Generate data with the multivariate normal (i.e., Gaussian) distribution
Function generates data from the multivariate normal distribution given some mean vector and/or covariance matrix.
rmvnorm(n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)))
n |
number of observations to generate |
mean |
mean vector, default is |
sigma |
positive definite covariance matrix, default is |
a numeric matrix with columns equal to length(mean)
Phil Chalmers rphilip.chalmers@gmail.com
Chalmers, R. P., & Adkins, M. C. (2020). Writing Effective and Reliable Monte Carlo Simulations
with the SimDesign Package. The Quantitative Methods for Psychology, 16(4), 248-280.
doi: 10.20982/tqmp.16.4.p248
Sigal, M. J., & Chalmers, R. P. (2016). Play it again: Teaching statistics with Monte
Carlo simulation. Journal of Statistics Education, 24(3), 136-156.
doi: 10.1080/10691898.2016.1246953
# random normal values with mean [5, 10] and variances [3,6], and covariance 2 sigma <- matrix(c(3,2,2,6), 2, 2) mu <- c(5,10) x <- rmvnorm(1000, mean = mu, sigma = sigma) head(x) summary(x) plot(x[,1], x[,2])
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