Latin Hypercube Sampling

Share:

Description

Different versions of latin hypercube sampling (LHS): ordinary LHS, midpoint LHS, symmetric LHS or randomized symmetric LHS. LHS is a method for constructing space-filling designs. They can be more efficient for hypercuboidal design regions than other sampling methods.

Usage

1
2
3
4
5
6
7
LHS(n, m = 3, lim = c(-1, 1))

MLHS(n, m = 3, lim = c(-1, 1))

SLHS(n, m = 3, lim = c(-1, 1))

RSLHS(n, m = 3, lim = c(-1, 1))

Arguments

n

number of design points to generate

m

number of design factors

lim

limits of the coordinates in all dimensions

Value

Matrix with samples as rows.

Author(s)

Pieter C. Schoonees

References

Pieter C. Schoonees, Niel J. le Roux, Roelof L.J. Coetzer (2016). Flexible Graphical Assessment of Experimental Designs in R: The vdg Package. Journal of Statistical Software, 74(3), 1-22. \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.18637/jss.v074.i03")}.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
set.seed(1234)
pts <- seq(-1, 1, length = 11)

# Ordinary LHS
samp <- LHS(n = 10, m = 2)
plot(samp, main = "LHS")
abline(h = pts, v = pts, col = "lightgrey")

# Midpoint LHS
samp <- MLHS(n = 10, m = 2)
plot(samp, main = "MLHS")
abline(h = pts, v = pts, col = "lightgrey")

# Symmetric LHS
samp <- SLHS(n = 10, m = 2)
plot(samp, main = "SLHS")
abline(h = pts, v = pts, col = "lightgrey")

# Randomized Symmetric LHS
samp <- RSLHS(n = 10, m = 2)
plot(samp, main = "RSLHS")
abline(h = pts, v = pts, col = "lightgrey")