Multivariate normal and t random values simulation
Multivariate normal and t random values simulation.
rmvnorm(n, mu, sigma) rmvt(n, mu, sigma, v)
n |
The sample size, a numerical value. |
mu |
The mean vector in R^d. |
sigma |
The covariance matrix in R^d. |
v |
The degrees of freedom. |
The algorithm uses univariate normal random values and transforms them to multivariate via a spectral decomposition. It is faster than the command "mvrnorm" available from MASS, and it allows for singular covariance matrices.
A matrix with the simulated data.
Michail Tsagris
R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr>
Aitchison J. (1986). The statistical analysis of compositional data. Chapman & Hall.
x <- as.matrix(iris[, 1:4]) m <- colmeans(x) s <- cov(x) y <- rmvnorm(1000, m, s) res<-colmeans(y) res<-cov(y) y <- NULL
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