The $sample()
method of a CmdStanModel
object runs Stan's
main Markov chain Monte Carlo algorithm.
Any argument left as NULL
will default to the default value used by the
installed version of CmdStan. See the
CmdStan User’s Guide
for more details.
After model fitting any diagnostics specified via the diagnostics
argument will be checked and warnings will be printed if warranted.
sample( data = NULL, seed = NULL, refresh = NULL, init = NULL, save_latent_dynamics = FALSE, output_dir = NULL, output_basename = NULL, sig_figs = NULL, chains = 4, parallel_chains = getOption("mc.cores", 1), chain_ids = seq_len(chains), threads_per_chain = NULL, opencl_ids = NULL, iter_warmup = NULL, iter_sampling = NULL, save_warmup = FALSE, thin = NULL, max_treedepth = NULL, adapt_engaged = TRUE, adapt_delta = NULL, step_size = NULL, metric = NULL, metric_file = NULL, inv_metric = NULL, init_buffer = NULL, term_buffer = NULL, window = NULL, fixed_param = FALSE, show_messages = TRUE, diagnostics = c("divergences", "treedepth", "ebfmi"), cores = NULL, num_cores = NULL, num_chains = NULL, num_warmup = NULL, num_samples = NULL, validate_csv = NULL, save_extra_diagnostics = NULL, max_depth = NULL, stepsize = NULL )
data  (multiple options) The data to use for the variables specified in the data block of the Stan program. One of the following:


seed  (positive integer(s)) A seed for the (P)RNG to pass to CmdStan.
In the case of multichain sampling the single 
refresh  (nonnegative integer) The number of iterations between
printed screen updates. If 
init  (multiple options) The initialization method to use for the variables declared in the parameters block of the Stan program. One of the following:

save_latent_dynamics  (logical) Should auxiliary diagnostic information
about the latent dynamics be written to temporary diagnostic CSV files?
This argument replaces CmdStan's 
output_dir  (string) A path to a directory where CmdStan should write
its output CSV files. For interactive use this can typically be left at

output_basename  (string) A string to use as a prefix for the names of
the output CSV files of CmdStan. If 
sig_figs  (positive integer) The number of significant figures used
when storing the output values. By default, CmdStan represent the output
values with 6 significant figures. The upper limit for 
chains  (positive integer) The number of Markov chains to run. The default is 4. 
parallel_chains  (positive integer) The maximum number of MCMC chains
to run in parallel. If 
chain_ids  (integer vector) A vector of chain IDs. Must contain as many
unique positive integers as the number of chains. If not set, the default
chain IDs are used (integers starting from 
threads_per_chain  (positive integer) If the model was
compiled with threading support, the number of
threads to use in parallelized sections within an MCMC chain (e.g., when
using the Stan functions 
opencl_ids  (integer vector of length 2) The platform and
device IDs of the OpenCL device to use for fitting. The model must
be compiled with 
iter_warmup  (positive integer) The number of warmup iterations to run
per chain. Note: in the CmdStan User's Guide this is referred to as

iter_sampling  (positive integer) The number of postwarmup iterations
to run per chain. Note: in the CmdStan User's Guide this is referred to as

save_warmup  (logical) Should warmup iterations be saved? The default
is 
thin  (positive integer) The period between saved samples. This should typically be left at its default (no thinning) unless memory is a problem. 
max_treedepth  (positive integer) The maximum allowed tree depth for the NUTS engine. See the Tree Depth section of the CmdStan User's Guide for more details. 
adapt_engaged  (logical) Do warmup adaptation? The default is 
adapt_delta  (real in 
step_size  (positive real) The initial step size for the discrete approximation to continuous Hamiltonian dynamics. This is further tuned during warmup. 
metric  (string) One of 
metric_file  (character vector) The paths to JSON or
Rdump files (one per chain) compatible with CmdStan that contain
precomputed inverse metrics. The 
inv_metric  (vector, matrix) A vector (if 
init_buffer  (nonnegative integer) Width of initial fast timestep adaptation interval during warmup. 
term_buffer  (nonnegative integer) Width of final fast timestep adaptation interval during warmup. 
window  (nonnegative integer) Initial width of slow timestep/metric adaptation interval. 
fixed_param  (logical) When 
show_messages  (logical) When 
diagnostics  (character vector) The diagnostics to automatically check
and warn about after sampling. Setting this to an empty string These diagnostics are also available after fitting. The
Diagnostics like Rhat and effective sample size are not currently
available via the 
cores, num_cores, num_chains, num_warmup, num_samples, save_extra_diagnostics, max_depth, stepsize, validate_csv  Deprecated and will be removed in a future release. 
A CmdStanMCMC
object.
The CmdStanR website (mcstan.org/cmdstanr) for online documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mcstan.org/users/documentation
CmdStan User’s Guide: mcstan.org/docs/cmdstanguide
Other CmdStanModel methods:
modelmethodcheck_syntax
,
modelmethodcompile
,
modelmethoddiagnose
,
modelmethodformat
,
modelmethodgeneratequantities
,
modelmethodoptimize
,
modelmethodsample_mpi
,
modelmethodvariables
,
modelmethodvariational
# \dontrun{ library(cmdstanr) library(posterior) library(bayesplot) color_scheme_set("brightblue") # Set path to CmdStan # (Note: if you installed CmdStan via install_cmdstan() with default settings # then setting the path is unnecessary but the default below should still work. # Otherwise use the `path` argument to specify the location of your # CmdStan installation.) set_cmdstan_path(path = NULL)#># Create a CmdStanModel object from a Stan program, # here using the example model that comes with CmdStan file < file.path(cmdstan_path(), "examples/bernoulli/bernoulli.stan") mod < cmdstan_model(file) mod$print()#> data { #> int<lower=0> N; #> array[N] int<lower=0,upper=1> y; // or int<lower=0,upper=1> y[N]; #> } #> parameters { #> real<lower=0,upper=1> theta; #> } #> model { #> theta ~ beta(1,1); // uniform prior on interval 0,1 #> y ~ bernoulli(theta); #> }# Data as a named list (like RStan) stan_data < list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1)) # Run MCMC using the 'sample' method fit_mcmc < mod$sample( data = stan_data, seed = 123, chains = 2, parallel_chains = 2 )#> Running MCMC with 2 parallel chains... #> #> Chain 1 Iteration: 1 / 2000 [ 0%] (Warmup) #> Chain 1 Iteration: 100 / 2000 [ 5%] (Warmup) #> Chain 1 Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 1 Iteration: 300 / 2000 [ 15%] (Warmup) #> Chain 1 Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 1 Iteration: 500 / 2000 [ 25%] (Warmup) #> Chain 1 Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 1 Iteration: 700 / 2000 [ 35%] (Warmup) #> Chain 1 Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 1 Iteration: 900 / 2000 [ 45%] (Warmup) #> Chain 1 Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 1 Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 1 Iteration: 1100 / 2000 [ 55%] (Sampling) #> Chain 1 Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 1 Iteration: 1300 / 2000 [ 65%] (Sampling) #> Chain 1 Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 1 Iteration: 1500 / 2000 [ 75%] (Sampling) #> Chain 1 Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 1 Iteration: 1700 / 2000 [ 85%] (Sampling) #> Chain 1 Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 1 Iteration: 1900 / 2000 [ 95%] (Sampling) #> Chain 1 Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 2 Iteration: 1 / 2000 [ 0%] (Warmup) #> Chain 2 Iteration: 100 / 2000 [ 5%] (Warmup) #> Chain 2 Iteration: 200 / 2000 [ 10%] (Warmup) #> Chain 2 Iteration: 300 / 2000 [ 15%] (Warmup) #> Chain 2 Iteration: 400 / 2000 [ 20%] (Warmup) #> Chain 2 Iteration: 500 / 2000 [ 25%] (Warmup) #> Chain 2 Iteration: 600 / 2000 [ 30%] (Warmup) #> Chain 2 Iteration: 700 / 2000 [ 35%] (Warmup) #> Chain 2 Iteration: 800 / 2000 [ 40%] (Warmup) #> Chain 2 Iteration: 900 / 2000 [ 45%] (Warmup) #> Chain 2 Iteration: 1000 / 2000 [ 50%] (Warmup) #> Chain 2 Iteration: 1001 / 2000 [ 50%] (Sampling) #> Chain 2 Iteration: 1100 / 2000 [ 55%] (Sampling) #> Chain 2 Iteration: 1200 / 2000 [ 60%] (Sampling) #> Chain 2 Iteration: 1300 / 2000 [ 65%] (Sampling) #> Chain 2 Iteration: 1400 / 2000 [ 70%] (Sampling) #> Chain 2 Iteration: 1500 / 2000 [ 75%] (Sampling) #> Chain 2 Iteration: 1600 / 2000 [ 80%] (Sampling) #> Chain 2 Iteration: 1700 / 2000 [ 85%] (Sampling) #> Chain 2 Iteration: 1800 / 2000 [ 90%] (Sampling) #> Chain 2 Iteration: 1900 / 2000 [ 95%] (Sampling) #> Chain 2 Iteration: 2000 / 2000 [100%] (Sampling) #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.2 seconds. #># Use 'posterior' package for summaries fit_mcmc$summary()#> # A tibble: 2 × 10 #> variable mean median sd mad q5 q95 rhat ess_bulk ess_tail #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ 7.30 7.03 0.721 0.380 8.82 6.75 1.00 902. 1006. #> 2 theta 0.247 0.233 0.122 0.129 0.0786 0.470 1.00 762. 712.#> # A draws_array: 1000 iterations, 2 chains, and 2 variables #> , , variable = lp__ #> #> chain #> iteration 1 2 #> 1 6.8 6.8 #> 2 6.9 6.8 #> 3 7.0 7.0 #> 4 6.9 7.1 #> 5 6.7 7.0 #> #> , , variable = theta #> #> chain #> iteration 1 2 #> 1 0.28 0.21 #> 2 0.19 0.20 #> 3 0.16 0.17 #> 4 0.20 0.36 #> 5 0.25 0.34 #> #> # ... with 995 more iterations#> # A draws_df: 1000 iterations, 2 chains, and 2 variables #> lp__ theta #> 1 6.8 0.28 #> 2 6.9 0.19 #> 3 7.0 0.16 #> 4 6.9 0.20 #> 5 6.7 0.25 #> 6 7.1 0.36 #> 7 9.0 0.55 #> 8 7.2 0.15 #> 9 6.8 0.23 #> 10 7.5 0.42 #> # ... with 1990 more draws #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}#># Call CmdStan's diagnose and stansummary utilities fit_mcmc$cmdstan_diagnose()#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpmzUYEz/bernoulli202203181226105e2b0.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpmzUYEz/bernoulli202203181226205e2b0.csv #> #> Checking sampler transitions treedepth. #> Treedepth satisfactory for all transitions. #> #> Checking sampler transitions for divergences. #> No divergent transitions found. #> #> Checking EBFMI  sampler transitions HMC potential energy. #> EBFMI satisfactory. #> #> Effective sample size satisfactory. #> #> Split Rhat values satisfactory all parameters. #> #> Processing complete, no problems detected.fit_mcmc$cmdstan_summary()#> Inference for Stan model: bernoulli_model #> 2 chains: each with iter=(1000,1000); warmup=(0,0); thin=(1,1); 2000 iterations saved. #> #> Warmup took (0.0050, 0.0050) seconds, 0.010 seconds total #> Sampling took (0.015, 0.014) seconds, 0.029 seconds total #> #> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat #> #> lp__ 7.3 2.6e02 0.72 8.8 7.0 6.8 781 26932 1.0 #> accept_stat__ 0.92 8.3e03 0.13 0.64 0.97 1.0 2.3e+02 8.1e+03 1.0e+00 #> stepsize__ 0.95 7.9e02 0.079 0.87 1.0 1.0 1.0e+00 3.5e+01 2.0e+13 #> treedepth__ 1.4 1.1e02 0.48 1.0 1.0 2.0 1.9e+03 6.5e+04 1.0e+00 #> n_leapfrog__ 2.5 1.4e01 1.3 1.0 3.0 3.0 8.9e+01 3.1e+03 1.0e+00 #> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan #> energy__ 7.8 3.6e02 1.00 6.8 7.5 9.6 7.7e+02 2.7e+04 1.0e+00 #> #> theta 0.25 4.3e03 0.12 0.079 0.23 0.47 796 27460 1.0 #> #> Samples were drawn using hmc with nuts. #> For each parameter, N_Eff is a crude measure of effective sample size, #> and R_hat is the potential scale reduction factor on split chains (at #> convergence, R_hat=1).# For models fit using MCMC, if you like working with RStan's stanfit objects # then you can create one with rstan::read_stan_csv() # stanfit < rstan::read_stan_csv(fit_mcmc$output_files()) # Run 'optimize' method to get a point estimate (default is Stan's LBFGS algorithm) # and also demonstrate specifying data as a path to a file instead of a list my_data_file < file.path(cmdstan_path(), "examples/bernoulli/bernoulli.data.json") fit_optim < mod$optimize(data = my_data_file, seed = 123)#> Initial log joint probability = 9.51104 #> Iter log prob dx grad alpha alpha0 # evals Notes #> 6 5.00402 0.000103557 2.55661e07 1 1 9 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.1 seconds.fit_optim$summary()#> # A tibble: 2 × 2 #> variable estimate #> <chr> <dbl> #> 1 lp__ 5.00 #> 2 theta 0.2# Run 'variational' method to approximate the posterior (default is meanfield ADVI) fit_vb < mod$variational(data = stan_data, seed = 123)#>  #> EXPERIMENTAL ALGORITHM: #> This procedure has not been thoroughly tested and may be unstable #> or buggy. The interface is subject to change. #>  #> Gradient evaluation took 8e06 seconds #> 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds. #> Adjust your expectations accordingly! #> Begin eta adaptation. #> Iteration: 1 / 250 [ 0%] (Adaptation) #> Iteration: 50 / 250 [ 20%] (Adaptation) #> Iteration: 100 / 250 [ 40%] (Adaptation) #> Iteration: 150 / 250 [ 60%] (Adaptation) #> Iteration: 200 / 250 [ 80%] (Adaptation) #> Success! Found best value [eta = 1] earlier than expected. #> Begin stochastic gradient ascent. #> iter ELBO delta_ELBO_mean delta_ELBO_med notes #> 100 6.262 1.000 1.000 #> 200 6.263 0.500 1.000 #> 300 6.307 0.336 0.007 MEDIAN ELBO CONVERGED #> Drawing a sample of size 1000 from the approximate posterior... #> COMPLETED. #> Finished in 0.1 seconds.fit_vb$summary()#> # A tibble: 3 × 7 #> variable mean median sd mad q5 q95 #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 lp__ 7.18 6.94 0.588 0.259 8.36 6.75 #> 2 lp_approx__ 0.515 0.221 0.692 0.303 2.06 0.00257 #> 3 theta 0.263 0.246 0.115 0.113 0.106 0.481#># Specifying initial values as a function fit_mcmc_w_init_fun < mod$sample( data = stan_data, seed = 123, chains = 2, refresh = 0, init = function() list(theta = runif(1)) )#> Running MCMC with 2 sequential chains... #> #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.3 seconds. #>fit_mcmc_w_init_fun_2 < mod$sample( data = stan_data, seed = 123, chains = 2, refresh = 0, init = function(chain_id) { # silly but demonstrates optional use of chain_id list(theta = 1 / (chain_id + 1)) } )#> Running MCMC with 2 sequential chains... #> #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.3 seconds. #>fit_mcmc_w_init_fun_2$init()#> [[1]] #> [[1]]$theta #> [1] 0.5 #> #> #> [[2]] #> [[2]]$theta #> [1] 0.3333333 #> #># Specifying initial values as a list of lists fit_mcmc_w_init_list < mod$sample( data = stan_data, seed = 123, chains = 2, refresh = 0, init = list( list(theta = 0.75), # chain 1 list(theta = 0.25) # chain 2 ) )#> Running MCMC with 2 sequential chains... #> #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> #> Both chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.3 seconds. #>fit_optim_w_init_list < mod$optimize( data = stan_data, seed = 123, init = list( list(theta = 0.75) ) )#> Initial log joint probability = 11.6657 #> Iter log prob dx grad alpha alpha0 # evals Notes #> 6 5.00402 0.000237915 9.55309e07 1 1 9 #> Optimization terminated normally: #> Convergence detected: relative gradient magnitude is below tolerance #> Finished in 0.3 seconds.fit_optim_w_init_list$init()#> [[1]] #> [[1]]$theta #> [1] 0.75 #> #># }