summary.emc.design

Prints a summary of the design object, including sampled parameters and design matrices.
For continuous covariates just prints one row, instead of all covariates.

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

summary.emc.design function

<dl><dt>object</dt>
<dd>An object of class <code>emc.design</code> containing the design to summarize</dd>
<dt>...</dt>
<dd>Additional arguments (not used)</dd></dl>

Arguments

Summary method for emc.design objects — summary.emc.design

<dl>

<dt>object</dt>
<dd>An object of class <code>emc.design</code> containing the design to summarize</dd>


<dt>...</dt>
<dd>Additional arguments (not used)</dd>

</dl>

Summary method for emc.design objects

summary.emc.design: Summary method for emc.design objects

Description

Usage

Value

Arguments