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Informative Hypotheses

BaIn

THIS WEBPAGE IS CURRENTLY UNDER CONSTRUCTION – NONE OF THE MATERIALS HAVE ARRIVED AT THEIR FINAL VERSIONS YET – MATERIALS YOU CANNOT FIND WILL BE ADDED IN THE COURSE OF 2017

Bain was developed and is being maintained by:

Xin Gu. Department of Geography and Planning, University of Liverpool, GuXin57@hotmail.com.

Herbert Hoijtink. Department of Methodology and Statistics, University Utrecht, H.Hoijtink@uu.nl.

Joris Mulder, Department of Methodology and Statistics, Tilburg University, J.Mulder3@uvt.nl.

The R package Bain can be used for the evaluation of classical and informative hypotheses using the Bayes factor. BaIn is licensed under the GNU General Public License Version >=3 The current version is Bain0.0. It is a beta version, that is, there may still be errors and bugs in the package. Let us know if you find one.

CLICK HERE to obtain all Bain versions released previous to Bain0.0. The first one is the Fortran90 version used in Xin Gu’s dissertation and the publication in the British Journal of Mathematical and Statisticsal Psychology.

CLICK HERE to obtain Bain0-0 and the tutorial explaining how to evaluate classical and informative hypotheses using the Bayes factor. You are well advised to read the tutorial before using Bain.

EXAMPLES OF USING BAIN IN THE CONTEXT OF VARIOUS STATISTICAL MODELS

Is your application not included in the list and you want support, send an e-mail to H.Hoijtink@uu.nl and include the model you want to use, your hypotheses, and data. You can also invite us to give a workshop based on the material covered in WorkshopSlides.pdf

Models that also have been or will be implemented in JASP (jasp-stats.org/) click to download example R code and description

  1. The t-test with unequal variances per group
  2. Equivalence testing
  3. The Bayesian paired t-test
  4. ANOVA: see tutorial.pdf and BFTutorial.R as included in the Bain download – at the bottom of the page you find software for sample size determination in the ANOVA context
  5. ANCOVA
  6. Multiple regression

Models that have not been implemented in JASP – click do download example R code and description

  1. Repeated measures in a four by two within between design
  2. Logistic regression
  3. Structural equation modeling and more specifically, a path model, a confirmatory factor analysis, and a regression with latent variables
  4. Dynamical modeling
  5. Multiple regression in the presence of missing data
  6. Confirmatory factor analysis in the presence of missing data

Approaches that can be executed with Bain or that support Bain

  1. Sample size determination for comparing an inequality constrained hypothesis to its complement or another inequality constrained hypothesis in the context of ANOVA
  2. Evaluating multiple N=1 studies

You can give credit to the authors of Bain by referring to:

Gu, X., Mulder, J., and Hoijtink, H. (in press). Approxomate adjusted fractional Bayes factors: A general method for testing informative hypotheses. British Journal of Mathematical and Statistical Psychology.

Hoijtink, H., Gu, X., and Mulder, J. (unpublished). Bain, multiple group Bayesian evaluation of informative hypotheses.

Other important publications are:

Gu, X. (2016). Bayesian Evaluation of Informative Hypotheses. Doctoral Dissertation, University Utrecht.

Hoijtink, H., Gu, X., Mulder, J., and Rosseel, Y. (unpublished). Computing Bayes factors from data with missing values.