normal_to_logn: Convert Normal parameters to Log-Normal parameters

In AlessandroDeCarlo27/mvlognCorrEst: Sampling from a multivariate Log-Normal distribution using Log-Normal parameters and Estimation of Indirect Correlations

Convert Normal parameters to Log-Normal parameters

Description

This function converts mean vector and covariance matrix of a multivariate Normal distribution to mean vector and covariance matrix of the associated multivariate Log-Normal distribution.

Usage

normal_to_logn(mu, covMatrix)

Arguments

mu	Array object containing means of the multivariate Normal distribution.
covMatrix	Matrix object containing covariance matrix of the multivariate Normal distribution.

Value

A list containing:

muLn	Array object. It contains mean of the multivariate Log-Normal distribution associated to the Normal one.
sigmaLn	Matrix object. It contains covariance matrix of the multivariate Log-Normal distribution associated to the Normal one.

Author(s)

Alessandro De Carlo [e-mail]

See Also

logn_to_normal

Examples

#define correlations
corr<- diag(rep(1,4))
corr[1,4] <- 0.9
corr[4,1]<-corr[1,4]
corr[2,4] <- -0.3
corr[4,2] <- corr[2,4]
corr[3,2] <- -0.2
corr[2,3] <- corr[3,2]
#define sd of variables
sd2 <- array(c(rep(1,4)))
#obtain covariance matrix
covMatrix2 <- sd2%*%t(sd2)*corr
#define mean vector
mu2 <- array(rep(2.5,4))
normal_to_logn(mu2,covMatrix2)
#output:

# $muLn
# [1] 20.08554 20.08554 20.08554 20.08554
#
# $sigmaLn
#           [,1]   [,2]      [,3]      [,4]
# [1,] 693.2044    0.00000   0.00000  588.8459
# [2,]   0.0000  693.20436 -73.12923 -104.5614
# [3,]   0.0000  -73.12923 693.20436    0.0000
# [4,] 588.8459 -104.56139   0.00000  693.2044

AlessandroDeCarlo27/mvlognCorrEst documentation built on March 23, 2023, 10:11 a.m.