plot_sbc_ecdf

Plots the difference in observed cumulative rank statistics and the
expected cumulative distribution of a uniform distribution. The blue
shaded areas indicate the 95% credible interval.

Fit Bayesian (hierarchical) cognitive models
using a linear modeling language interface using particle Metropolis Markov
chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM),
linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal
race model (LNR) are supported. Additionally, users can specify their own likelihood
function and/or choose for non-hierarchical
estimation, as well as for a diagonal, blocked or full multivariate normal
group-level distribution to test individual differences. Prior specification
is facilitated through methods that visualize the (implied) prior.
A wide range of plotting functions assist in assessing model convergence and
posterior inference. Models can be easily evaluated using functions
that plot posterior predictions or using relative model comparison metrics
such as information criteria or Bayes factors.
References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.

Niek Stevenson

EMC2

Bayesian Hierarchical Analysis of Cognitive Models of Choice

Michelle Donzallaz

Andrew Heathcote

Steven Miletić

Raphael Hartmann

Karl C. Klauer

Steven G. Johnson

Jean M. Linhart

Brian Gough

Gerard Jungman

Rudolf Schuerer

Przemyslaw Sliwa

Jason H. Stover

plot_sbc_ecdf function

<dl><dt>ranks</dt>
<dd>A list of named dataframes of the rank statistic</dd>
<dt>layout</dt>
<dd>Optional. A numeric vector specifying the layout using <code>par(mfrow = layout)</code></dd></dl>

Arguments

Plot the ECDF Difference in SBC Ranks — plot_sbc_ecdf

<dl>

<dt>ranks</dt>
<dd>A list of named dataframes of the rank statistic</dd>


<dt>layout</dt>
<dd>Optional. A numeric vector specifying the layout using <code>par(mfrow = layout)</code></dd>

</dl>

Plot the ECDF Difference in SBC Ranks

plot_sbc_ecdf: Plot the ECDF Difference in SBC Ranks

Description

Usage

Value

Arguments