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Plot CV parameter recovery

Source: R/plot_cv_recovery.R
plot_cv_recovery.Rd

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
)



if (FALSE) { # \dontrun{
# 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
)
} # }