Category Archives: Tutorial

Bayesian ANOVA: Powerful inference with within-group sample size of 1

By | March 9, 2017

1 Objective 2 The data 3 Fixed-effects ANOVA in JAGS 4 Relaxing the assumption of constant variance 5 Conclusion This post is inspired by a question by Dylan Craven that he raised during my Bayesian stats course. 1 Objective My aim here is to demonstrate that, in Bayesian setting, one can make powerful inference about… Read More »

Survival analysis: basic terms, the exponential model, censoring, examples in R and JAGS

By | May 13, 2015

I have put together some basic material on survival analysis. It is available as: .html document with highlighted syntax here. Printer-ready .pdf document here. GitHub repository with all the source files here. Main motivation was that I wanted to learn the basics myself; also, it's tricky to find simple examples of survival models fitted in… Read More »

Simple template for scientific manuscripts in R markdown

By | March 12, 2015

I've made a really simple template for the classical manuscript format for R markdown and knitr. Here are the resulting .pdf and .html. The template contains the four usual components of any scientific manuscript: equations (using LaTeX syntax) table with caption (done by kable package, but you can also use xtable) figure with caption citations… Read More »

Poisson regression fitted by glm(), maximum likelihood, and MCMC

By | October 30, 2013

The goal of this post is to demonstrate how a simple statistical model (Poisson log-linear regression) can be fitted using three different approaches. I want to demonstrate that both frequentists and Bayesians use the same models, and that it is the fitting procedure and the inference that differs. This is also for those who understand… Read More »

Spatial correlograms in R: a mini overview

By | May 21, 2013

Spatial correlograms are great to examine patterns of spatial autocorrelation in your data or model residuals. They show how correlated are pairs of spatial observations when you increase the distance (lag) between them - they are plots of some index of autocorrelation (Moran's I or Geary's c) against distance. Although correlograms are not as fundamental… Read More »