# optAugmentLHS

##### Optimal Augmented Latin Hypercube Sample

Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the *latin* properties of the design. This function attempts to
add the points to the design in an optimal way.

- Keywords
- design

##### Usage

`optAugmentLHS(lhs, m = 1, mult = 2)`

##### Arguments

- lhs
The Latin Hypercube Design to which points are to be added

- m
The number of additional points to add to matrix

`lhs`

- mult
`m*mult`

random candidate points will be created.

##### Details

Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the *latin* properties of the design. This function attempts to
add the points to the design in a way that maximizes S optimality.

S-optimality seeks to maximize the mean distance from each design point to all the other points in the design, so the points are as spread out as possible.

##### Value

An `n`

by `k`

Latin Hypercube Sample matrix with values uniformly distributed on [0,1]

##### References

Stein, M. (1987) Large Sample Properties of Simulations Using Latin Hypercube Sampling.
*Technometrics*. **29**, 143--151.

##### See Also

[randomLHS()], [geneticLHS()], [improvedLHS()], [maximinLHS()], and [optimumLHS()] to generate Latin Hypercube Samples. [optSeededLHS()] and [augmentLHS()] to modify and augment existing designs.

##### Examples

```
# NOT RUN {
set.seed(1234)
a <- randomLHS(4,3)
b <- optAugmentLHS(a, 2, 3)
# }
```

*Documentation reproduced from package lhs, version 1.0.1, License: GPL-3*