Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the latin properties of the design.
Usage
augmentLHS(lhs, m=1)
Arguments
lhs
The Latin Hypercube Design to which points are to be added
m
The number of additional points to add to matrix lhs
Details
Augments an existing Latin Hypercube Sample, adding points to the design, while
maintaining the latin properties of the design. Augmentation is perfomed
in a random manner.
The algorithm used by this function has the following steps.
First, create a new matrix to hold the candidate points after the design has
been re-partitioned into (n+m)^2 cells, where n is number of
points in the original lhs matrix. Then randomly sweep through each
column (1...k) in the repartitioned design to find the missing cells.
For each column (variable), randomly search for an empty row, generate a
random value that fits in that row, record the value in the new matrix.
The new matrix can contain more filled cells than m unles m = 2n,
in which case the new matrix will contain exactly m filled cells.
Finally, keep only the first m rows of the new matrix. It is guaranteed to
have m full rows in the new matrix. The deleted rows are partially full.
The additional candidate points are selected randomly due to the random search
for empty cells.
Value
An n by k Latin Hypercube Sample matrix with values uniformly distributed on [0,1]
Author(s)
Rob Carnell
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 optSeededLHS
to modify and augment existing designs.
providing 
.