Approximate Bayesian Computation wrapper

Wrapper around abc to perform ABC inference. This function provides a consistent interface within the eam package and encapsulates the dependency on the abc package.

Usage

abc_abc(abc_input, tol, method, transf = "none", ...)

Arguments

abc_input: A list with components target, param, and sumstat (typically produced by build_abc_input)
tol: Tolerance level (0 to 1) for ABC acceptance
method: ABC method: "rejection", "loclinear", "neuralnet"
transf: Transformations to apply to parameters: "none" (default), "log", or "logit"
...: Additional arguments passed to abc

Value

An object of class abc from abc

Details

This is a thin wrapper around the abc::abc() function. Users should refer to the abc package documentation for detailed parameter descriptions and options.

Examples

# \donttest{
# Load example simulation output and observed data
rdm_minimal_example <- system.file("extdata", "rdm_minimal", package = "eam")
sim_output <- load_simulation_output(file.path(rdm_minimal_example, "simulation"))
obs_df <- read.csv(file.path(rdm_minimal_example, "observation", "observation_data.csv"))

# Define a summary-statistics pipeline
summary_pipe <- summarise_by(
  .by = c("condition_idx"),
  rt_mean = mean(rt)
)

# Summarise simulation output and observed data
sim_summary <- map_by_condition(
  sim_output,
  .progress = FALSE,
  .parallel = FALSE,
  function(cond_df) {
    summary_pipe(cond_df)
  }
)
obs_summary <- summary_pipe(obs_df)

# Build ABC input
abc_input <- build_abc_input(
  simulation_output = sim_output,
  simulation_summary = sim_summary,
  target_summary = obs_summary,
  param = c("V_beta_1", "V_beta_group")
)

# Fit an ABC model
abc_rejection_model <- abc_abc(
  abc_input = abc_input,
  tol = 0.5,
  method = "rejection"
)
# }