Description Usage Arguments Details Value References See Also Examples
Random number generation from the multivariate normal (Gaussian) distribution.
1 |
n |
the number of samples requested |
mean |
a vector giving the means of each variable |
Sigma |
a positive-definite covariance matrix |
The function rmnorm
is an interface to C routines, which make calls to
subroutines from LAPACK. The matrix decomposition is internally done using
the Cholesky decomposition. If Sigma
is not non-negative definite then
there will be a warning message.
If n = 1
a vector of the same length as mean
, otherwise a
matrix of n
rows of random vectors.
Devroye, L. (1986). Non-Uniform Random Variate Generation. Springer-Verlag, New York.
1 2 3 4 5 6 7 8 9 10 11 12 13 | # covariance parameters
Sigma <- matrix(c(10,3,3,2), ncol = 2)
Sigma
# generate the sample
y <- rmnorm(n = 1000, Sigma = Sigma)
var(y)
# scatterplot of a random bivariate normal sample with mean
# vector zero and covariance matrix 'Sigma'
par(pty = "s")
plot(y, xlab = "", ylab = "")
title("bivariate normal sample", font.main = 1)
|
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