BayesFactorMPT: Bayes Factors for Simple (Nonhierarchical) MPT Models

In TreeBUGS: Hierarchical Multinomial Processing Tree Modeling

Bayes Factors for Simple (Nonhierarchical) MPT Models

Description

Computes Bayes factors for simple (fixed-effects, nonhierarchical) MPT models with beta distributions as priors on the parameters.

Usage

BayesFactorMPT(
  models,
  dataset = 1,
  resample,
  batches = 5,
  scale = 1,
  store = FALSE,
  cores = 1
)

Arguments

models	list of models fitted with simpleMPT, e.g., list(mod1, mod2)
dataset	for which data set should Bayes factors be computed?
resample	how many of the posterior samples of the MPT parameters should be resampled per model
batches	number of batches. Used to compute a standard error of the estimate.
scale	how much should posterior-beta approximations be downscaled to get fatter importance-sampling density
store	whether to save parameter samples
cores	number of CPUs used

Details

Currently, this is only implemented for a single data set!

Uses a Rao-Blackwellized version of the product-space method (Carlin & Chib, 1995) as proposed by Barker and Link (2013). First, posterior distributions of the MPT parameters are approximated by independent beta distributions. Second, for one a selected model, parameters are sampled from these proposal distributions. Third, the conditional probabilities to switch to a different model are computed and stored. Finally, the eigenvector with eigenvalue one of the matrix of switching probabilities provides an estimate of the posterior model probabilities.

References

Barker, R. J., & Link, W. A. (2013). Bayesian multimodel inference by RJMCMC: A Gibbs sampling approach. The American Statistician, 67(3), 150-156.

Carlin, B. P., & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society. Series B (Methodological), 57(3), 473-484.

See Also

marginalMPT

TreeBUGS documentation built on May 31, 2023, 9:21 p.m.