Publications by Luis
Lattice when modeling, ggplot when publishing
When working in research projects I tend to fit several, sometimes quite a few, alternative models. This model fitting is informed by theoretical considerations (e.g. quantitative genetics, experimental design we used, our understanding of the process under study, etc.) but also by visual inspection of the data. Trellis graphics—where subsets o...
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Large applications of linear mixed models
In a previous post I summarily described our options for (generalized to varying degrees) linear mixed models from a frequentist point of view: nlme, lme4 and ASReml-R†, followed by a quick example for a split-plot experiment. But who is really happy with a toy example? We can show a slightly more complicated example assuming that we have a sim...
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On R, bloggers, politics, sex, alcohol and rock & roll
Yesterday morning at 7 am I was outside walking the dog before getting a taxi to go to the airport to catch a plane to travel from Christchurch to Blenheim (now I can breath after reading without a pause). It was raining cats and dogs while I was thinking of a post idea for Quantum Forest; something that I could work on without a computer. Then I...
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Spatial correlation in designed experiments
Last Wednesday I had a meeting with the folks of the New Zealand Drylands Forest Initiative in Blenheim. In addition to sitting in a conference room and having nice sandwiches we went to visit one of our progeny trials at Cravens. Plantation forestry trials are usually laid out following a rectangular lattice defined by rows and columns. The tria...
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Teaching with R: the switch
There are several blog posts, websites (and even books) explaining the transition from using another statistical system (e.g. SAS, SPSS, Stata, etc) to relying on R. Most of that material treats the topic from the point of view of i- an individual user and ii- a researcher. This post explains some of the issues involved in, first, moving several ...
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Longitudinal analysis: autocorrelation makes a difference
Back to posting after a long weekend and more than enough rugby coverage to last a few years. Anyway, back to linear models, where we usually assume normality, independence and homogeneous variances. In most statistics courses we live in a fantasy world where we meet all of the assumptions, but in real life—and trees and forests are no exceptio...
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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 ...
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Multivariate linear mixed models: livin’ la vida loca
I swear there was a point in writing an introduction to covariance structures: now we can start joining all sort of analyses using very similar notation. In a previous post I described simple (even simplistic) models for a single response variable (or ‘trait’ in quantitative geneticist speak). The R code in three R packages (asreml-R, lme4 an...
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Teaching with R: the tools
I bought an Android phone, nothing fancy just my first foray in the smartphone world, which is a big change coming from the dumb phone world(*). Everything is different and I am back at being a newbie; this is what many students feel the same time they are exposed to R. However, and before getting into software, I find it useful to think of teach...
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Coming out of the (Bayesian) closet: multivariate version
This week I’m facing my—and many other lecturers’—least favorite part of teaching: grading exams. In a supreme act of procrastination I will continue the previous post, and the antepenultimate one, showing the code for a bivariate analysis of a randomized complete block design. Just to recap, the results from the REML multivariate analysi...
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