Online supplementary (R code and data) for the article:
- Heck*, D. W., Arnold*, N. R., & Arnold, D. (in press). TreeBUGS: An R Package for Hierarchical Multinomial-Processing-Tree Modeling. Behavior Research Methods. doi:10.3758/s13428-017-0869-7
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Abstract
Multinomial processing tree (MPT) models are a class of measurement models that allow explaining categorical data by a finite number of underlying cognitive processes. Traditionally, data are aggregated across participants and analyzed under the assumption of independently and identically distributed observations. Hierarchical Bayesian extensions of MPT models explicitly account for participant heterogeneity by assuming that the individual parameters follow a continuous hierarchical distribution.
We provide an accessible introduction to hierarchical MPT modeling and present the user-friendly and comprehensive R package TreeBUGS, which implements the two most important hierarchical MPT approaches for participant heterogeneity − the beta-MPT (Smith & Batchelder, 2010) and the latent-trait MPT approach (Klauer, 2010). TreeBUGS reads standard MPT model files and obtains Markov chain Monte Carlo samples that approximate the posterior distribution.
The functionality and output of TreeBUGS is tailored to the specific needs of MPT modelers and provides tests for the homogeneity of items and participants, individual and group parameters estimates, fit statistics, between-subject comparisons, as well as goodness-of-fit and summary plots. We also propose and implement novel statistical extensions to include continuous and discrete predictors (either as fixed or random effects) in the latent-trait MPT model.
