A CmdStanGQ object is the fitted model object returned by the $generate_quantities() method of a CmdStanModel object.

Methods

CmdStanGQ objects have the following associated methods, all of which have their own (linked) documentation pages.

Extract contents of generated quantities object

MethodDescription
$draws()Return the generated quantities as a draws_array.
$metadata()Return a list of metadata gathered from the CmdStan CSV files.

Summarize inferences

MethodDescription
$summary()Run posterior::summarise_draws().

Save fitted model object and temporary files

MethodDescription
$save_object()Save fitted model object to a file.
$save_output_files()Save output CSV files to a specified location.
$save_data_file()Save JSON data file to a specified location.

Report run times, console output, return codes

MethodDescription
$time()Report the total run time.
$output()Return the stdout and stderr of all chains or pretty print the output for a single chain.
$return_codes()Return the return codes from the CmdStan runs.

See also

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

The Stan and CmdStan documentation:

Other fitted model objects: CmdStanMCMC, CmdStanMLE, CmdStanVB

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)
#> This is posterior version 0.1.3
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'}
# }