Draw a (random) Latin Hypercube (LH) sample of size n from in
the region outlined by the provided rectangle
lhs(n, rect, shape=NULL, mode=NULL)The output is a matrix with n rows and
nrow(rect) columns. Each of the n rows represents
a sample point.
Size of the LH sample
Rectangle describing the domain from which the LH sample
is to be taken. The rectangle should be a matrix or
data.frame with ncol(rect) = 2, and number of rows equal to the
dimension of the domain. For 1-d data, a vector of length 2
is allowed
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 with shape > 1 are supported.
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. If shape is specified,
but this is not, then the scaled Beta distributions will be symmetric
Robert B. Gramacy, rbg@vt.edu, and Matt Taddy, mataddy@amazon.com
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).
tgp.design, dopt.gp,
sens
# 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)
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