| abc_cv | R Documentation |
Wrapper around cv4abc to perform cross-validation of ABC results.
This function provides a consistent interface within the eam package
and encapsulates the dependency on the abc package.
abc_cv(abc_input, abc_result, nval, tols, ...)
abc_input |
A list with components |
abc_result |
Fitted ABC model from |
nval |
Number of cross-validation folds |
tols |
Tolerance levels to test during cross-validation |
... |
Additional arguments passed to |
This is a thin wrapper around the abc::cv4abc() function.
When abc_result is provided, cv4abc extracts the method, transf,
and other settings from the fitted ABC object.
Users should refer to the abc package documentation for detailed parameter
descriptions and options.
A cross-validation object from cv4abc
# 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 and fit an ABC model
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")
)
abc_model <- abc_abc(
abc_input = abc_input,
tol = 0.5,
method = "rejection"
)
# Run cross-validation for the fitted ABC model
abc_cv_result <- abc_cv(
abc_input = abc_input,
abc_result = abc_model,
nval = 10,
tols = c(0.1, 0.5)
)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.