sim1.R

#' Simulate a single data set consisting of n subjects and 2 phenotypes for each
#'
#' @param X design matrix (incorporating genotype probabilities from two loci), 2n by 2f
#' @param B a matrix of allele effects, f rows by 2 columns
#' @param Vg a genetic covariance matrix, 2 by 2
#' @param Ve an error covariance matrix, 2 by 2
#' @param kinship a chromosome-specific kinship matrix, n by n
#' @export

sim1 <- function(X, B, Vg, Ve, kinship) {
    stopifnot(nrow(kinship) == ncol(kinship), nrow(Vg) == nrow(Ve), ncol(Vg) == ncol(Ve), nrow(Vg) == 
        ncol(Vg), nrow(Ve) == ncol(Ve))
    Sigma <- calc_Sigma(Vg, Ve, kinship)
    n_mouse <- nrow(kinship)
    error_vec <- MASS::mvrnorm(n = 1, mu = rep(0, 2 * n_mouse), Sigma = Sigma)
    Y <- X %*% as.matrix(as.vector(B)) + error_vec
    return(Y)
}