Sample from Stan model

Method to sample from compiled Stan model and return a CmdStanMCMC object with draws.

Usage

mcmc_sample(x, ...)

# S4 method for class 'ANY'
mcmc_sample(x, ...)

# S4 method for class 'Analysis'
mcmc_sample(
  x,
  iter_warmup = 1000L,
  iter_sampling = 10000L,
  chains = 4L,
  verbose = FALSE,
  ...
)

# S4 method for class 'Simulation'
mcmc_sample(
  x,
  posterior_quantiles = c(0.025, 0.975),
  iter_warmup = 1000L,
  iter_sampling = 10000L,
  chains = 4L,
  verbose = FALSE,
  keep_cmd_stan_models = FALSE,
  ...
)

Arguments

x

object to sample, such as Analysis (created with create_analysis_obj()) or Simulation.

...

additional arguments passed to the $sample() method of a cmdstanr Stan model. See https://mc-stan.org/cmdstanr/reference/model-method-sample.html

iter_warmup

integer. The number of warm up iterations to run per chain. The default is 1000.

iter_sampling

integer. The number of post-warm up iterations to run per chain. The default is 10000.

chains

integer. The number of Markov chains to run. The default is 4.

verbose

logical. Whether to print sampler updates (TRUE) or not (FALSE)

posterior_quantiles

numeric vector of length two. The posterior quantiles used for summarizing simulation results. The default is c(0.025, 0.975) See details.

keep_cmd_stan_models

logical. Whether to keep the CmdStanModel objects from the mcmc_sampler (TRUE, discouraged in most scenarios) or not (FALSE). The default is FALSE.

Value

An object of class CmdStanMCMC

An object of class MCMCSimulationResult

Details

Simulation objects

This function takes draws from an MCMC sampler and summarizes results.

Examples

## Analysis objects
if (check_cmdstan()) {
  anls <- create_analysis_obj(
    data_matrix = example_matrix,
    covariates = add_covariates(
      covariates = c("cov1", "cov2"),
      priors = prior_normal(0, 1000)
    ),
    outcome = outcome_surv_weibull_ph(
      "time",
      "cnsr",
      shape_prior = prior_normal(0, 1000),
      baseline_prior = prior_normal(0, 1000)
    ),
    borrowing = borrowing_hierarchical_commensurate(
      "ext",
      prior_exponential(.001)
    ),
    treatment = treatment_details("trt", prior_normal(0, 1000))
  )

  mcmc_results <- mcmc_sample(anls, chains = 1, iter_warmup = 500L, iter_sampling = 1000L)
}
#> Inputs look good.
#> Stan program compiled successfully!
#> Ready to go! Now call `mcmc_sample()`.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 3.0 seconds.

## Simulation objects
base_mat <- matrix(
  c(
    rep(0, 200), rep(0, 200), rep(1, 200),
    rep(1, 200), rep(0, 200), rep(0, 200),
    rep(0, 600)
  ),
  ncol = 3,
  dimnames = list(NULL, c("ext", "trt", "driftOR"))
)

add_binary_endpoint <- function(odds_ratio,
                                base_matrix = base_mat) {
  linear_predictor <- base_matrix[, "trt"] * log(odds_ratio)
  prob <- 1 / (1 + exp(-linear_predictor))

  bin_endpoint <- rbinom(
    NROW(base_matrix),
    1,
    prob
  )

  cbind(base_matrix, matrix(bin_endpoint, ncol = 1, dimnames = list(NULL, "ep")))
}

data_list <- list(
  list(add_binary_endpoint(1.5), add_binary_endpoint(1.5)),
  list(add_binary_endpoint(2.5), add_binary_endpoint(2.5))
)

guide <- data.frame(
  trueOR = c(1.5, 2.5),
  driftOR = c(1.0, 1.0),
  index = 1:2
)

sdl <- sim_data_list(
  data_list = data_list,
  guide = guide,
  effect = "trueOR",
  drift = "driftOR",
  index = "index"
)

if (check_cmdstan()) {
  sim_object <- create_simulation_obj(
    data_matrix_list = sdl,
    outcome = outcome_bin_logistic("ep", prior_normal(0, 1000)),
    borrowing = sim_borrowing_list(list(
      full_borrowing = borrowing_full("ext"),
      bdb = borrowing_hierarchical_commensurate("ext", prior_exponential(0.0001))
    )),
    treatment = treatment_details("trt", prior_normal(0, 1000))
  )

  mcmc_sample(sim_object, chains = 1, iter_warmup = 500L, iter_sampling = 1000L)
}
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 0.3 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 0.4 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 0.3 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 0.3 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 1.9 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 2.7 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 3.3 seconds.
#> Running MCMC with 1 chain...
#> 
#> Chain 1 finished in 2.0 seconds.
#> `MCMCSimulationResult` object.  Call `get_results()` to save outputs as a data.frame
if (FALSE) { # \dontrun{
library(future)
# Use two separate R processes
plan("multisession", workers = 2)

# and two parallel threads in each.
mcmc_sample(sim_object, chains = 1, iter_warmup = 500L, iter_sampling = 1000L, parallel_chains = 2)

# Tidy up processes when finished
plan("sequential")
} # }