plot_cv_recovery

plot_cv_recovery.cv4abc

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

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

Guangyu Zhu

plot_cv_recovery 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>n_rows</dt>
<dd>Integer; number of rows in the plot grid (default: 3)</dd></dl></dd> <dt>n_cols</dt>
<dd>Integer; number of columns in the plot grid, multiplied by 2
 for paired plots (default: 1)</dd> <dt>method</dt>
<dd>Character; smoothing method for <code>geom_smooth</code> (default: "lm")</dd> <dt>formula</dt>
<dd>Formula; used in <code>geom_smooth</code> (default: y ~ x)</dd> <dt>resid_tol</dt>
<dd>Numeric; quantile threshold for filtering residuals by
 absolute value. If specified, only observations with residuals below
 this quantile are plotted (default: NULL, no filtering)</dd> <dt>interactive</dt>
<dd>Logical; whether to pause between pages and wait for
 user input (default: FALSE)</dd>
</dl>

Arguments

Plot CV parameter recovery — plot_cv_recovery

<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>n_rows</dt>
<dd>Integer; number of rows in the plot grid (default: 3)</dd>

 <dt>n_cols</dt>
<dd>Integer; number of columns in the plot grid, multiplied by 2
 for paired plots (default: 1)</dd>

 <dt>method</dt>
<dd>Character; smoothing method for <code>geom_smooth</code> (default: "lm")</dd>

 <dt>formula</dt>
<dd>Formula; used in <code>geom_smooth</code> (default: y ~ x)</dd>

 <dt>resid_tol</dt>
<dd>Numeric; quantile threshold for filtering residuals by
 absolute value. If specified, only observations with residuals below
 this quantile are plotted (default: NULL, no filtering)</dd>

 <dt>interactive</dt>
<dd>Logical; whether to pause between pages and wait for
 user input (default: FALSE)</dd>


</dl></dd>

</dl>

plot_cv_recovery: Plot CV parameter recovery

Description

Usage

Value

Arguments

See Also

Examples