R help - Mar 2007 - simulate from a multivariate lognormal distribution with fixed covariance/correlation structure

Dear R users
 
I use simulated data to evaluate a model by sampling the parameters in
my model from lognormal distributions.
 
I would like these (lognormal distributed) parameters to be correlated,
that is, I would like to have pairwise samples of 2 parameters with a
given correlation coefficient.
 
I have seen that a covariance matrix can be fixed when generating random
variables from a multivariate normal distribution e.g. by the function
mvrnorm.
 
Is there a function to do the same (as illustrated below from a
multivariate normal) from a multivariate lognormal distribution?
 
Thank you!
 
Fabian
 
 

library(MASS)
corab<-0.8
a.mu<-2; a.sd<-1
b.mu<-1; b.sd<-0.5
sigma<-matrix(c(a.sd^2,corab*a.sd*b.sd,corab*a.sd*b.sd,b.sd^2),nrow=2,nc
ol=2,byrow=T)
mvn<-mvrnorm(n = 1000, mu=c(a.mu,b.mu), Sigma=sigma, tol = 1e-6,
empirical = TRUE)
avar<-mvn[,1]
bvar<-mvn[,2]
cor(avar,bvar)

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