augmentLHS {lhs} | R Documentation |
Augments an existing Latin Hypercube Sample, adding points to the design, while maintaining the latin properties of the design.
augmentLHS(lhs, m=1)
lhs |
The Latin Hypercube Design to which points are to be added |
m |
The number of additional points to add to matrix lhs |
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.
An n
by k
Latin Hypercube Sample matrix with values uniformly distributed on [0,1]
Rob Carnell
Stein, M. (1987) Large Sample Properties of Simulations Using Latin Hypercube Sampling. Technometrics. 29, 143–151.
randomLHS
, geneticLHS
,
improvedLHS
, maximinLHS
, and
optimumLHS
to generate Latin Hypercube Samples.
optAugmentLHS
and optSeededLHS
to modify and augment existing designs.
a <- randomLHS(4,3) a augmentLHS(a, 2)