# copulaSample: Generate correlated samples from elicited marginal... In OakleyJ/SHELF: Tools to Support the Sheffield Elicitation Framework

## 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

 `1` ```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", "t", "gamma", "lognormal", "logt", "beta"`. The default is to use the best fitting distribution in each case.

## Value

A matrix of sampled values, one row per sample.

## Author(s)

Jeremy Oakley <j.oakley@sheffield.ac.uk>

## Examples

 ``` 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) ```

OakleyJ/SHELF documentation built on June 21, 2021, 1:24 a.m.