rmvnorm: Generate data with the multivariate normal (i.e., Gaussian)...

In SimDesign: Structure for Organizing Monte Carlo Simulation Designs

Generate data with the multivariate normal (i.e., Gaussian) distribution

Description

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)

Author(s)

Phil Chalmers [e-mail]

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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10691898.2016.1246953")}

See Also

runSimulation

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)
summary(x)
plot(x[,1], x[,2])

SimDesign documentation built on Sept. 11, 2024, 8 p.m.