# rmvnorm: Generate data with the multivariate normal (i.e., Gaussian)... In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

## Description

Function generates data from the multivariate normal distribution given some mean vector and/or covariance matrix.

## Usage

 `1` ```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)`

## Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

## 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

`runSimulation`
 ```1 2 3 4 5 6 7``` ```# 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]) ```