Category: asreml (Page 2 of 4)

Gratuitous picture: Detail of Mosteiro dos Jerónimos, Belém, Lisboa, Portugal (Photo: Luis).

Split-plot 2: let’s throw in some spatial effects

2012-07-31 / Luis

Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid and Lisbon.

Where was I? Showing how split-plots look like under the bonnet (hood for you US readers). Yates presented a nice diagram of his oats data set in the paper, so we have the spatial location of each data point which permits us playing with within-trial spatial trends.
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Split-plot 1: How does a linear mixed model look like?

2012-06-25 / Luis

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 packages to fit them. At no point I have shown what are the calculations behind the scenes. So, I decided to combine my frustration and an explanation to myself in a couple of posts. This is number one and the follow up is Split-plot 2: let’s throw in some spatial effects.
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Gratuitous picture: Trees at 8 pm illuminated by bonfire and full moon (Photo: Luis).

Bivariate linear mixed models using ASReml-R with multiple cores

2012-05-07 / Luis

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 70×70 additive genetic covariance matrix, which requires 70*69/2 = 2,415 covariance components. Besides requiring huge amounts of memory and being subject to all sort of estimation problems there were all sort of connectedness issues that precluded the use of Factor Analytic models to model the covariance matrix. The best next thing was to run over 2,000 bivariate analyses to build a large genetic correlation matrix (which has all sort of issues, I know). This meant leaving the computer running for over a week.
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Rstudio and asreml working together in a mac

2012-02-06 / Luis

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 out of functionality with nlme and lme4 very quickly. Ten-trait multivariate mixed model with a pedigree, anyone? I thought so. Well, there are asreml-R versions for Windows, Linux and OS X; unsurprisingly, I use the latter. Installation in OS X is not particularly complicated (just follow the instructions in this PDF file) and remember to add and export the following environment variables in your .bash_profile:
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Tall big data, wide big data

2011-12-13 / Luis

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 were some complaints about the speed of R, particularly when dealing with simulations or some genomic analyses.