampling) #> Iteration: 1900 / 2000 [ 95%] (Sampling) #> Iteration: 2000 / 2000 [100%] (Sampling) #> #> Elapsed Time: 0.035 seconds (Warm-up) #> 0.137 seconds (Sampling) #> 0.172 seconds (Total)
out <- fit_mcmc$output() str(out)
#> List of 4 #> $ : chr [1:70] "" "method = sample (Default)" " sample" " num_samples = 1000 (Default)" ... #> $ : chr [1:70] "" "method = sample (Default)" " sample" " num_samples = 1000 (Default)" ... #> $ : chr [1:70] "" "method = sample (Default)" " sample" " num_samples = 1000 (Default)" ... #> $ : chr [1:70] "" "method = sample (Default)" " sample" " num_samples = 1000 (Default)" ...
fit_mle <- cmdstanr_example("logistic", method = "optimize")
#> Model executable is up to date!
fit_mle$output()
#> #> method = optimize #> optimize #> algorithm = lbfgs (Default) #> lbfgs #> init_alpha = 0.001 (Default) #> tol_obj = 9.9999999999999998e-13 (Default) #> tol_rel_obj = 10000 (Default) #> tol_grad = 1e-08 (Default) #> tol_rel_grad = 10000000 (Default) #> tol_param = 1e-08 (Default) #> history_size = 5 (Default) #> iter = 2000 (Default) #> save_iterations = 0 (Default) #> id = 1 #> data #> file = /private/var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpOUu4d5/temp_libpath1f1e474979aa/cmdstanr/logistic.data.json #> init = 2 (Default) #> random #> seed = 432754710 #> output #> file = /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpGraDrG/logistic-202012171338-1-30f448.csv #> diagnostic_file = (Default) #> refresh = 100 (Default) #> sig_figs = -1 (Default) #> #> Initial log joint probability = -103.426 #> Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes #> 7 -63.9218 0.000113707 0.000210786 1 1 9 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance
fit_vb <- cmdstanr_example("logistic", method = "variational")
#> Model executable is up to date!
fit_vb$output()
#> #> method = variational #> variational #> algorithm = meanfield (Default) #> meanfield #> iter = 10000 (Default) #> grad_samples = 1 (Default) #> elbo_samples = 100 (Default) #> eta = 1 (Default) #> adapt #> engaged = 1 (Default) #> iter = 50 (Default) #> tol_rel_obj = 0.01 (Default) #> eval_elbo = 100 (Default) #> output_samples = 1000 (Default) #> id = 1 #> data #> file = /private/var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpOUu4d5/temp_libpath1f1e474979aa/cmdstanr/logistic.data.json #> init = 2 (Default) #> random #> seed = 1062574477 #> output #> file = /var/folders/h6/14xy_35x4wd2tz542dn0qhtc0000gn/T/RtmpGraDrG/logistic-202012171338-1-7dd145.csv #> diagnostic_file = (Default) #> refresh = 100 (Default) #> sig_figs = -1 (Default) #> #> ------------------------------------------------------------ #> EXPERIMENTAL ALGORITHM: #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #> ------------------------------------------------------------ #> #> #> #> Gradient evaluation took 2.8e-05 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds. #> Adjust your expectations accordingly! #> #> #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) #> Iteration: 50 / 250 [ 20%] (Adaptation) #> Iteration: 100 / 250 [ 40%] (Adaptation) #> Iteration: 150 / 250 [ 60%] (Adaptation) #> Iteration: 200 / 250 [ 80%] (Adaptation) #> Success! Found best value [eta = 1] earlier than expected. #> #> Begin stochastic gradient ascent. #> iter ELBO delta_ELBO_mean delta_ELBO_med notes #> 100 -66.324 1.000 1.000 #> 200 -66.614 0.502 1.000 #> 300 -66.607 0.335 0.004 MEDIAN ELBO CONVERGED #> #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED.
# }