An attempt to illustrate what a Bayes factor looks like, using GIFs.
As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive p-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.
In the current post, we present and compare three methods of obtaning an estimation of the ICC in multilevel logistic regression models.
This post continues our exploration of the logistic regression model by extending it to a multilevel logistic regression model, using the brms package.
This post aims to assess the average probability of participant presence in psychological experiments and, in the meantime, to introduce Bayesian logistic regression using R and the rethinking package.
According to Rubin (1984), a Bayesianly justifiable analysis is one that “treats known values as observed values of random variables, treats unknown values as unobserved random variables, and calculates the conditional distribution of unknowns given knowns and model specifications using Bayes’ theorem”