Latinhyper: Latin Hypercube Sampling

In FME: A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability and Monte Carlo Analysis

Latin Hypercube Sampling

Description

Generates random parameter sets using a latin hypercube sampling algorithm.

Usage

Latinhyper(parRange, num)

Arguments

parRange	the range (min, max) of the parameters, a matrix or a data.frame with one row for each parameter, and two columns with the minimum (1st) and maximum (2nd) column.
num	the number of random parameter sets to generate.

Details

In the latin hypercube sampling, the space for each parameter is subdivided into num equally-sized segments and one parameter value in each of the segments drawn randomly.

Value

a matrix with one row for each generated parameter set, and one column per parameter.

Note

The latin hypercube distributed parameter sets give better coverage in parameter space than the uniform random design (Unif). It is a reasonable choice in case the number of parameter sets is limited.

Author(s)

Karline Soetaert <[e-mail]>

References

Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (2007) Numerical Recipes in C. Cambridge University Press.

See Also

Norm for (multi)normally distributed random parameter sets.

Unif for uniformly distributed random parameter sets.

Grid to generate random parameter sets arranged on a regular grid.

Examples

## 4 parameters
parRange <- data.frame(min = c(0, 1, 2, 3), max = c(10, 9, 8, 7))
rownames(parRange) <- c("par1", "par2", "par3", "par4")

## Latin hypercube
pairs(Latinhyper(parRange, 100), main = "Latin hypercube")

FME documentation built on July 9, 2023, 5:59 p.m.