eam (version 1.1.0)

build_abc_input: Build input for Approximate Bayesian Computation (ABC)

Description

Prepares simulation output, summary statistics, and target data for ABC analysis using the abc package. Extracts parameters and summary statistics from simulation results and formats them into matrices suitable for ABC parameter estimation.

Usage

build_abc_input(simulation_output, simulation_summary, target_summary, param)

Value

A list with components suitable for abc::abc

Arguments

simulation_output

A eam_simulation_output object containing that is from run_simulation or load_simulation_output.

simulation_summary

A data frame containing summary statistics for each simulated condition. Should have a 'condition_idx' column and be created by summarise_by.

target_summary

A data frame containing target summary statistics to match against simulation results. Should have the same summary statistic columns as simulation_summary (excluding 'wider_by' columns).

param

Character vector of parameter names to extract from simulation_output. These parameters will be used as the parameter space for ABC estimation.

Required format for summary statistics

Both simulation_summary and target_summary must be created using summarise_by. This ensures consistent column naming and data structure required for ABC analysis. See summarise_by for details on generating properly formatted summaries, and map_by_condition for typical workflow examples. If you want more flexibility in summary statistic calculation, you can manually construct the ABC input list. It is not necessary to use this function if you are familiar with the abc package.

Details

This function provides a streamlined workflow for preparing ABC inputs, but it requires that all components be constructed using this package's functions. Specifically, simulation_output must be created by run_simulation or load_simulation_output, and both simulation_summary and target_summary must be generated using summarise_by. If your data originates from external sources or custom pipelines, you should manually construct the ABC input list instead, ensuring proper matrix formatting and column alignment as expected by abc::abc.

Examples

# \donttest{
# Load the example dataset
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 summary statistics pipeline
summary_pipe <- summarise_by(
  .by = c("condition_idx"),
  rt_mean = mean(rt)
)

# Calculate summary statistics for simulation and observation
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")
)

# Perform ABC parameter estimation using rejection method
abc_rejection_model <- abc::abc(
  target = abc_input$target,
  param = abc_input$param,
  sumstat = abc_input$sumstat,
  tol = 0.5,
  method = "rejection"
)
# }