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 split-plot, particularly because 'it didn't look like' a split-plot to me. Additionally, I have made a few posts discussing linear mixed models using several different… Continue reading Split-plot 1: How does a linear mixed model look like?
A while ago I wanted to run a quantitative genetic analysis where the performance of genotypes in each site was considered as a different trait. If you think about it, with 70 sites and thousands of genotypes one is trying to fit a 70x70 additive genetic covariance matrix, which requires 70*69/2 = 2,415 covariance components.… Continue reading Bivariate linear mixed models using ASReml-R with multiple cores
Things have been a bit quiet at Quantum Forest during the last ten days. Last Monday (Sunday for most readers) I flew to Australia to attend a couple of one-day workshops; one on spatial analysis (in Sydney) and another one on modern applications of linear mixed models (in Wollongong). This will be followed by attending… Continue reading On the (statistical) road, workshops and R
A few years ago we had this really cool idea: we had to establish a trial to understand wood quality in context. Sort of following the saying "we don't know who discovered water, but we are sure that it wasn't a fish" (attributed to Marshall McLuhan). By now you are thinking WTF is this guy… Continue reading Surviving a binomial mixed model
This week I'm facing my—and many other lecturers'—least favorite part of teaching: grading exams. In a supreme act of procrastination I will continue the previous post, and the antepenultimate one, showing the code for a bivariate analysis of a randomized complete block design. Just to recap, the results from the REML multivariate analysis (that used… Continue reading Coming out of the (Bayesian) closet: multivariate version