notes in a shoebox

Quantum Forest

Category: linear models Page 2 of 4

Split-plot 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 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 packages to fit them. At no point I have shown what are the calculations behind the scenes. So, I decided to combine my frustration and an explanation to myself in a couple of posts. This is number one and the follow up is Split-plot 2: let’s throw in some spatial effects.

Bivariate linear mixed models using ASReml-R with multiple cores

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 70×70 additive genetic covariance matrix, which requires 70*69/2 = 2,415 covariance components. Besides requiring huge amounts of memory and being subject to all sort of estimation problems there were all sort of connectedness issues that precluded the use of Factor Analytic models to model the covariance matrix. The best next thing was to run over 2,000 bivariate analyses to build a large genetic correlation matrix (which has all sort of issues, I know). This meant leaving the computer running for over a week.

On the (statistical) road, workshops and R

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 The International Biometric Society Australasian Region Conference in Kiama.

I would like to comment on the workshops to look for commonalities and differences. First, both workshops heavily relied on R, supporting the idea that if you want to reach a lot of people and get them using your ideas, R is pretty much the vehicle to do so. It is almost trivial to get people to install R and RStudio before the workshop so they are ready to go. “Almost” because you have to count on someone having a bizarre software configuration or draconian security policies for their computer.

Surviving a binomial mixed model

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 talking about? But the idea was simple; let’s put a trial that had the species we wanted to study (Pinus radiata, a gymnosperm) and an angiosperm (Eucalyptus nitens if you wish to know) to provide the contrast, as they are supposed to have vastly different types of wood. From space the trial looked like this:

Coming out of the (Bayesian) closet: multivariate version

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.

Page 2 of 4

Powered by WordPress & Theme by Anders Norén