lhs
Latin Hypercube sampling
Draw a (random) Latin Hypercube (LH) sample of size n from in
the region outlined by the provided rectangle
Usage
lhs(n, rect, shape=NULL, mode=NULL)Arguments
- n
Size of the LH sample
- rect
Rectangle describing the domain from which the LH sample is to be taken. The rectangle should be a
matrixordata.framewithncol(rect) = 2, and number of rows equal to the dimension of the domain. For 1-d data, a vector of length 2 is allowed- shape
Optional vector of shape parameters for the Beta distribution. Vector of length equal to the dimension of the domain, with elements > 1. If this is specified, the LH sample is proportional to a joint pdf formed by independent Beta distributions in each dimension of the domain, scaled and shifted to have support defined by
rect. Only concave Beta distributions withshape> 1 are supported.- mode
Optional vector of mode values for the Beta distribution. Vector of length equal to the dimension of the domain, with elements within the support defined by
rect. Ifshapeis specified, but this is not, then the scaled Beta distributions will be symmetric
Value
The output is a matrix with n rows and
nrow(rect) columns. Each of the n rows represents
a sample point.
Note
The domain bounds specified by the rows of rect can
be specified backwards with no change in effect.
References
Gramacy, R. B. (2020) Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences. Boca Raton, Florida: Chapman Hall/CRC. (See Chapter 4.) https://bobby.gramacy.com/surrogates/
McKay, M. D., W. J. Conover and R. J. Beckman. (1979). A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code, Technometrics 21: (pp. 239--245).
See Also
Examples
# NOT RUN {
# get and plot a 2-d LH design
s1 <- lhs(10, rbind(c(-2,3), c(0.5, 0.8)))
plot(s1)
# plot a grid to show that there is one sample
# in each grid location
abline(v=seq(-2,3,length=11), lty=2, col=3)
abline(h=seq(0.5,0.8,length=11), lty=2, col=3)
# }