simulate.bgms: Simulate Data from a Fitted bgms Model

Description

Generates new observations from the Markov Random Field model using the estimated parameters from a fitted bgms object.

Usage

# S3 method for bgms
simulate(
  object,
  nsim = 500,
  seed = NULL,
  method = c("posterior-mean", "posterior-sample"),
  ndraws = NULL,
  iter = 1000,
  cores = parallel::detectCores(),
  display_progress = c("per-chain", "total", "none"),
  ...
)

Value

If method = "posterior-mean": A matrix with nsim rows and p columns containing simulated observations.

If method = "posterior-sample": A list of matrices, one per posterior draw, each with nsim rows and p columns.

Arguments

object

An object of class bgms.

nsim

Number of observations to simulate. Default: 500.

seed

Optional random seed for reproducibility.

method

Character string specifying which parameter estimates to use:

"posterior-mean": Use posterior mean parameters (faster, single simulation).

"posterior-sample"

Sample from posterior draws, producing one dataset per draw (accounts for parameter uncertainty). This method uses parallel processing when cores > 1.

ndraws

Number of posterior draws to use when method = "posterior-sample". If NULL, uses all available draws.

iter

Number of Gibbs iterations for equilibration before collecting samples. Default: 1000.

cores

Number of CPU cores for parallel execution when method = "posterior-sample". Default: parallel::detectCores().

display_progress

Character string specifying the type of progress bar. Options: "per-chain", "total", "none". Default: "per-chain".

...

Additional arguments (currently ignored).

Details

This function uses the estimated interaction and threshold parameters to generate new data via Gibbs sampling. When method = "posterior-sample", parameter uncertainty is propagated to the simulated data by using different posterior draws. Parallel processing is available for this method via the cores argument.

Examples

Run this code

# \donttest{
# Fit a model
fit <- bgm(x = Wenchuan[, 1:5], chains = 2)

# Simulate 100 new observations using posterior means
new_data <- simulate(fit, nsim = 100)

# Simulate with parameter uncertainty (10 datasets)
new_data_list <- simulate(fit, nsim = 100, method = "posterior-sample", ndraws = 10)

# Use parallel processing for faster simulation
new_data_list <- simulate(fit, nsim = 100, method = "posterior-sample",
                          ndraws = 100, cores = 2)
# }