Btw: I have done this for non-binary variables already. If anyone is
interested, it looks like this.
I am pretty new to R, so excuse the potentially unelegant code.
# Implement library
library(ecodist)
# Prepare ACOutcome vector
ACOutcomes = c()
# Set desired variablelength & Number of simulations
VarLength = 1000
NSim = 10000
# Set desired range for correlations
rangeAB = c(0.0, 1.0)
rangeBC = c(0.0, 1.0)
# Start n simulation runs
n = 0
while(n < NSim) {n=n+1;
# Set desired correlations between Variables
DesCorAB = runif(1, rangeAB[1], rangeAB[2])
DesCorBC = runif(1, rangeBC[1], rangeBC[2])
# Simulate A and B with desired correlation between them
DatasetAB = corgen(len=VarLength, r=DesCorAB, epsilon=0.00)
A = DatasetAB$x
B = DatasetAB$y
# Option of saving correlation between A & B
cor(A, B, method = "pearson")
# Simulate C with desired correlation to B
C = corgen(x=B, r=DesCorBC, epsilon = 0.00)$y
# Calculate correlation between A & C
corAC = cor(A, C, method = "pearson")
# Save correlation AC into vector
ACOutcomes = append(ACOutcomes, corAC)
;}
# Show results: Mean, Minimum, Maximum
hist(ACOutcomes, breaks = 100)
mean(ACOutcomes)
min(ACOutcomes)
max(ACOutcomes)
--
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