Augments an existing Latin Hypercube Sample, adding points to the design, while maintaining the latin properties of the design.
augmentLHS(lhs, m = 1)The Latin Hypercube Design to which points are to be added. Contains an existing latin hypercube design with a number of rows equal to the points in the design (simulations) and a number of columns equal to the number of variables (parameters). The values of each cell must be between 0 and 1 and uniformly distributed
The number of additional points to add to matrix lhs
An n by k Latin Hypercube Sample matrix with values
uniformly distributed on [0,1]
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.
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.