The $generate_quantities() method of a CmdStanModel object runs Stan's standalone generated quantities to obtain generated quantities based on previously fitted parameters.

generate_quantities(
  fitted_params,
  data = NULL,
  seed = NULL,
  output_dir = NULL,
  output_basename = NULL,
  sig_figs = NULL,
  parallel_chains = getOption("mc.cores", 1),
  threads_per_chain = NULL,
  opencl_ids = NULL
)

Arguments

fitted_params

(multiple options) The parameter draws to use. One of the following:

data

(multiple options) The data to use for the variables specified in the data block of the Stan program. One of the following:

  • A named list of R objects (like for RStan). Internally this list is then written to JSON for CmdStan using write_stan_json().

  • A path to a data file compatible with CmdStan (JSON or R dump). See the appendices in the CmdStan manual for details on using these formats.

  • NULL or an empty list if the Stan program has no data block.

seed

(positive integer(s)) A seed for the (P)RNG to pass to CmdStan. In the case of multi-chain sampling the single seed will automatically be augmented by the the run (chain) ID so that each chain uses a different seed. The exception is the transformed data block, which defaults to using same seed for all chains so that the same data is generated for all chains if RNG functions are used. The only time seed should be specified as a vector (one element per chain) is if RNG functions are used in transformed data and the goal is to generate different data for each chain.

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 NULL (temporary directory) since CmdStanR makes the CmdStan output (posterior draws and diagnostics) available in R via methods of the fitted model objects. The behavior of output_dir is as follows:

  • If NULL (the default), then the CSV files are written to a temporary directory and only saved permanently if the user calls one of the $save_* methods of the fitted model object (e.g., $save_output_files()). These temporary files are removed when the fitted model object is garbage collected (manually or automatically).

  • If a path, then the files are created in output_dir with names corresponding to the defaults used by $save_output_files().

output_basename

(string) A string to use as a prefix for the names of the output CSV files of CmdStan.

  • If NULL (the default), the basename of the output CSV files will be comprised from the model name, timestamp and 5 random characters.

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 sig_figs is 18. Increasing this value will result in larger output CSV files and thus an increased usage of disk space.

parallel_chains

(positive integer) The maximum number of MCMC chains to run in parallel. If parallel_chains is not specified then the default is to look for the option "mc.cores", which can be set for an entire R session by options(mc.cores=value). If the "mc.cores" option has not been set then the default is 1.

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 reduce_sum() or map_rect()). This is in contrast with parallel_chains, which specifies the number of chains to run in parallel. The actual number of CPU cores used use is parallel_chains*threads_per_chain. For an example of using threading see the Stan case study Reduce Sum: A Minimal Example.

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 cpp_options = list(stan_opencl = TRUE) for this argument to have an effect.

Value

A CmdStanGQ object.

See also

The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.

The Stan and CmdStan documentation:

Other CmdStanModel methods: model-method-check_syntax, model-method-compile, model-method-optimize, model-method-sample_mpi, model-method-sample, model-method-variational

Examples

# \dontrun{ # first fit a model using MCMC mcmc_program <- write_stan_file( "data { int<lower=0> N; int<lower=0,upper=1> y[N]; } parameters { real<lower=0,upper=1> theta; } model { y ~ bernoulli(theta); }" ) mod_mcmc <- cmdstan_model(mcmc_program)
#> Compiling Stan program...
#> - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | /
data <- list(N = 10, y = c(1,1,0,0,0,1,0,1,0,0)) fit_mcmc <- mod_mcmc$sample(data = data, seed = 123, refresh = 0)
#> Running MCMC with 4 sequential chains... #> #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> Chain 3 finished in 0.0 seconds. #> Chain 4 finished in 0.0 seconds. #> #> All 4 chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.7 seconds.
# stan program for standalone generated quantities # (could keep model block, but not necessary so removing it) gq_program <- write_stan_file( "data { int<lower=0> N; int<lower=0,upper=1> y[N]; } parameters { real<lower=0,upper=1> theta; } generated quantities { int y_rep[N] = bernoulli_rng(rep_vector(theta, N)); }" ) mod_gq <- cmdstan_model(gq_program)
#> Compiling Stan program...
#> - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | /
fit_gq <- mod_gq$generate_quantities(fit_mcmc, data = data, seed = 123)
#> Running standalone generated quantities after 4 MCMC chains, 1 chain at a time ... #> #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> Chain 3 finished in 0.0 seconds. #> Chain 4 finished in 0.0 seconds. #> #> All 4 chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.6 seconds.
str(fit_gq$draws())
#> 'draws_array' int [1:1000, 1:4, 1:10] 0 0 0 0 1 0 1 0 0 0 ... #> - attr(*, "dimnames")=List of 3 #> ..$ iteration: chr [1:1000] "1" "2" "3" "4" ... #> ..$ chain : chr [1:4] "1" "2" "3" "4" #> ..$ variable : chr [1:10] "y_rep[1]" "y_rep[2]" "y_rep[3]" "y_rep[4]" ...
library(posterior) as_draws_df(fit_gq$draws())
#> # A draws_df: 1000 iterations, 4 chains, and 10 variables #> y_rep[1] y_rep[2] y_rep[3] y_rep[4] y_rep[5] y_rep[6] y_rep[7] y_rep[8] #> 1 0 0 0 0 0 0 0 0 #> 2 0 1 1 1 1 1 1 1 #> 3 0 0 0 0 0 1 1 1 #> 4 0 0 0 0 0 1 0 1 #> 5 1 1 1 1 1 0 1 0 #> 6 0 0 0 0 1 0 1 1 #> 7 1 0 0 0 1 1 1 0 #> 8 0 1 0 0 0 1 0 1 #> 9 0 1 0 1 1 1 1 1 #> 10 0 1 0 1 0 0 0 0 #> # ... with 3990 more draws, and 2 more variables #> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# }