summarizeMCMC

TreeBUGS-specific MCMC summary for <code>mcmc.list</code>-objects.

User-friendly analysis of hierarchical multinomial processing tree (MPT)
models that are often used in cognitive psychology. Implements the latent-trait
MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT
approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model
heterogeneity of participants. MPT models are conveniently specified by an
.eqn-file as used by other MPT software and data are provided by a .csv-file
or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored
Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides
tests of heterogeneity and MPT-tailored summaries and plotting functions.
A detailed documentation is available in Heck, Arnold, & Arnold (2018)
<DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found
in Schmidt, Erdfelder, & Heck (2022) <DOI:10.31234/osf.io/gh8md>.

Daniel W. Heck

TreeBUGS

Hierarchical Multinomial Processing Tree Modeling

Nina R. Arnold

Denis Arnold

Alexander Ly

Marius Barth

summarizeMCMC function

<dl><dt>mcmc</dt>
<dd>a <code>mcmc.list</code> object</dd>
<dt>batchSize</dt>
<dd>size of batches of parameters used to reduce memory load
when computing posterior summary statistics (including Rhat and effective
sample size).</dd>
<dt>probs</dt>
<dd>quantile probabilities used to compute credibility intervals</dd></dl>

Arguments

MCMC Summary — summarizeMCMC

<dl>

<dt>mcmc</dt>
<dd>a <code>mcmc.list</code> object</dd>


<dt>batchSize</dt>
<dd>size of batches of parameters used to reduce memory load
when computing posterior summary statistics (including Rhat and effective
sample size).</dd>


<dt>probs</dt>
<dd>quantile probabilities used to compute credibility intervals</dd>

</dl>

summarizeMCMC: MCMC Summary

Description

Usage

Arguments