Latin Hypercube Sampling
Generates random parameter sets using a latin hypercube sampling algorithm.
Latinhyper(parRange, num)
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. |
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.
a matrix with one row for each generated parameter set, and one column per parameter.
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.
Karline Soetaert <karline.soetaert@nioz.nl>
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.
## 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")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.