Optimum Seeded Latin Hypercube Sample

Augments an existing Latin Hypercube Sample, adding points to the design, while maintaining the latin properties of the design. This function then uses the columnwise pairwise (CP) algoritm to optimize the design. The original design is not necessarily maintained.

optSeededLHS(seed, m = 0, maxSweeps = 2, eps = 0.1, verbose = FALSE)

Arguments

seed

The number of partitions (simulations or design points)

m

The number of additional points to add to the seed matrix seed. default value is zero. If m is zero then the seed design is optimized.

maxSweeps

The maximum number of times the CP algorithm is applied to all the columns.

eps

The optimal stopping criterion

verbose

Print informational messages

Value

An n by k Latin Hypercube Sample matrix with values uniformly distributed on [0,1]

Details

Augments an existing Latin Hypercube Sample, adding points to the design, while maintaining the latin properties of the design. This function then uses the CP algoritm to optimize the design. The original design is not necessarily maintained.

References

Stein, M. (1987) Large Sample Properties of Simulations Using Latin Hypercube Sampling. Technometrics. 29, 143--151.

See also

[randomLHS()], [geneticLHS()], [improvedLHS()], [maximinLHS()], and [optimumLHS()] to generate Latin Hypercube Samples. [optAugmentLHS()] and [augmentLHS()] to modify and augment existing designs.

Examples

  set.seed(1234)
  a <- randomLHS(4,3)
  b <- optSeededLHS(a, 2, 2, .1)