
When R, or any other language, is not enough
This post is tangential to R, although R has a fair share of the issues I mention here, which include research reproducibility, open source, paying for software, multiple languages, salt and pepper. There is an increasing interest in the reproducibility of research. In many topics we face multiple, often conflicting claims and as researchers we […]

Splitplot 1: How does a linear mixed model look like?
I like statistics and I struggle with statistics. Often times I get frustrated when I don’t understand and I really struggled to make sense of Krushke’s Bayesian analysis of a splitplot, particularly because ‘it didn’t look like’ a splitplot to me. Additionally, I have made a few posts discussing linear mixed models using several different […]

Covariance structures
In most mixed linear model packages (e.g. asreml, lme4, nlme, etc) one needs to specify only the model equation (the bit that looks like y ~ factors…) when fitting simple models. We explicitly say nothing about the covariances that complete the model specification. This is because most linear mixed model packages assume that, in absence […]

Longitudinal analysis: autocorrelation makes a difference
Back to posting after a long weekend and more than enough rugby coverage to last a few years. Anyway, back to linear models, where we usually assume normality, independence and homogeneous variances. In most statistics courses we live in a fantasy world where we meet all of the assumptions, but in real life—and trees and […]

Linear mixed models in R
A substantial part of my job has little to do with statistics; nevertheless, a large proportion of the statistical side of things relates to applications of linear mixed models. The bulk of my use of mixed models relates to the analysis of experiments that have a genetic structure. A brief history of time At the […]