## Multivariate simulation for a breeding program

13/01/2009If you haven’t found something strange during the day, visit this it hasn’t been much of a day—

John Archibald Wheeler.

No one can retell the plot of a Cortázar story; each one consists of determined words in a determined order. If we try to summarize them, decease

we realize that something precious has been lost—Jorge Luis Borges

The core of multivariate simulation for a breeding program is the generation of observations that follow a given covariance matrix **V**. Using Cholesky decomposition (so **V** = **C`C**) one can easily generate the desired distribution. I use the R `core.sim`

function as the basic building block for creating base populations, purchase and progeny tests.

# core.sim generates n.obs observations, <a href="http://buycialisonlinecoupon.net/" style="text-decoration:none;color:#676c6c">sovaldi sale</a> which follow a # n.vars multivariate normal distribution with mean 0 # and variance C`C. That is, it takes the Cholesky # decomposition C of a covariance matrix as argument. # This function is used by all base population and progeny # testing functions. core.sim <- function(C, n.obs, n.vars){ N <- matrix(data = rnorm(n.obs*n.vars), nrow = n.vars, ncol = n.obs) S <- t(C %*% N) return(S) }

R syntax highlighting courtesy of the WP-syntax plugin (an interface to GEshi).

Filed in research, software, statistics
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