mvnorm: The Multivariate Normal Distribution
In lcmix: Layered and chained mixture models

Description Usage Arguments Details Value Author(s) References See Also Examples

Density and random generation functions for the multivariate normal distribution.

1 2	dmvnorm(x, mean=rep(0, ncol(x)), cov=diag(ncol(x)), log=FALSE) rmvnorm(n, mean=NULL, cov=NULL)

`x`	a numeric matrix of which each row represents an observation.
`mean`	a vector of column means.
`cov`	a covariance matrix.
`log`	logical; if `TRUE`, density is given as the log-density.
`n`	number of vectors to simulate.

See Evans et al. (2000) for one of many references on the multivariate normal density.

For rmvnorm, if the mean argument is NULL, then the scalar 0 will be used by default, unless cov is not NULL, in which case mean = rep(0, nrow(cov)) will be used. If the cov argument is NULL, then diag(length(mean)) will be used by default. Thus rmvnorm(n) is equivalent to rnorm(n).

For dmvnorm, a vector of densities. For rmvnorm, a vector with n rows and length(mean) columns representing a sample from the multivariate normal distribution with the specified parameters.

Daniel Dvorkin

Evans, M., Hastings, N., and Peacock, B. (2000) Statistical Distributions, third edition, John Wiley & Sons.

mvgamma, mvweisd for related distributions; Normal in package stats; dmvnorm and rmvnorm in package mixtools.

set.seed(123)

m = c(-3, 0, 3)
v = cov(matrix(rnorm(90), ncol=3))

x = rmvnorm(5, m, v)
print(x)
#            [,1]       [,2]     [,3]
# [1,] -2.0498637  0.3599609 3.218045
# [2,] -3.7479240  1.2302183 2.290916
# [3,] -0.9852752  1.1433559 2.790147
# [4,] -3.9353966 -0.5451158 3.223321
# [5,] -3.2864769 -0.1672326 2.201353

dmvnorm(x, m, v)
# 0.048079901 0.025591976 0.002587824 0.041810685 0.054688032