plot_cv_recovery: Plot CV parameter recovery

In eam: Evidence Accumulation Models

Plot CV parameter recovery

Description

Visualize parameter recovery from cross-validation results, showing estimated vs. true parameter values and residual distributions for each parameter.

Usage

plot_cv_recovery(data, ...)

## S3 method for class 'cv4abc'
plot_cv_recovery(data, ...)

## S3 method for class 'eam_abi_assess'
plot_cv_recovery(data, ...)

## S3 method for class 'eam_abi_posterior_samples'
plot_cv_recovery(data, trained_estimator = NULL, theta = NULL, ...)

Arguments

data	An eam_abi_posterior_samples object from abi_sample_posterior containing posterior samples with columns dataset_id and parameter columns. The median of each parameter for each dataset is used as the point estimate for recovery assessment.
...	Additional arguments: n_rows Integer; number of rows in the plot grid (default: 3) n_cols Integer; number of columns in the plot grid, multiplied by 2 for paired plots (default: 1) method Character; smoothing method for geom_smooth (default: "lm") formula Formula; used in geom_smooth (default: y ~ x) resid_tol Numeric; quantile threshold for filtering residuals by absolute value. If specified, only observations with residuals below this quantile are plotted (default: NULL, no filtering) interactive Logical; whether to pause between pages and wait for user input (default: FALSE)
trained_estimator	Optional. A trained estimator object returned by abi_train. If provided, the true parameter values are extracted from trained_estimator$abi_input$theta_test. Either trained_estimator or theta must be provided, but not both.
theta	Optional. A matrix of true parameter values with parameters as rows and datasets as columns. Column count must match the number of unique dataset_id values in data. Either trained_estimator or theta must be provided, but not both.

Value

Invisibly returns 'NULL'. Called for its side effect of producing plots.

See Also

plot_cv_recovery.cv4abc, plot_cv_recovery.eam_abi_assess, plot_cv_recovery.eam_abi_posterior_samples

Examples

# Load CV output from saved file
cv_file <- system.file(
  "extdata", "rdm_minimal", "abc", "cv", "neuralnet.rds",
  package = "eam"
)
abc_neuralnet_cv <- readRDS(cv_file)

# Plot parameter recovery
plot_cv_recovery(
  abc_neuralnet_cv,
  n_rows = 2,
  n_cols = 1,
  resid_tol = 0.99
)

## Not run: 
# Train a posterior estimator
trained_estimator <- abi_train(
  estimator = posterior_estimator,
  abi_input = abi_input,
  epochs = 50
)

# Sample from posterior using test data (default)
posterior_samples <- abi_sample_posterior(
  trained_estimator = trained_estimator,
  N = 1000
)

# Plot recovery using trained_estimator to get true values
plot_cv_recovery(
  posterior_samples,
  trained_estimator = trained_estimator
)

# Alternatively, provide true parameter values directly
plot_cv_recovery(
  posterior_samples,
  theta = abi_input$theta_test
)

## End(Not run)

eam documentation built on March 29, 2026, 5:07 p.m.