randomLHS {lhs} | R Documentation |
Draws a Latin Hypercube Sample from a set of uniform distributions for use in creating a Latin Hypercube Design. This sample is taken in a random manner without regard to optimization.
randomLHS(n, k)
n |
The number of partitions (simulations or design points) |
k |
The number of replications (variables) |
Latin hypercube sampling (LHS) was developed to generate a distribution
of collections of parameter values from a multidimensional distribution.
A square grid containing possible sample points is a Latin square iff there
is only one sample in each row and each column. A Latin hypercube is the
generalisation of this concept to an arbitrary number of dimensions. When
sampling a function of k
variables, the range of each variable is divided
into n
equally probable intervals. n
sample points are then drawn such that a
Latin Hypercube is created. Latin Hypercube sampling generates more efficient
estimates of desired parameters than simple Monte Carlo sampling.
This program generates a Latin Hypercube Sample by creating random permutations
of the first n
integers in each of k
columns and then transforming those
integers into n sections of a standard uniform distribution. Random values are
then sampled from within each of the n sections. Once the sample is generated,
the uniform sample from a column can be transformed to any distribution by
using the quantile functions, e.g. qnorm(). Different columns can have
different distributions.
An n
by k
Latin Hypercube Sample matrix with values uniformly distributed on [0,1]
Rob Carnell and D. Mooney
Stein, M. (1987) Large Sample Properties of Simulations Using Latin Hypercube Sampling. Technometrics. 29, 143–151.
geneticLHS
,
improvedLHS
, maximinLHS
, and
optimumLHS
to generate Latin Hypercube Samples.
optAugmentLHS
, optSeededLHS
, and
augmentLHS
to modify and augment existing designs.
randomLHS(4, 3)