Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example)

Wabersich, Dominik; Vandekerckhove, Joachim

doi:10.3758/s13428-013-0369-3

Cited by 124 publications

(152 citation statements)

References 12 publications

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“…For example, one must choose a method for estimating parameters from data. Though Frequentist approaches, such as chi-square (Ratcliff & Tuerlinckx, 2002), maximum-likelihood Vandekerckhove & Tuerlinckx, 2007), or Kolmogorov-Smirnov (Voss & Voss, 2007) are and remain commonplace, Bayesian methods are becoming increasingly popular (Vandekerckhove, Tuerlinckx, & Lee, 2011;Wabersich & Vandekerckhove, 2014;Wiecki, Sofer, & Frank, 2013). Beyond estimation, there still also remains a choice over how statistical inference will be performed.…”

Section: Threats To the Validity Of Cognitive Modelsmentioning

confidence: 99%

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Dutilh¹,

Annis²,

Brown³

et al. 2016

Preprint

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Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) nondecision time. Inferences about these psychological factors, hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models.

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Section: Threats To the Validity Of Cognitive Modelsmentioning

confidence: 99%

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Dutilh¹,

Annis²,

Brown³

et al. 2016

Preprint

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“…It can be used either for fitting a hierarchical model or for fitting parameters for each individual subject. Recently, another platform-independent software option was introduced by Wabersich and Vandekerckhove (2014).…”

Section: Estimation Programsmentioning

confidence: 99%

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

2016

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Diffusion models (Ratcliff, 1978) make it possible to identify and separate different cognitive processes underlying responses in binary decision tasks (e.g., the speed of information accumulation vs. the degree of response conservatism). This becomes possible because of the high degree of information utilization involved. Not only mean response times or error rates are used for the parameter estimation, but also the response time distributions of both correct and error responses. In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in numbers of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm (Voss, Voss, & Lerche, 2015). The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov-Smirnov outperforms the other two methods. For most conditions, chi-square-based parameter estimations lead to less precise results than the other optimization criteria. The performance of the fast-dm methods was compared to the EZ approach (Wagenmakers, van der Maas, & Grasman, 2007) and to a Bayesian implementation (Wiecki, Sofer, & Frank, 2013). Recommendations for trial numbers are derived from the results for models of different complexities. Interestingly, under certain conditions even small numbers of trials (N < 100) are sufficient for robust parameter estimation.

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“…A very useful tutorial on writing and installing a standalone JAGS module is provided by Wabersich and Vandekerckhove (2014), but it is arguably more straightforward to implement a shared JAGS library inside an R package. The configure script provided inside the runjags package sets up the necessary environmental variables for compilation on any platform, and can be used as a template for creating additional extension modules within R packages.…”

Section: Discussionmentioning

confidence: 99%

“…Although writing MCMC sampling algorithms such as the Metropolis-Hastings algorithm (Hastings 1970) is relatively straightforward, many users employ software such as the Bayesian analysis Using Gibbs Sampling (BUGS) software variants WinBUGS and OpenBUGS (Lunn, Thomas, Best, and Spiegelhalter 2000). Just Another Gibbs Sampler (JAGS; Plummer 2003) is a cross-platform alternative with a direct interface to R using rjags (Plummer 2016), which can be easily extended with user-specified modules supporting additional distributions and random number generators (Wabersich and Vandekerckhove 2014). Each of these uses the BUGS syntax to allow the user to define arbitrary models more easily, which is attractive and attainable for researchers who are more familiar with traditional modeling techniques.…”

Section: Introductionmentioning

confidence: 99%

runjags: AnRPackage Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models inJAGS

Denwood¹

2016

J. Stat. Soft.

613

471

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The runjags package provides a set of interface functions to facilitate running Markov chain Monte Carlo models in JAGS from within R. Automated calculation of appropriate convergence and sample length diagnostics, user-friendly access to commonly used graphical outputs and summary statistics, and parallelized methods of running JAGS are provided. Template model specifications can be generated using a standard lme4-style formula interface to assist users less familiar with the BUGS syntax. Automated simulation study functions are implemented to facilitate model performance assessment, as well as drop-k type cross-validation studies, using high performance computing clusters such as those provided by parallel. A module extension for JAGS is also included within runjags, providing the Pareto family of distributions and a series of minimally-informative priors including the DuMouchel and half-Cauchy priors. This paper outlines the primary functions of this package, and gives an illustration of a simulation study to assess the sensitivity of two equivalent model formulations to different prior distributions.

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Extending JAGS: A tutorial on adding custom distributions to JAGS (with a diffusion model example)

Cited by 124 publications

References 12 publications

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria

runjags: AnRPackage Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models inJAGS

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