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Simulate a single multivariate data set consisting of n subjects and d phenotypes for each
sim1(X, B, Sigma)
design matrix (incorporating genotype probabilities from two loci), dn by df
a matrix of allele effects, f rows by d columns
dn by dn covariance matrix
a vector of length dn. The first n entries are for trait 1, the second n for trait 2, etc.
# NOT RUN { n_mouse <- 20 geno <- rbinom(n = n_mouse, size = 1, prob = 1 / 2) X <- gemma2::stagger_mats(geno, geno) B <- matrix(c(1, 2), ncol = 2, nrow = 1) sim1(X, B, Sigma = diag(2 * n_mouse)) # }
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