correlatedLHS: Transformed Latin hypercube with a multivariate distribution

In lhs: Latin Hypercube Samples

Transformed Latin hypercube with a multivariate distribution

Description

A method to create a transformed Latin Hypercube sample where the marginal distributions can be correlated according to an arbitrary set of criteria contained in a minimized cost function

Usage

correlatedLHS(
  lhs,
  marginal_transform_function,
  cost_function,
  debug = FALSE,
  maxiter = 10000,
  ...
)

Arguments

lhs	a Latin hypercube sample that is uniformly distributed on the margins
marginal_transform_function	a function that takes Latin hypercube sample as the first argument and other passed-through variables as desired. ... must be passed as a argument. For example, f <- function(W, second_argument, ...). Must return a matrix or data.frame
cost_function	a function that takes a transformed Latin hypercube sample as the first argument and other passed-through variables as desired. ... must be passed as a argument. For example, f <- function(W, second_argument, ...)
debug	Should debug messages be printed. Causes cost function output and iterations to be printed to aid in setting the maximum number of iterations
maxiter	the maximum number of iterations. The algorithm proceeds by swapping one variable of two points at a time. Each swap is an iteration.
...	Additional arguments to be passed through to the marginal_transform_function and cost_function

Value

a list of the Latin hypercube with uniform margins, the transformed Latin hypercube, and the final cost

Examples

correlatedLHS(lhs::randomLHS(30, 2),
  marginal_transform_function = function(W, ...) {
    W[,1] <- qnorm(W[,1], 1, 3)
    W[,2] <- qexp(W[,2], 2)
    return(W)
  },
  cost_function = function(W, ...) {
    (cor(W[,1], W[,2]) - 0.5)^2
  },
  debug = FALSE,
  maxiter = 1000)

lhs documentation built on July 1, 2024, 1:06 a.m.