Simulate a single multivariate data set consisting of n subjects and d phenotypes for each

sim1(X, B, Sigma)

## Arguments

X 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

## Value

a vector of length dn. The first n entries are for trait 1, the second n for trait 2, etc.

## Examples

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))
#>              [,1]
#>  [1,]  0.12863291
#>  [2,] -0.45729772
#>  [3,]  0.62772851
#>  [4,] -0.19146280
#>  [5,]  0.58708961
#>  [6,] -0.11533387
#>  [7,]  0.34681789
#>  [8,]  0.72167655
#>  [9,] -0.19136770
#> [10,] -0.54452703
#> [11,] -0.03527251
#> [12,]  1.04785759
#> [13,] -0.24038406
#> [14,] -0.73669918
#> [15,] -0.97348996
#> [16,] -0.50186833
#> [17,]  1.08659135
#> [18,]  2.14486935
#> [19,]  2.28297816
#> [20,]  1.11339509
#> [21,]  0.10040642
#> [22,]  3.47456298
#> [23,]  1.75776342
#> [24,]  2.61409513
#> [25,]  1.49274063
#> [26,]  1.19029818
#> [27,]  2.25928577
#> [28,]  2.62588272
#> [29,] -0.74465958
#> [30,]  1.83711640
#> [31,]  0.83662292
#> [32,] -0.61284798
#> [33,]  0.86238545
#> [34,]  1.65703622
#> [35,] -0.42119374
#> [36,]  2.59785938
#> [37,] -0.53900500
#> [38,]  2.98459760
#> [39,]  1.45445926
#> [40,]  1.07715988