
When R, or any other language, is not enough
This post is tangential to R, although R has a fair share of the issues I mention here, which include research reproducibility, open source, paying for software, multiple languages, salt and pepper. There is an increasing interest in the reproducibility of research. In many topics we face multiple, often conflicting claims and as researchers we…

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…

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…

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…

Covariance structures
In most mixed linear model packages (e.g. asreml, lme4, nlme, etc) one needs to specify only the model equation (the bit that looks like y ~ factors…) when fitting simple models. We explicitly say nothing about the covariances that complete the model specification. This is because most linear mixed model packages assume that, in absence…