# optSeededLHS

##### Optimum Seeded Latin Hypercube Sample

Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the *latin* properties of the design. This function then uses the
columnwise pairwise (CP) algoritm to optimize the design. The original design is not necessarily maintained.

- Keywords
- design

##### Usage

```
optSeededLHS(seed, m = 0, maxSweeps = 2, eps = 0.1,
verbose = FALSE)
```

##### Arguments

- seed
The number of partitions (simulations or design points)

- m
The number of additional points to add to the seed matrix

`seed`

. default value is zero. If m is zero then the seed design is optimized.- maxSweeps
The maximum number of times the CP algorithm is applied to all the columns.

- eps
The optimal stopping criterion

- verbose
Print informational messages

##### Details

Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the *latin* properties of the design. This function then uses the
CP algoritm to optimize the design. The original design
is not necessarily maintained.

##### 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. [optAugmentLHS()] and [augmentLHS()] to modify and augment existing designs.

##### Examples

```
# NOT RUN {
set.seed(1234)
a <- randomLHS(4,3)
b <- optSeededLHS(a, 2, 2, .1)
# }
```

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