A CmdStanGQ object is the fitted model object returned by the
$generate_quantities() method of a
CmdStanModel object.
CmdStanGQ objects have the following associated methods,
all of which have their own (linked) documentation pages.
| Method | Description |
$draws() | Return the generated quantities as a draws_array. |
$metadata() | Return a list of metadata gathered from the CmdStan CSV files. |
$code() | Return Stan code as a character vector. |
| Method | Description |
$summary() | Run posterior::summarise_draws(). |
| Method | Description |
$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. |
| Method | Description |
$time() | Report the total run time. |
$output() | Return the stdout and stderr of all chains or pretty print the output for a single chain. |
$return_codes() | Return the return codes from the CmdStan runs. |
The CmdStanR website (mc-stan.org/cmdstanr) for online documentation and tutorials.
The Stan and CmdStan documentation:
Stan documentation: mc-stan.org/users/documentation
CmdStan User’s Guide: mc-stan.org/docs/cmdstan-guide
Other fitted model objects:
CmdStanDiagnose,
CmdStanLaplace,
CmdStanMCMC,
CmdStanMLE,
CmdStanPathfinder,
CmdStanVB
# \dontrun{
# first fit a model using MCMC
mcmc_program <- write_stan_file(
"data {
int<lower=0> N;
array[N] int<lower=0,upper=1> y;
}
parameters {
real<lower=0,upper=1> theta;
}
model {
y ~ bernoulli(theta);
}"
)
mod_mcmc <- cmdstan_model(mcmc_program)
data <- list(N = 10, y = c(1,1,0,0,0,1,0,1,0,0))
fit_mcmc <- mod_mcmc$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: 0.9 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;
array[N] int<lower=0,upper=1> y;
}
parameters {
real<lower=0,upper=1> theta;
}
generated quantities {
array[N] int y_rep = bernoulli_rng(rep_vector(theta, N));
}"
)
mod_gq <- cmdstan_model(gq_program)
fit_gq <- mod_gq$generate_quantities(fit_mcmc, data = data, seed = 123)
#> Running standalone generated quantities after 4 MCMC chains, 1 chain at a time ...
#>
#> Chain 1 finished in 0.0 seconds.
#> Chain 2 finished in 0.0 seconds.
#> Chain 3 finished in 0.0 seconds.
#> Chain 4 finished in 0.0 seconds.
#>
#> All 4 chains finished successfully.
#> Mean chain execution time: 0.0 seconds.
#> Total execution time: 0.6 seconds.
str(fit_gq$draws())
#> 'draws_array' int [1:1000, 1:4, 1:10] 0 0 0 1 1 0 1 1 0 1 ...
#> - 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 1.6.0
#>
#> Attaching package: ‘posterior’
#> The following objects are masked from ‘package:stats’:
#>
#> mad, sd, var
#> The following objects are masked from ‘package:base’:
#>
#> %in%, match
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 1 1 1
#> 2 0 0 0 0 1 1 0 0
#> 3 0 0 0 1 0 0 1 1
#> 4 1 1 0 0 0 0 1 0
#> 5 1 0 1 0 1 0 1 0
#> 6 0 0 0 1 1 0 0 0
#> 7 1 1 0 1 1 1 0 0
#> 8 1 1 1 1 1 0 1 1
#> 9 0 1 0 1 0 1 1 0
#> 10 1 1 1 1 1 1 1 1
#> # ... with 3990 more draws, and 2 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# }