Simulating Normally Distributed Data
Some functions for normally distributed data.
The function ma_rmvnorm is like mvtnorm::rmvnorm, but allows
for a covariance matrix sigma which can have zero variances.
ma_rmvnorm(n, mu=NULL, sigma, eps=1e-10)
n |
Sample size |
mu |
Mean vector |
sigma |
Covariance matrix |
eps |
Trimming constant for zero variances |
Matrix of simulated values
############################################################################# # EXAMPLE 1: Two-dimensional simulation with zero variance at dimension 1 ############################################################################# sigma <- matrix( c(0,0,0,1), nrow=2, ncol=2) miceadds::ma_rmvnorm( n=10, sigma=sigma )
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.