Calculate information criteria (DIC, BPIC), effective number of parameters and
constituent posterior deviance (D) summaries (meanD = mean of D, Dmean = D
for mean of posterior parameters and minD = minimum of D).

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

Jochen Voss

Andreas Voss

IC function

<dl><dt>emc</dt>
<dd>emc object or list of these</dd>
<dt>stage</dt>
<dd>A string. Specifies which stage you want to plot.</dd>
<dt>filter</dt>
<dd>An integer or vector. If it's an integer, iterations up until the value set by <code>filter</code> will be excluded.
If a vector is supplied, only the iterations in the vector will be considered.</dd>
<dt>use_best_fit</dt>
<dd>Boolean, default TRUE use best of minD and Dmean in
calculation otherwise always use Dmean</dd>
<dt>print_summary</dt>
<dd>Boolean (default TRUE) print table of results</dd>
<dt>digits</dt>
<dd>Integer, significant digits in printed table</dd>
<dt>subject</dt>
<dd>Integer or string selecting a single subject, default NULL
returns sums over all subjects</dd>
<dt>group_only</dt>
<dd>Boolean. If <code>TRUE</code> will calculate the IC for the group-level only</dd></dl>

Arguments

Calculate information criteria (DIC, BPIC), effective number of parameters and
constituent posterior deviance (D) summaries (meanD = mean of D, Dmean = D
for mean of posterior parameters and minD = minimum of D). — IC

<dl>

<dt>emc</dt>
<dd>emc object or list of these</dd>


<dt>stage</dt>
<dd>A string. Specifies which stage you want to plot.</dd>


<dt>filter</dt>
<dd>An integer or vector. If it's an integer, iterations up until the value set by <code>filter</code> will be excluded.
If a vector is supplied, only the iterations in the vector will be considered.</dd>


<dt>use_best_fit</dt>
<dd>Boolean, default TRUE use best of minD and Dmean in
calculation otherwise always use Dmean</dd>


<dt>print_summary</dt>
<dd>Boolean (default TRUE) print table of results</dd>


<dt>digits</dt>
<dd>Integer, significant digits in printed table</dd>


<dt>subject</dt>
<dd>Integer or string selecting a single subject, default NULL
returns sums over all subjects</dd>


<dt>group_only</dt>
<dd>Boolean. If <code>TRUE</code> will calculate the IC for the group-level only</dd>

</dl>

Calculate information criteria (DIC, BPIC), effective number of parameters and
constituent posterior deviance (D) summaries (meanD = mean of D, Dmean = D
for mean of posterior parameters and minD = minimum of D).

IC: Calculate information criteria (DIC, BPIC), effective number of parameters and constituent posterior deviance (D) summaries (meanD = mean of D, Dmean = D for mean of posterior parameters and minD = minimum of D).

Description

Usage

Value

Arguments