SimComp (version 3.3)

ermvnorm: Multivariate Normal Random Numbers with Exact Parameters

Description

Random numbers of the multivariate normal distribution with EXACT mean vector, EXACT variance vector and approximate correlation matrix. This function is based on the function rmvnorm of the package mvtnorm.

Usage

ermvnorm(n, mean, sd, corr = diag(rep(1, length(mean))), mnt = 10000)

Arguments

n

a number of observations

mean

a mean vector

sd

a vector of standard deviations

corr

a correlation matrix

mnt

a maximum number of tries for the computation

Value

A matrix of random numbers with dimension n * length(mean).

Details

Unfortunately, it's very common to present only summary statistics in the literature when evaluating real data. This makes it hard to retrace or to verify the related statistical evaluation. Also, the use of such data as an example for other statistical tests is hardly possible. For that reason, ermvnorm allows to reproduce data by simulation. In contrast to rmvnorm of the package mvtnorm, the function ermvnorm produces random numbers which have EXACTLY the same paramer values as specified by mean and sd. The correlation matrix corr is met only approximately.

The simple idea behind ermvnorm is to apply rmvnorm of the package mvtnorm, but only for the first n-2 random numbers. The remaining 2 numbers are obtained by solving a quadratic equation to achieve the specified values for the mean vector and for the vector of standard deviations. Depending on the n-2 random numbers, the underlying quadratic equation can possibly have no solution. In this case, ermvnorm creates a new set of n-2 random numbers until a valid data set is obtained, or until the maximum number of tries mnt is reached.

References

Hothorn, T. et al. (2001): On Multivariate t and Gauss Probabilities in R. R News 1, 27--29.

See Also

rmvnorm, rcm

Examples

# NOT RUN {
# Example 1:
# A dataset representing one endpoint.

set.seed(1234)
dataset1 <- ermvnorm(n=10,mean=100,sd=10)
dataset1
mean(dataset1)
sd(dataset1)

# Example 2:
# A dataset representing two correlated endpoints.

set.seed(5678)
dataset2 <- ermvnorm(n=10,mean=c(10,120),sd=c(1,10),corr=rbind(c(1,0.7),c(0.7,1)))
dataset2
mean(dataset2[,1]); mean(dataset2[,2])
sd(dataset2[,1]); sd(dataset2[,2])
round(cor(dataset2),3)
pairs(dataset2)

# Example 3:
# A dataset representing three uncorrelated endpoints.

set.seed(9101)
dataset3 <- ermvnorm(n=20,mean=c(1,12,150),sd=c(0.5,2,20))
dataset3
mean(dataset3[,1]); mean(dataset3[,2]); mean(dataset3[,3])
sd(dataset3[,1]); sd(dataset3[,2]); sd(dataset3[,3])
pairs(dataset3)

# Example 4:
# A dataset representing four randomly correlated endpoints.

set.seed(1121)
dataset4 <- ermvnorm(n=10,mean=c(2,10,50,120),sd=c(1,4,8,10),corr=rcm(ncol=4))
dataset4
mean(dataset4[,1]); mean(dataset4[,2]); mean(dataset4[,3]); mean(dataset4[,4])
sd(dataset4[,1]); sd(dataset4[,2]); sd(dataset4[,3]); sd(dataset4[,4])
round(cor(dataset4),3)
pairs(dataset4)
# }