R/model.R
modelmethodgeneratequantities.Rd
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, sig_figs = NULL, parallel_chains = getOption("mc.cores", 1), threads_per_chain = NULL )
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

seed  (positive integer) A seed for the (P)RNG to pass to CmdStan. 
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

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 
parallel_chains  (positive integer) The maximum number of MCMC chains
to run in parallel. If 
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 
A CmdStanGQ
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
,
modelmethodoptimize
,
modelmethodsample_mpi
,
modelmethodsample
,
modelmethodvariational
# \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)#>data < list(N = 10, y = c(1,1,0,0,0,1,0,1,0,0)) fit_mcmc < mod_mcmc$sample(data = data, seed = 123, refresh = 0)#> Running MCMC with 4 sequential chains... #> #> Chain 1 finished in 0.0 seconds. #> Chain 2 finished in 0.0 seconds. #> Chain 3 finished in 0.0 seconds. #> Chain 4 finished in 0.0 seconds. #> #> All 4 chains finished successfully. #> Mean chain execution time: 0.0 seconds. #> Total execution time: 0.6 seconds.# stan program for standalone generated quantities # (could keep model block, but not necessary so removing it) gq_program < write_stan_file( "data { int<lower=0> N; int<lower=0,upper=1> y[N]; } parameters { real<lower=0,upper=1> theta; } generated quantities { int y_rep[N] = bernoulli_rng(rep_vector(theta, N)); }" ) mod_gq < cmdstan_model(gq_program)#>fit_gq < mod_gq$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: 0.5 seconds.#> '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]" ...#> # 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'}# }