Description Usage Arguments Value Author(s) Examples
Takes elicited marginal distributions and elicited concordance probabilities: pairwise probabilities of two uncertain quantities being greater than their medians, and generates a correlated sample, assuming the elicited marginal distributions and a multivariate normal copula
1  copulaSample(..., cp, n, d = NULL)

... 
A list of objects of class 
cp 
A matrix of pairwise concordance probabilities, with element i,j the elicited probability P(X_i > m_i, X_j > m_j or X_i < m_i, X_j < m_j), where m_i and m_j are the elicited medians of the uncertain quantities X_i and X_j. Only the upper triangular elements in the matrix need to be specified; the remaining elements can be set at 0. 
n 
The sample size to be generated 
d 
A vector of distributions to be used for each elicited quantity: a string with elements chosen from

A matrix of sampled values, one row per sample.
Jeremy Oakley <j.oakley@sheffield.ac.uk>
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Not run:
p1 < c(0.25, 0.5, 0.75)
v1 < c(0.5, 0.55, 0.6)
v2 < c(0.22, 0.3, 0.35)
v3 < c(0.11, 0.15, 0.2)
myfit1 < fitdist(v1, p1, 0, 1)
myfit2 < fitdist(v2, p1, 0, 1)
myfit3 < fitdist(v3, p1, 0, 1)
quad.probs < matrix(0, 3, 3)
quad.probs[1, 2] < 0.4
quad.probs[1, 3] < 0.4
quad.probs[2, 3] < 0.3
copulaSample(myfit1, myfit2, myfit3, cp=quad.probs, n=100, d=NULL)
## End(Not run)

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