Category: rblogs (Page 8 of 22)

Gratuitous picture: 3000 trees leaning in a foggy Christchurch day (Photo: Luis).

When R, or any other language, is not enough

2012-12-15 / Luis

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.

R pitfalls #4: redefining the basics

2012-12-14 / Luis

I try to be economical when writing code; for example, I tend to use single quotes over double quotes for characters because it saves me one keystroke. One area where I don’t do that is when typing TRUE and FALSE (R accepts T and F as well), just because it is clearer to see in code and syntax highlighting kicks in. That’s why I was surprised to read Jason Morgan’s post in that it is possible to redefine T and F and get undesirable behavior.
Continue reading

Multisite, multivariate genetic analysis: simulation and analysis

2012-12-14 / Luis

The email wasn’t a challenge but a simple question: Is it possible to run a multivariate analysis in multiple sites? I was going to answer yes, of course, and leave it there but it would be a cruel, non-satisfying answer. We can get a better handle of the question if we use a simple example; let’s assume that we have two traits (call them tree stem diameter and stem density) assessed in two sites (localities).

Because this is genetics we have a family structure, let’s say half-siblings so we only half the mother in common, and we will ignore any experimental design features to keep things simple. We have 100 families, with 30 trees each, in sites A and B, for a total of 6,000 trees (100 x 30 x 2). The data could look like this: Continue reading

More sense of random effects

2012-11-17 / Luis

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

Overlay of design matrices in genetic analysis

2012-10-12 / Luis

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 acts as both males and females. For example, with 5 parents we can have 25 crosses, including reciprocals and selfing (crossing an individual with itself). Decades ago this mating design was tricky to fit and, considering an experimental layout with randomized complete blocks, one would have something like y = mu + blocks + dads + mums + cross + error. In this model dads and mums were estimating a fraction of the additive genetic variance. With the advent of animal model BLUP, was possible to fit something like y = mu + blocks + individual (using a pedigree) + cross + error. Another less computationally demanding alternative (at least with unrelated parents) is to fit a parental model, overlaying the design matrices for parents with something like this y = mu + blocks + (dad + mum) + cross + error.
Continue reading