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Draw samples of generated quantities from a Stan model

stanmodel-method-gqs.Rd

Draw samples from the generated quantities block of a stanmodel.

Usage

# S4 method for class 'stanmodel'
gqs(object, data = list(), draws, 
    seed = sample.int(.Machine$integer.max, size = 1L))

Methods

object

signature(object = "stanmodel") Evaluate the generated quantities block of a Stan program by supplying data and the draws output from a previous Stan program.

Arguments

object

An object of class stanmodel.

data

A named list or environment providing the data for the model or a character vector for all the names of objects used as data. See the Passing data to Stan section in stan.

draws

A matrix of posterior draws, typically created by calling as.matrix on a stanfit.

seed

The seed for random number generation. The default is generated from 1 to the maximum integer supported by R on the machine. When a seed is specified by a number, as.integer will be applied to it. If as.integer produces NA, the seed is generated randomly. The seed can also be specified as a character string of digits, such as "12345", which is converted to integer.

Value

An object of S4 class stanmodel representing the fitted results.

See also

stanmodel, stanfit, stan

Examples

# \dontrun{
m <- stan_model(model_code = 'parameters {real y;} model {y ~ normal(0,1);}')
f <- sampling(m, iter = 300)
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.02 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 1: Iteration:  30 / 300 [ 10%]  (Warmup)
#> Chain 1: Iteration:  60 / 300 [ 20%]  (Warmup)
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#> Chain 1: Iteration: 120 / 300 [ 40%]  (Warmup)
#> Chain 1: Iteration: 150 / 300 [ 50%]  (Warmup)
#> Chain 1: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 1: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 1: Iteration: 210 / 300 [ 70%]  (Sampling)
#> Chain 1: Iteration: 240 / 300 [ 80%]  (Sampling)
#> Chain 1: Iteration: 270 / 300 [ 90%]  (Sampling)
#> Chain 1: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 1:                0 seconds (Sampling)
#> Chain 1:                0 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 2: Iteration:  30 / 300 [ 10%]  (Warmup)
#> Chain 2: Iteration:  60 / 300 [ 20%]  (Warmup)
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#> Chain 2: Iteration: 120 / 300 [ 40%]  (Warmup)
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#> Chain 2: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 2: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 2: Iteration: 210 / 300 [ 70%]  (Sampling)
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#> Chain 2: Iteration: 270 / 300 [ 90%]  (Sampling)
#> Chain 2: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 2:                0 seconds (Sampling)
#> Chain 2:                0 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 0 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 3: Iteration:  30 / 300 [ 10%]  (Warmup)
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#> Chain 3: Iteration: 120 / 300 [ 40%]  (Warmup)
#> Chain 3: Iteration: 150 / 300 [ 50%]  (Warmup)
#> Chain 3: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 3: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 3: Iteration: 210 / 300 [ 70%]  (Sampling)
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#> Chain 3: Iteration: 270 / 300 [ 90%]  (Sampling)
#> Chain 3: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 3:                0 seconds (Sampling)
#> Chain 3:                0 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 0 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:   1 / 300 [  0%]  (Warmup)
#> Chain 4: Iteration:  30 / 300 [ 10%]  (Warmup)
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#> Chain 4: Iteration: 120 / 300 [ 40%]  (Warmup)
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#> Chain 4: Iteration: 151 / 300 [ 50%]  (Sampling)
#> Chain 4: Iteration: 180 / 300 [ 60%]  (Sampling)
#> Chain 4: Iteration: 210 / 300 [ 70%]  (Sampling)
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#> Chain 4: Iteration: 300 / 300 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0 seconds (Warm-up)
#> Chain 4:                0 seconds (Sampling)
#> Chain 4:                0 seconds (Total)
#> Chain 4: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
mc <-
'
parameters {real y;}
generated quantities {real y_rep = normal_rng(y, 1);}
'
m2 <- stan_model(model_code = mc)
f2 <- gqs(m2, draws = as.matrix(f))
f2
#> Inference for Stan model: anon_model.
#> 1 chains, each with iter=600; warmup=0; thin=1; 
#> post-warmup draws per chain=600, total post-warmup draws=600.
#> 
#>       mean se_mean   sd  2.5%   25%  50%  75% 97.5% n_eff Rhat
#> y_rep 0.15    0.08 1.43 -2.57 -0.79 0.08 1.07  2.83   352    1
#> 
#> Samples were drawn using  at Wed Dec 10 00:32:43 2025.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at 
#> convergence, Rhat=1).
# }