| Mvnorm {mvtnorm} | R Documentation |
These functions provide the density function and a random number
generator for the multivariate normal
distribution with mean equal to mean and covariance matrix
sigma.
dmvnorm(x, mean, sigma, log=FALSE)
rmvnorm(n, mean, sigma, method=c("svd", "chol"))
x |
Vector or matrix of quantiles. If x is a matrix, each
row is taken to be a quantile. |
n |
Number of observations. |
mean |
Mean vector, default is rep(0, length = ncol(x)). |
sigma |
Covariance matrix, default is diag(ncol(x)). |
log |
Logical; if TRUE, densities d are given as log(d). |
method |
Matrix decomposition used to determine the matrix root of
sigma, possible methods are singular value decomposition
("svd", default) and Cholesky decomposition ("chol"). |
SVD will work even for numerically negative definite sigma
(with a warning), while the Cholesky decomposition is possible only
for non-negative
definite sigma and issues a warning for positive
semi-definiteness.
Using method = "chol" may be faster and more stable for higher
dimensional sigma.
Friedrich Leisch <Friedrich.Leisch@R-project.org>, Fabian Scheipl
dmvnorm(x=c(0,0)) dmvnorm(x=c(0,0), mean=c(1,1)) sigma <- matrix(c(4,2,2,3), ncol=2) x <- rmvnorm(n=500, mean=c(1,2), sigma=sigma) colMeans(x) var(x) x <- rmvnorm(n=500, mean=c(1,2), sigma=sigma, method="chol") colMeans(x) var(x) plot(x)