LHS | R Documentation |

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.

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))

`n` |
number of design points to generate |

`m` |
number of design factors |

`lim` |
limits of the coordinates in all dimensions |

Matrix with samples as rows.

Pieter C. Schoonees

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.
doi: 10.18637/jss.v074.i03.

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")

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