abi_assess: Assess neural estimator using trained estimator

In eam: Evidence Accumulation Models

Assess neural estimator using trained estimator

Description

A wrapper around NeuralEstimators::assess() that automatically unpacks the trained estimator and ABI input from a trained estimator object created by abi_train.

Usage

abi_assess(
  trained_estimator,
  estimator_name = NULL,
  use_gpu = TRUE,
  verbose = TRUE
)

Arguments

trained_estimator	A trained estimator object returned by abi_train. Must be of class eam_abi_trained_estimator and contain trained_estimator and abi_input elements.
estimator_name	Character string; optional name for the estimator (default: NULL).
use_gpu	Logical; whether to use GPU for assessment (default: TRUE).
verbose	Logical; whether to print progress information (default: TRUE).

Details

This function extracts the trained estimator and ABI input from the trained estimator object, then extracts test parameters and summary statistics from the ABI input, along with parameter names (theta), and passes them to NeuralEstimators::assess(). The test set (theta_test and Z_test) is used for assessment.

The returned object has class eam_abi_assess, which enables the use of S3 methods like plot_cv_recovery for visualization.

Value

A list with class eam_abi_assess containing:

estimates

Data frame with columns: m, k, j, estimator, parameter, estimate, truth

runtimes

Data frame with runtime information

Note

This function initializes the global Julia environment on first call.

Examples

## Not run: 
# Train an estimator first
trained_estimator <- abi_train(
  estimator = estimator,
  abi_input = abi_input,
  epochs = 100
)

# Assess the trained estimator
assessment <- abi_assess(
  trained_estimator = trained_estimator,
  estimator_name = "MyEstimator",
  use_gpu = TRUE,
  verbose = TRUE
)

# View the assessment results
str(assessment)

# Plot parameter recovery
plot_cv_recovery(assessment)

## End(Not run)

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