Mvnorm
Multivariate Normal Density and Random Deviates
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.
- Keywords
- multivariate, distribution
Usage
dmvnorm(x, mean = rep(0, p), sigma = diag(p), log = FALSE)
rmvnorm(n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)),
method=c("eigen", "svd", "chol"), pre0.9_9994 = FALSE)Arguments
- x
vector or matrix of quantiles. If
xis 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
string specifying the matrix decomposition used to determine the matrix root of
sigma. Possible methods are eigenvalue decomposition ("eigen", default), singular value decomposition ("svd"), and Cholesky decomposition ("chol"). The Cholesky is typically fastest, not by much though.- pre0.9_9994
logical; if
FALSE, the output produced in mvtnorm versions up to 0.9-9993 is reproduced. In 0.9-9994, the output is organized such thatrmvnorm(10,...)has the same first ten rows asrmvnorm(100, ...)when called with the same seed.
See Also
Examples
# NOT RUN {
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)
# }