Multiverse Analysis of Multinomial Processing Tree Models
Statistical or cognitive modeling usually requires a number of more or less
arbitrary choices creating one specific path through a 'garden of forking paths'.
The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016,
<10.1177>) offers a principled alternative in which results
for all possible combinations of reasonable modeling choices are reported.
MPTmultiverse performs a multiverse analysis for multinomial processing tree
(MPT, Riefer & Batchelder, 1988, <10.1037>) models combining
maximum-likelihood/frequentist and Bayesian estimation approaches with
different levels of pooling (i.e., data aggregation). For the
frequentist approaches, no pooling (with and without parametric or nonparametric
bootstrap) and complete pooling are implemented using
MPTinR < https://cran.r-project.org/package=MPTinR>.
For the Bayesian approaches, no pooling, complete pooling, and three different
variants of partial pooling are implemented using
TreeBUGS < https://cran.r-project.org/package=TreeBUGS>. The main function is
fit_mpt() who performs the multiverse analysis in one call.10.1037>10.1177>
MPTmultiverse is an R package that provides functions for a multiverse analysis of multinomial processing tree (MPT) models. Note that the package is currently work in progress and should be considered alpha. If you experience problems, open an issue.
MPTmultiverse, make sure you already installed the
devtools package via
install.packages("devtools"). Moreover, you also need a to have JAGS installed: Go to http://mcmc-jags.sourceforge.net/ for instructions on how to install JAGS on your machine.
If these prerequisites are met, type
devtools::install_github("mpt-network/MPTmultiverse") in your R console to install
MPTmultiverse together with all required packages that it depends on. To make sure that you are using the latest versions of all packages, you should also run
update.packages(ask = FALSE).
- Create a new folder that contains the following three files
(cf. the subfolder
- The MPT model in the
- The model should be parameterized including all equality constraints.
- To encode fixed parameters (e.g., g=.50), replace the parameter
in the eqn-file by constants.
- The data with individual frequencies as a
- The file
analysis.rmd(copied from the
- Adjust the input options in
analysis.rmd in the section
"MPT model definition & Data". You have to specify the correct file names
and the names of the columns in your data that contain a subject identifier and
- Optionally, set some options (e.g., the number of bootstrap samples) via
- Run the analysis script (e.g., by knitting the .rmd file).
- For the Bayesian models with "no-pooling" and "complete-pooling", no additional
MCMC samples are drawn to achieve the desired level of convergence (e.g.,
Rhat < 1.05).
This might be addressed in future versions of TreeBUGS.
As a remedy, the number of MCMC iterations can be increased a priori (via
All code in this repository is released under the GPL v2 or later license. All non-code materials is released under the CC-BY-SA license.