Posts Tagged ‘ asreml ’

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

July 30, 2012
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Split-plot 2: let’s throw in some spatial effects

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 … Continue reading →

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Split-plot 1: How does a linear mixed model look like?

June 24, 2012
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Split-plot 1: How does a linear mixed model look like?

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 … Continue reading →

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Bivariate linear mixed models using ASReml-R with multiple cores

May 7, 2012
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Bivariate linear mixed models using ASReml-R with multiple cores

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 … Continue reading →

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Rstudio and asreml working together in a mac

February 5, 2012
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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 … Continue reading →

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Tall big data, wide big data

December 12, 2011
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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 one … Continue reading →

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Surviving a binomial mixed model

November 11, 2011
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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 … Continue reading →

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Multivariate linear mixed models: livin’ la vida loca

October 31, 2011
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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 … Continue reading →

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Covariance structures

October 26, 2011
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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 … Continue reading →

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Longitudinal analysis: autocorrelation makes a difference

October 25, 2011
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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 … Continue reading →

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Spatial correlation in designed experiments

October 20, 2011
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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 … Continue reading →

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