Quantum Forest

notes in a shoebox

Quantum Forest

Category: simulation

Multisite, multivariate genetic analysis: simulation and analysis

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On 2012/12/14

In asreml, r, rblogs, simulation

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:

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Overlay of design matrices in genetic analysis

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On 2012/10/12

In asreml, linear models, programming, rblogs, simulation, teaching

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.

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Suicide statistics and the Christchurch earthquake

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On 2012/09/06

In r, rblogs, simulation, teaching

Suicide is a tragic and complex problem. This week New Zealand’s Chief Coroner released its annual statistics on suicide, which come with several tables and figures. One of those figures refers to monthly suicides in the Christchurch region (where I live) and comes with an interesting comment:

Suicides in the Christchurch region (Timaru to Kaikoura) have risen from 67 (2010/11) to 81 (2011/12). The average number of suicides per year for this region over the past four years is 74. The figure of 67 deaths last year reflected the drop in suicides post-earthquake. The phenomenon of a drop in the suicide rate after a large scale crisis event, such as a natural disaster, has been observed elsewhere. [my emphasis]

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R, Julia and genome wide selection

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On 2012/04/25

In bayesian, julia, r, rblogs, simulation, stats

— “You are a pussy” emailed my friend.
— “Sensu cat?” I replied.
— “No. Sensu chicken” blurbed my now ex-friend.

What was this about? He read my post on R, Julia and the shiny new thing, which prompted him to assume that I was the proverbial old dog unwilling (or was it unable?) to learn new tricks. (Incidentally, with friends like this who needs enemies? Hi, Gus.)

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Simulating data following a given covariance structure

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On 2011/10/13

In linear models, r, rblogs, simulation, stats

Every year there is at least a couple of occasions when I have to simulate multivariate data that follow a given covariance matrix. For example, let’s say that we want to create an example of the effect of collinearity when fitting multiple linear regressions, so we want to create one variable (the response) that is correlated with a number of explanatory variables and the explanatory variables have different correlations with each other.

There is a matrix operation called Cholesky decomposition, sort of equivalent to taking a square root with scalars, that is useful to produce correlated data. If we have a covariance matrix M, the Cholesky descomposition is a lower triangular matrix L, such as that M = L L'. How does this connect to our simulated data? Let’s assume that we generate a vector z of random normally independently distributed numbers with mean zero and variance one (with length equal to the dimension of M), we can create a realization of our multivariate distribution using the product L z.

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