SHELF (version 1.2.0)

copulaSample: Generate correlated samples from elicited marginal distributions using a multivariate normal copula

Description

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

Usage

copulaSample(..., cp, n, d = NULL)

Arguments

...

A list of objects of class elicitation. command, one per marginal distribution, separated by commas.

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 "Normal", "Student-t", "Gamma", "Log normal" "Log Student-t", "Beta". The default is to use the best fitting distribution in each case.

Value

A matrix of sampled values, one row per sample.

Examples

## 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, qp=quad.probs, n=100, d=NULL)
# ## End(Not run)