# R

## What does a Bayes Factor look like ?

An attempt to illustrate what a Bayes Factor looks like, using GIFs.

## Visualising within-subject effects and stochastic dominance with (augmented) modified Brinley plots

Why can’t we be more idiographic in our research? It is the individual organism that is the principle unit of analysis in the science of psychology (Barlow & Nock, 2009).

## Checking the asumption of independence in binomial trials using posterior predictive checking

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’.

## Using R to make sense of the generalised linear model

What is the difference between the errors and the residuals ? What does it mean for a model to predict something ? What is a link function ? In the current post, we use four R functions (viz., the predict, fitted, residuals and simulate functions) to illustrate the mechanisms and assumptions of the generalised linear model.

## Three methods for computing the intra-class correlation in multilevel logistic regression

In the current post, we present and compare three methods of obtaning an estimation of the ICC in multilevel logistic regression models.

## Experimental absenteeism and logistic regression, part II

This post continues our exploration of the logistic regression model by extending it to a multilevel logistic regression model, using the brms package.

## Experimental absenteeism and logistic regression, part I

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.

## On power and the 'match point situation'

In this post I will argue that the “match point situation” (i.e., the situation in which your entire life is dictated by chance only), is an everyday’ situation for the researcher who uses Null Hypothesis Significance Testing (NHST) without controlling power.