Computes posterior predictive p-values to test model fit.

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

PPP function

<dl><dt>fittedModel</dt>
<dd>fitted latent-trait or beta MPT model (<code>traitMPT</code>, <code>betaMPT</code>)</dd>
<dt>M</dt>
<dd>number of posterior predictive samples. As a maximum, the number of posterior samples in <code>fittedModel</code> is used.</dd>
<dt>nCPU</dt>
<dd>number of CPUs used for parallel sampling. For large models and many participants, this requires considerable computer-memory resources (as a remedy, use <code>nCPU=1</code>).</dd>
<dt>T2</dt>
<dd>whether to compute T2 statistic to check coveriance structure (can
take a lot of time). If some participants do not have responses for some
trees, (co)variances are computed by pairwise deletion of the corresponding
persons.</dd>
<dt>type</dt>
<dd>whether the T1 statistic of expected means is computed using
Person's <code>"X2"</code> or the likelihood-ratio statistic <code>"G2"</code></dd></dl>

Arguments

Author

Compute Posterior Predictive P-Values — PPP

<dl>

<dt>fittedModel</dt>
<dd>fitted latent-trait or beta MPT model (<code>traitMPT</code>, <code>betaMPT</code>)</dd>


<dt>M</dt>
<dd>number of posterior predictive samples. As a maximum, the number of posterior samples in <code>fittedModel</code> is used.</dd>


<dt>nCPU</dt>
<dd>number of CPUs used for parallel sampling. For large models and many participants, this requires considerable computer-memory resources (as a remedy, use <code>nCPU=1</code>).</dd>


<dt>T2</dt>
<dd>whether to compute T2 statistic to check coveriance structure (can
take a lot of time). If some participants do not have responses for some
trees, (co)variances are computed by pairwise deletion of the corresponding
persons.</dd>


<dt>type</dt>
<dd>whether the T1 statistic of expected means is computed using
Person's <code>"X2"</code> or the likelihood-ratio statistic <code>"G2"</code></dd>

</dl>

PPP: Compute Posterior Predictive P-Values

Description

Usage

Arguments

Author

References