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

 LHS R Documentation

## Latin Hypercube Sampling

### 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

```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

```
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 July 8, 2022, 1:08 a.m.