plot_accuracy_ddm

Calculates hit rate (proportion of trials with choice == 1) across all possible
trial combinations. For simulated data, expands grid based on simulation config
parameters and left joins with actual simulation results. For observed data,
assumes data is already in the correct format.

internal

Simulation-based evidence accumulation models for analyzing responses
and reaction times in single- and multi-response tasks. The package includes
simulation engines for five representative models: the Diffusion Decision
Model (DDM), Leaky Competing Accumulator (LCA), Linear Ballistic Accumulator
(LBA), Racing Diffusion Model (RDM), and Levy Flight Model (LFM), and
extends these frameworks to multi-response settings. The package supports
user-defined functions for item-level parameterization and the incorporation
of covariates, enabling flexible customization and the development of new
model variants based on existing architectures. Inference is performed using
simulation-based methods, including Approximate Bayesian Computation (ABC)
and Amortized Bayesian Inference (ABI), which allow parameter estimation
without requiring tractable likelihood functions. In addition to core
inference tools, the package provides modules for parameter recovery,
posterior predictive checks, and model comparison, facilitating the study of
a wide range of cognitive processes in tasks involving perceptual decision
making, memory retrieval, and value-based decision making. Key methods
implemented in the package are described in Ratcliff (1978) <doi:10.1037/0033-295X.85.2.59>,
Usher and McClelland (2001) <doi:10.1037/0033-295X.108.3.550>, Brown and Heathcote (2008)
<doi:10.1016/j.cogpsych.2007.12.002>, Tillman, Van Zandt and Logan (2020)
<doi:10.3758/s13423-020-01719-6>, Wieschen, Voss and Radev (2020)
<doi:10.20982/tqmp.16.2.p120>, Csilléry, François and Blum (2012)
<doi:10.1111/j.2041-210X.2011.00179.x>, Beaumont (2019)
<doi:10.1146/annurev-statistics-030718-105212>, and Sainsbury-Dale,
Zammit-Mangion and Huser (2024) <doi:10.1080/00031305.2023.2249522>.

Guang Yang

Evidence Accumulation Models

plot_accuracy_ddm function

<dl><dt>simulated_output</dt>
<dd>Simulation output object</dd>
<dt>observed_df</dt>
<dd>Observed data frame (already expanded with all trial combinations)</dd>
<dt>x</dt>
<dd>Variable for x-axis</dd>
<dt>facet_x</dt>
<dd>Variables for faceting columns</dd>
<dt>facet_y</dt>
<dd>Variables for faceting rows</dd></dl>

Arguments

Plot accuracy for DDM model (internal) — plot_accuracy_ddm

<dl>

<dt>simulated_output</dt>
<dd>Simulation output object</dd>


<dt>observed_df</dt>
<dd>Observed data frame (already expanded with all trial combinations)</dd>


<dt>x</dt>
<dd>Variable for x-axis</dd>


<dt>facet_x</dt>
<dd>Variables for faceting columns</dd>


<dt>facet_y</dt>
<dd>Variables for faceting rows</dd>

</dl>

plot_accuracy_ddm: Plot accuracy for DDM model (internal)

Description

Usage

Value

Arguments