ma_rmvnorm: Simulating Normally Distributed Data

In alexanderrobitzsch/miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

Simulating Normally Distributed Data

Description

Some functions for normally distributed data.

The function ma_rmvnorm is like mvtnorm::rmvnorm, but allows for a covariance matrix sigma which can have zero variances.

Usage

ma_rmvnorm(n, mu=NULL, sigma, eps=1e-10)

Arguments

n	Sample size
mu	Mean vector
sigma	Covariance matrix
eps	Trimming constant for zero variances

Value

Matrix of simulated values

See Also

MASS::mvrnorm

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Two-dimensional simulation with zero variance at dimension 1
#############################################################################

sigma <- matrix( c(0,0,0,1), nrow=2, ncol=2)
miceadds::ma_rmvnorm( n=10, sigma=sigma )

## End(Not run)

alexanderrobitzsch/miceadds documentation built on Feb. 2, 2024, 10:21 a.m.