
Coming out of the (Bayesian) closet
Until today all the posts in this blog have used a frequentist view of the world. I have a confession to make: I have an ecumenical view of statistics and I do sometimes use Bayesian approaches in data analyses. This is not quite one of those “the truth will set you free” moments, but I’ll […]

Multivariate linear mixed models: livin’ la vida loca
I swear there was a point in writing an introduction to covariance structures: now we can start joining all sort of analyses using very similar notation. In a previous post I described simple (even simplistic) models for a single response variable (or ‘trait’ in quantitative geneticist speak). The R code in three R packages (asremlR, […]

Spatial correlation in designed experiments
Last Wednesday I had a meeting with the folks of the New Zealand Drylands Forest Initiative in Blenheim. In addition to sitting in a conference room and having nice sandwiches we went to visit one of our progeny trials at Cravens. Plantation forestry trials are usually laid out following a rectangular lattice defined by rows […]

Large applications of linear mixed models
In a previous post I summarily described our options for (generalized to varying degrees) linear mixed models from a frequentist point of view: nlme, lme4 and ASRemlR†, followed by a quick example for a splitplot experiment. But who is really happy with a toy example? We can show a slightly more complicated example assuming that […]

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 […]