# Latinhyper: Latin Hypercube Sampling In FME: A Flexible Modelling Environment for Inverse Modelling, Sensitivity, Identifiability and Monte Carlo Analysis

## Description

Generates random parameter sets using a latin hypercube sampling algorithm.

## Usage

 `1` ```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 <[email protected]>

## References

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

`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

 ```1 2 3 4 5 6``` ```## 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 May 31, 2017, 5:05 a.m.