Published August 21, 2019 | Version v1
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Tutorial: Bayesian Regression in R (using brms)

  • 1. Utrecht University

Description

This tutorial provides the reader with a basic tutorial on how to perform a Bayesian regression in brms, using Stan instead of as the MCMC sampler. Throughout this tutorial, the reader will be guided through importing data files, exploring summary statistics and regression analyses. Here, we will exclusively focus on Bayesian statistics.

Notes

Since we continuously improve the tutorials, let us know via Github (https://github.com/Rensvandeschoot/Tutorials) if you discover mistakes, or if you have additional resources we can refer to.

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Additional details

Related works

Cites
Dataset: 10.5281/zenodo.3962123 (DOI)

References

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  • Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N. Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology 31 (4). https://doi.org/10.1007/s10654-016-0149-3
  • Hoffman, M. D., & Gelman, A. (2014). The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593-1623.
  • van de Schoot R, Yerkes MA, Mouw JM, Sonneveld H (2013) What Took Them So Long? Explaining PhD Delays among Doctoral Candidates. PLoS ONE 8(7): e68839. https://doi.org/10.1371/journal.pone.0068839
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