SimCorrRVs: Simulation of correlated random variables.

In itsoukal/anySim: Stochastic Simulation of Processes with any Marginal Distribution and Correlation Structure

Description

Simulation of k correlated Random variables (RVs) with target marginal distributions and correlation structure.

Usage

SimCorrRVs(n = 1000, paramsRVs)

Arguments

n	A scalar indicating the number of RVs to be generated.
paramsRVs	A list generated by function "EstCorrRVs".

Value

A matrix (n x k) with the generated RVs.

Examples

## Simulation of 3 correlated RVs with Gamma, Beta and Log-Normal distribution, respectively. 
## We assume also a target correlation matrix.
## Not run: 
set.seed(13)

# Define the target distribution functions (ICDFs) of X1, X2 and X3 RV.
FX1='qgamma'; FX2='qbeta'; FX3='qlnorm'
Distr=c(FX1,FX2,FX3) # store in a vector.

# Define the parameters of the target distribution functions
# and store them in a list.
pFX1=list(shape=1.5,scale=2); pFX2=list(shape1=1.5,shape2=3)
pFX3=list(meanlog=1,sdlog=0.5)

DistrParams=list()
DistrParams[[1]]=pFX1;DistrParams[[2]]=pFX2;DistrParams[[3]]=pFX3

# Define the target correlation matrix.
CorrelMat=matrix(c(1,0.7,0.5,
                   0.7,1,0.8,
                   0.5,0.8,1),ncol=3,nrow=3,byrow=T);
            
# Estimate the parameters of the auxiliary Gaussian model.
paramsRVs=EstCorrRVs(R=CorrelMat,dist=Distr,params=DistrParams, 
                        NatafIntMethod='GH',NoEval=9,polydeg=8)

# Generate 10000 synthetic realisations of the 3 correlated RVs.
SynthRVs=SimCorrRVs(n=10000,paramsRVs=paramsRVs)

# Comparison of target and simulation correlation matrix.
pairs(SynthRVs)
CorrelMatSim=cor(SynthRVs)
difference=(CorrelMat-CorrelMatSim)^2; print(difference)

## End(Not run)

itsoukal/anySim documentation built on May 7, 2020, 11:57 p.m.