# LHS: Latin Hypercube Sampling In vdg: Variance Dispersion Graphs and Fraction of Design Space Plots

## 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. 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") ```

vdg documentation built on May 29, 2017, 7:18 p.m.