LA_LHDC {LA}

Lioness Algorithm for Latin hypercube design

Description

LA_LHDC returns an optimal n by k Latin hypercube design (LHD) matrix generated by lioness algorithm (LA)

Usage

LA_LHDC(n, k, m = 100L, N = 5000L, OC = "phi_p", p = 15L, q = 1L)

permuC(x)

seqC(a, b)

rLHDC(n, k)

dijC(X, i, j, q = 1L)

phi_pC(X, p = 15L, q = 1L)

MaxProCriterionC(X)

corC(x, y)

MaxAbsCorC(X)

AvgAbsCorC(X)

exchangeC(X, j, type = "col")

Arguments

n

A positive integer that stands for the number of rows (or run size).

k

A positive integer that stands for the number of columns (or factor size).

m

A positive integer that stands for the number of starting design candidates. The default is set to be 100.

N

A positive integer that stands for the maximum number of iterations. The default is set to be 5000. A larger value of N may result a high CPU time.

OC

An optimality criterion. The default setting is "phi_p" (for space-filling LHDs), and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion".

p

A positive integer that is the parameter in the phi_p formula, and p is preferred to be large. The default and recommended value is 15.

q

The default is set to be 1, and it could be either 1 or 2. If q is 1, the Manhattan (rectangular) distance will be used. If q is 2, the Euclidean distance will be used.

x

A vector.

a

is the starting value of the sequence.

b

is the ending value of the sequence.

X

A matrix object. In general, X stands for a design matrix.

i

A positive integer, which stands for the ith row of X.

j

A positive integer, which stands for the jth column (or row) of X, and it should be within [1,ncol(X)] (or [1,nrow(X)]).

y

A vector.

type

An exchange type. If type is "col" (the default setting), two random elements will be exchanged within column j. If type is "row", two random elements will be exchanged within row j.

Value

If all inputs are logical, then the output will be an optimal n by k LHD.

Examples

#generate a 6 by 3 maximin distance LHD with the default setting
try=LA_LHDC(n=6,k=3)
try

#Another example
#generate a 9 by 4 nearly orthogonal LHD
try2=LA_LHDC(n=9,k=4,OC="AvgAbsCor")
try2