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
December and January were crazy months, with a lot of travel and suddenly I found myself in February working in four parallel projects involving quantitative genetics data analyses. (I'll write about some of them very soon) Anyhow, as I have pointed out in repeated occasions, I prefer asreml-R for mixed model analyses because I run… Continue reading Rstudio and asreml working together in a mac
After attending two one-day workshops last week I spent most days paying attention to (well, at least listening to) presentations in this biostatistics conference. Most presenters were R users—although Genstat, Matlab and SAS fans were also present and not once I heard "I can't deal with the current size of my data sets". However, there… Continue reading Tall big data, wide big data
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