31 lines
892 B
R
31 lines
892 B
R
#' Multivariate Normal Distribution.
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#'
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#' Random generation for the multivariate normal distribution.
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#' \deqn{X \sim N_p(\mu, \Sigma)}{X ~ N_p(\mu, \Sigma)}
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#'
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#' @param n number of samples.
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#' @param mu mean
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#' @param sigma covariance matrix.
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#'
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#' @return a \eqn{n\times p}{n x p} matrix with samples in its rows.
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#'
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#' @examples
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#' \dontrun{
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#' rmvnorm(20, sigma = matrix(c(2, 1, 1, 2), 2))
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#' rmvnorm(20, mu = c(3, -1, 2))
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#' }
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#' @keywords internal
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rmvnorm <- function(n = 1, mu = rep(0, p), sigma = diag(p)) {
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if (!missing(sigma)) {
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p <- nrow(sigma)
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} else if (!missing(mu)) {
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mu <- matrix(mu, ncol = 1)
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p <- nrow(mu)
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} else {
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stop("At least one of 'mu' or 'sigma' must be supplied.")
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}
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# See: https://en.wikipedia.org/wiki/Multivariate_normal_distribution
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return(rep(mu, each = n) + matrix(rnorm(n * p), n) %*% chol(sigma))
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}
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