Evolving notes, images and sounds by Luis Apiolaza

Category: lme4 (Page 1 of 2)

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 value the ability to evaluate those claims, including repeating/reproducing research results. While I share the interest in reproducibility, some times I feel we are obsessing too much on only part of the research process: statistical analysis. Even here, many people focus not on the models per se, but only on the code for the analysis, which should only use tools that are free of charge.

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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 thought one could add to the explanation to make it a bit clearer.

The idea is that there are 9 individuals, assessed five times each—once under each of five different levels for a treatment—so we need to include individual as a random effect; after all, it is our experimental unit. The code to generate the data, plot it and fit the model is available in the post, but I redid data generation to make it a bit more R-ish and, dare I say, a tad more elegant: Continue reading

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:
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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 of any additional information, the covariance structure is the product of a scalar (a variance component) by a design matrix. For example, the residual covariance matrix in simple models is R = I σe2, or the additive genetic variance matrix is G = A σa2 (where A is the numerator relationship matrix), or the covariance matrix for a random effect f with incidence matrix Z is ZZ σf2.
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