the mean of the vectors: either a single vector of length p=ncol(V) or an n by p matrix.
V
A positive semi definite covariance matrix.
Value
An n row matrix, with each row being a draw from a multivariate normal density with covariance matrix V and mean vector mu. Alternatively each row may have a different mean vector if mu is a vector.
Details
Uses a `square root' of V to transform standard normal deviates to multivariate normal with the correct covariance matrix.
# NOT RUN {library(mgcv)
V <- matrix(c(2,1,1,2),2,2)
mu <- c(1,3)
n <- 1000
z <- rmvn(n,mu,V)
crossprod(sweep(z,2,colMeans(z)))/n ## observed covariance matrixcolMeans(z) ## observed mu # }