chain_n

Returns a matrix with the number of samples per chain for each stage that is present
in the emc object (i.e., <code>preburn</code>, <code>burn</code>, <code>adapt</code>,
<code>sample</code>). The number of rows of the matrix reflects the number of chains
and the number of columns the number of sampling stages.

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

chain_n function

<dl><dt>emc</dt>
<dd>A list, the output of <code>fit()</code>.</dd></dl>

Arguments

MCMC Chain Iterations — chain_n

<dl>

<dt>emc</dt>
<dd>A list, the output of <code>fit()</code>.</dd>

</dl>

MCMC Chain Iterations

chain_n: MCMC Chain Iterations

Description

Usage

Value

Arguments

Examples