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.

Usage

rmvnorm(n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)))

Arguments

n: number of observations to generate
mean: mean vector, default is rep(0, length = ncol(sigma))
sigma: positive definite covariance matrix, default is diag(length(mean))

Value

a numeric matrix with columns equal to length(mean)

References

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

Author

Phil Chalmers rphilip.chalmers@gmail.com

Examples


# 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)
#>          [,1]      [,2]
#> [1,] 6.060771 11.172431
#> [2,] 4.222922  9.918127
#> [3,] 6.189192  7.146768
#> [4,] 6.328861  7.243104
#> [5,] 4.874275 12.614418
#> [6,] 2.573901  9.202745
summary(x)
#>        V1                  V2        
#>  Min.   : 0.008016   Min.   : 1.091  
#>  1st Qu.: 3.779530   1st Qu.: 8.364  
#>  Median : 5.018941   Median :10.009  
#>  Mean   : 4.927963   Mean   : 9.975  
#>  3rd Qu.: 6.062034   3rd Qu.:11.546  
#>  Max.   :10.018291   Max.   :17.717  
plot(x[,1], x[,2])