Multivariable Random Normal data
Generate a simulated multivariable random normally distributed dataset using the method of Cholesky Decomposition.
randnor(n, mu, Cov)
n |
the number of rows of observations in the dataset |
mu |
a vector of length m containing the column means of the dataset |
Cov |
an m x m covariance matrix |
A simulated matrix of values based on the input parameters is returned.
Rizzo M. L., "Statistical Computing with R", Chapman & Hall/CRC (2007)
## Simulated data based on the iris dataset mu <- c(rep(0, 4)) covmatr <- matrix(c(0.7, -0.04, 1.3, 0.5, -0.04, 0.2, -0.3, -0.1, 1.3, -0.3, 3.1, 1.3, 0.5, -0.1, 1.3, 0.6), ncol = 4) sim.dat <- randnor(n = 100, mu = mu, Cov = covmatr) head(sim.dat)
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