
Analyzing a simple experiment with heterogeneous variances using asreml, MCMCglmm and SAS
I was working with a small experiment which includes families from two Eucalyptus species and thought it would be nice to code a first analysis using alternative approaches. The experiment is a randomized complete block design, with species as fixed effect and family and block as a random effects, while the response variable is growth […]

More sense of random effects
I can’t exactly remember how I arrived to Making sense of random effects, a good post in the Distributed Ecology blog (go over there and read it). Incidentally, my working theory is that I follow Scott Chamberlain (@recology_), who follows Karthik Ram ?(@_inundata) who mentioned Edmund Hart’s (@DistribEcology) post. I liked the discussion, but I […]

Overlay of design matrices in genetic analysis
I’ve ignored my quantitative geneticist side of things for a while (at least in this blog) so this time I’ll cover some code I was exchanging with a couple of colleagues who work for other organizations. It is common to use diallel mating designs in plant and tree breeding, where a small number of parents […]

INLA: Bayes goes to Norway
INLA is not the Norwegian answer to ABBA; that would probably be aha. INLA is the answer to ‘Why do I have enough time to cook a threecourse meal while running MCMC analyses?”. Integrated Nested Laplace Approximations (INLA) is based on direct numerical integration (rather than simulation as in MCMC) which, according to people ‘in […]

Splitplot 2: let’s throw in some spatial effects
Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid and Lisbon. Where was I? Showing how splitplots look like under the bonnet (hood for […]