plot_cv_pair_correlation

plot_cv_pair_correlation.cv4abc

Create a matrix of pairwise plots for cross-validation parameter estimates,
including scatter plots with fitted trends, rank correlations, and marginal
distributions.

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_cv_pair_correlation function

<dl><dt>data</dt>
<dd>A <code>cv4abc</code> object containing true parameters and
cross-validated estimates.</dd>
<dt>...</dt>
<dd>Additional arguments:<dl>
 <dt>interactive</dt>
<dd>Logical; whether to pause between tolerance levels and wait for input</dd></dl></dd>
</dl>

Arguments

Plot CV parameter pair correlations — plot_cv_pair_correlation

<dl>

<dt>data</dt>
<dd>A <code>cv4abc</code> object containing true parameters and
cross-validated estimates.</dd>


<dt>...</dt>
<dd>Additional arguments:<dl>
 <dt>interactive</dt>
<dd>Logical; whether to pause between tolerance levels and wait for input</dd>


</dl></dd>

</dl>

plot_cv_pair_correlation: Plot CV parameter pair correlations

Description

Usage

Value

Arguments

See Also

Examples