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lhs
provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.
You can install the released version of lhs
from CRAN with:
install.packages("lhs")
You can also install the development version of lhs
from github with:
if (!require(devtools)) install.packages("devtools")
devtools::install_github("bertcarnell/lhs")
Create a random LHS with 10 samples and 3 variables:
Create a design that is more optimal than the random case:
X_gen <- geneticLHS(10, 3, pop = 100, gen = 5, pMut = 0.1)
X_max1 <- maximinLHS(10, 3, method = "build", dup = 5)
X_max2 <- maximinLHS(10, 3, method = "iterative", optimize.on = "result", eps = 0.01, maxIter = 300)
X_imp <- improvedLHS(10, 3, dup = 5)
X_opt <- optimumLHS(10, 3, maxSweeps = 10, eps = 0.01)
Method | Mean Distance | Minimum Distance | |
---|---|---|---|
6 | optimum | 0.7289 | 0.4598 |
2 | genetic | 0.7190 | 0.4059 |
4 | maximin | 0.7246 | 0.3975 |
5 | improved | 0.7028 | 0.3872 |
3 | maximin | 0.7296 | 0.3611 |
1 | random | 0.7067 | 0.2709 |
Augment an existing design:
Y <- randomLHS(10, 5)
Z <- augmentLHS(Y, 2)
dim(Z)
Build an orthogonal array LHS:
# a 9 row design is returned because a 10 row design is not possible with these algorithms
W9 <- create_oalhs(10, 3, bChooseLargerDesign = FALSE, bverbose = FALSE)
dim(W9)
# a 16 row design is returned because a 10 row design is not possible with these algorithms
W16 <- create_oalhs(10, 3, bChooseLargerDesign = TRUE, bverbose = FALSE)
dim(W16)
R-Help Examples of using the LHS package
StackExchange Examples:
lhs package announcement: R-pkgs New R-Packages: Triangle and LHS