ABC posterior predictive check

High-level convenience wrapper for posterior predictive checks from abc_abc() outputs.

Usage

abc_posterior_predictive_check(
  config,
  abc_result,
  observed_df,
  n_conditions = 1,
  n_trials_per_condition = 500,
  n_items = config$n_items,
  n_conditions_per_chunk = NULL,
  output_dir = NULL,
  rt_facet_x = c("item_idx"),
  rt_facet_y = c(),
  accuracy_x = "item_idx",
  accuracy_facet_x = c(),
  accuracy_facet_y = c()
)

Arguments

config

Simulation configuration object.

abc_result

Fitted object from abc_abc().

observed_df

Observed trial-level data frame.

n_conditions

Number of posterior predictive conditions.

n_trials_per_condition

Number of trials per condition.

n_items

Number of items per trial.

n_conditions_per_chunk

Number of conditions per processing chunk.

output_dir

Optional output directory for simulation files.

rt_facet_x

Facet columns for plot_rt() x facets.

rt_facet_y

Facet columns for plot_rt() y facets.

accuracy_x

Grouping variable for plot_accuracy() x-axis.

accuracy_facet_x

Facet columns for plot_accuracy() x facets.

accuracy_facet_y

Facet columns for plot_accuracy() y facets.

Value

invisible(NULL). This function is used for plotting side effects only and prints RT and accuracy plots directly.

Details

This function is for teaching and quick demonstrations. It is intentionally specific to one input shape (an abc object). For step checks, follow these functions: abc_posterior_bootstrap(), update_config_from_posterior(), and run_simulation().

This wrapper is mainly a teaching tool. It provides a compact end-to-end posterior predictive workflow, but it intentionally hides several modeling choices by collapsing the posterior to a single summary and then simulating from that reduced representation.

For more serious work, manual posterior predictive simulation is preferred. The recommended workflow is to draw posterior parameter values explicitly with abc_posterior_bootstrap(), inspect or modify those draws as needed, rebuild a simulation configuration explicitly with new_simulation_config so the parameter structure is fully under your control, run the simulation with run_simulation(), and then compare the simulated output with the observed data using plotting or summary functions. update_config_from_posterior() can still be useful for quick checks, but rebuilding the config is the safer option when you need to know exactly how posterior values are mapped back into the model. Following the steps manually makes each assumption visible, including which posterior draw was used, how parameter values entered the simulation config, and how the posterior predictive data were generated.

Examples

# \donttest{
# Load example simulation config, fitted ABC model, and observed data
base_dir <- system.file("extdata", "rdm_minimal", package = "eam")
sim_output <- load_simulation_output(file.path(base_dir, "simulation"))
abc_model <- readRDS(file.path(base_dir, "abc", "abc_neuralnet_model.rds"))
obs_df <- read.csv(file.path(base_dir, "observation", "observation_data.csv"))

# Run a high-level posterior predictive check
abc_posterior_predictive_check(
  config = sim_output$simulation_config,
  abc_result = abc_model,
  observed_df = obs_df,
  n_conditions = 1,
  n_trials_per_condition = 500,
  rt_facet_x = c("item_idx"),
  rt_facet_y = c(),
  accuracy_x = "item_idx",
  accuracy_facet_x = c("group"),
  accuracy_facet_y = c()
)
#> Error in accumulate_evidence_ddm_2b(item_params$A_upper, item_params$A_lower,     item_params$V, Z, item_params$ndt, max_t, dt, max_reached,     noise_mechanism, noise_fun): could not find function "accumulate_evidence_ddm_2b"
# }