| Title: | Latin Hypercube Designs (LHDs) |
|---|---|
| Description: | Contains different algorithms and construction methods for optimal Latin hypercube designs (LHDs) with flexible sizes. Our package is comprehensive since it is capable of generating maximin distance LHDs, maximum projection LHDs, and orthogonal and nearly orthogonal LHDs. Detailed comparisons and summary of all the algorithms and construction methods in this package can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <arXiv:2010.09154>. This package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, design of computer experiments. |
| Authors: | Hongzhi Wang [aut, cre], Qian Xiao [aut], Abhyuday Mandal [aut] |
| Maintainer: | Hongzhi Wang <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.3.3 |
| Built: | 2024-07-22 05:59:08 UTC |
| Source: | CRAN |
AvgAbsCor returns the average absolute correlation of a matrix
AvgAbsCor(X)AvgAbsCor(X)
X |
A matrix object. In general, |
If all inputs are logical, then the output will be a positive number indicating the average absolute correlation of input matrix.
average absolute correlation = \frac{2 \sum_{i=1}^{k-1} \sum_{j=i+1}^{k}|q_{ij}|}{k(k-1)}
Georgiou, S. D. (2009) Orthogonal Latin hypercube designs from generalized orthogonal designs. Journal of Statistical Planning and Inference, 139, 1530-1540.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the average absolute correlation of toy AvgAbsCor(X=toy)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the average absolute correlation of toy AvgAbsCor(X=toy)
dij returns the inter-site distance of two design points of an LHD
dij(X, i, j, q = 1)dij(X, i, j, q = 1)
X |
A matrix object. In general, |
i |
A positive integer, which stands for the i^th row of |
j |
A positive integer, which stands for the j^th row of |
q |
The default is set to be 1, and it could be either 1 or 2. If |
If all inputs are logical, then the output will be a positive number indicating the distance. dij = \left\{ \sum_{k=1}^{m} \vert x_{ik}-x_{jk}\vert ^q \right\}^{1/q}
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the inter-site distance of the 2nd and the 4th row of toy (with default q) dij(X=toy,i=2,j=4) #Calculate the inter-site distance of the 2nd and the 4th row of toy (with q=2) dij(X=toy,i=2,j=4,q=2)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the inter-site distance of the 2nd and the 4th row of toy (with default q) dij(X=toy,i=2,j=4) #Calculate the inter-site distance of the 2nd and the 4th row of toy (with q=2) dij(X=toy,i=2,j=4,q=2)
exchange returns a new matrix by switching two randomly selected elements from a user-defined matrix
exchange(X, j, type = "col")exchange(X, j, type = "col")
X |
A matrix object. In general, |
j |
A positive integer, which stands for the j^th column (or row) of |
type |
An exchange type. If |
If all inputs are logical, then the output will be a new design matrix after the exchange.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Choose the first column of toy and exchange two randomly selected elements. try.col=exchange(X=toy,j=1,type="col") toy;try.col #Choose the first row of toy and exchange two randomly selected elements. try.row=exchange(X=toy,j=1,type="row") toy;try.row#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Choose the first column of toy and exchange two randomly selected elements. try.col=exchange(X=toy,j=1,type="col") toy;try.col #Choose the first row of toy and exchange two randomly selected elements. try.row=exchange(X=toy,j=1,type="row") toy;try.row
FastMmLHD returns a n by k maximin distance LHD matrix generated by the construction method of Wang, L., Xiao, Q., and Xu, H. (2018)
FastMmLHD(n, k, method = "manhattan", t1 = 10)FastMmLHD(n, k, method = "manhattan", t1 = 10)
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
method |
A distance measure method. The default setting is "manhattan", and it could be one of the following: "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given. |
t1 |
A tunning parameter, which determines how many repeats will be implemented to search for the optimal design. The default is set to be 10. |
If all inputs are logical, then the output will be a n by k maximin distance LHD under under the maximin L_1 distance criterion..
Wang, L., Xiao, Q., and Xu, H. (2018) Optimal maximin $L_1$-distance Latin hypercube designs based on good lattice point designs. The Annals of Statistics, 46(6B), 3741-3766.
#n by n design when 2n+1 is prime try=FastMmLHD(8,8) try phi_p(try) #calculate the phi_p of "try". #n by n design when n+1 is prime try2=FastMmLHD(12,12) try2 phi_p(try2) #calculate the phi_p of "try2". #n by n-1 design when n is prime try3=FastMmLHD(7,6) try3 phi_p(try3) #calculate the phi_p of "try3". #General cases try4=FastMmLHD(24,8) try4 phi_p(try4) #calculate the phi_p of "try4".#n by n design when 2n+1 is prime try=FastMmLHD(8,8) try phi_p(try) #calculate the phi_p of "try". #n by n design when n+1 is prime try2=FastMmLHD(12,12) try2 phi_p(try2) #calculate the phi_p of "try2". #n by n-1 design when n is prime try3=FastMmLHD(7,6) try3 phi_p(try3) #calculate the phi_p of "try3". #General cases try4=FastMmLHD(24,8) try4 phi_p(try4) #calculate the phi_p of "try4".
GA returns a n by k LHD matrix generated by genetic algorithm (GA)
GA( n, k, m = 10, N = 10, pmut = 1/(k - 1), OC = "phi_p", p = 15, q = 1, maxtime = 5 )GA( n, k, m = 10, N = 10, pmut = 1/(k - 1), OC = "phi_p", p = 15, q = 1, maxtime = 5 )
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
m |
A positive even integer, which stands for the population size and it must be an even number. The default is set to be 10. A large value of |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
pmut |
A probability, which stands for the probability of mutation. The default is set to be 1/( |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
If all inputs are logical, then the output will be a n by k LHD.
Liefvendahl, M., and Stocki, R. (2006) A study on algorithms for optimization of Latin hypercubes. Journal of Statistical Planning and Inference, 136, 3231-3247.
#generate a 5 by 3 maximin distance LHD with the default setting try=GA(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=GA(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.#generate a 5 by 3 maximin distance LHD with the default setting try=GA(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=GA(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.
GLP returns a n by k design matrix generated by good lattice point (GLP)
GLP(n, k, h = sample(seq(from = 1, to = (n - 1)), k))GLP(n, k, h = sample(seq(from = 1, to = (n - 1)), k))
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
h |
A vector whose length is |
If all inputs are logical, then the output will be a n by k GLP design matrix.
Korobov, A.N. (1959) The approximate computation of multiple integrals. Dokl. Akad. Nauk SSSR, 124, 1207-1210.
#generate a 5 by 3 GLP design with the default setting try=GLP(n=5,k=3) try #Another example #generate a 8 by 4 GLP design with given h vector try2=GLP(n=8,k=4,h=c(1,3,5,7)) try2#generate a 5 by 3 GLP design with the default setting try=GLP(n=5,k=3) try #Another example #generate a 8 by 4 GLP design with given h vector try2=GLP(n=8,k=4,h=c(1,3,5,7)) try2
LaPSO returns a n by k LHD matrix generated by particle swarm optimization algorithm (PSO)
LaPSO( n, k, m = 10, N = 10, SameNumP = 0, SameNumG = n/4, p0 = 1/(k - 1), OC = "phi_p", p = 15, q = 1, maxtime = 5 )LaPSO( n, k, m = 10, N = 10, SameNumP = 0, SameNumG = n/4, p0 = 1/(k - 1), OC = "phi_p", p = 15, q = 1, maxtime = 5 )
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
m |
A positive integer, which stands for the number of particles. The default is set to be 10. A large value of |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
SameNumP |
A non-negative integer, which stands for how many elements in current column of current particle LHD should be the same as corresponding Personal Best. SameNumP=0, 1, 2, ..., n, and 0 means to skip the "exchange". The default is set to be 0. |
SameNumG |
A non-negative integer, which stands for how many elements in current column of current particle LHD should be the same as corresponding Global Best. SameNumP=0, 1, 2, ..., n, and 0 means to skip the "exchange". The default is set to be |
p0 |
A probability of exchanging two randomly selected elements in current column of current particle LHD. The default is set to be 1/( |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
If all inputs are logical, then the output will be a n by k LHD. Here are some general suggestions about the parameters:
SameNumP is approximately n/2 when SameNumG is 0.
SameNumG is approximately n/4 when SameNumP is 0.
p0 * (k - 1) = 1 to 2 is often sufficient. So p0 = 1/(k - 1) to 2/(k - 1).
Chen, R.-B., Hsieh, D.-N., Hung, Y., and Wang, W. (2013) Optimizing Latin hypercube designs by particle swarm. Stat. Comput., 23, 663-676.
#generate a 5 by 3 maximin distance LHD with the default setting try=LaPSO(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=LaPSO(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.#generate a 5 by 3 maximin distance LHD with the default setting try=LaPSO(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=LaPSO(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.
MaxAbsCor returns the maximum absolute correlation of a matrix
MaxAbsCor(X)MaxAbsCor(X)
X |
A matrix object. In general, |
If all inputs are logical, then the output will be a positive number indicating maximum absolute correlation.
maximum absolute correlation = max_{ij} |q_{ij}|
Georgiou, S. D. (2009) Orthogonal Latin hypercube designs from generalized orthogonal designs. Journal of Statistical Planning and Inference, 139, 1530-1540.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the maximum absolute correlation of toy MaxAbsCor(X=toy)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the maximum absolute correlation of toy MaxAbsCor(X=toy)
MaxProCriterion returns the maximum projection criterion of an LHD
MaxProCriterion(X)MaxProCriterion(X)
X |
A matrix object. In general, |
If all inputs are logical, then the output will be a positive number indicating maximum projection criterion.
maximum projection criterion = \Bigg{ \frac{1}{{n \choose 2}} \sum_{i=1}^{n-1} \sum_{j=i+1}^{n} \frac{1}{\Pi_{l=1}^{k}(x_{il}-x_{jl})^2} \Bigg}^{1/k}
Joseph, V. R., Gul, E., and Ba, S. (2015) Maximum projection designs for computer experiments. Biometrika, 102, 371-380.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the maximum projection criterion of toy MaxProCriterion(X=toy)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the maximum projection criterion of toy MaxProCriterion(X=toy)
OA2LHD transfers an OA into an LHD with corresponding size
OA2LHD(OA)OA2LHD(OA)
OA |
An orthogonal array matrix. |
If the input is logical, then the output will be an LHD whose sizes are the same as input OA. The assumption is that the elements of OAs must be positive.
Tang, B. (1993) Orthogonal-array-based latin hypercubes. Journal of the Americal Statistical Association, 88, 1392-1397.
#create an OA(9,2,3,2) OA=matrix(c(rep(1:3,each=3),rep(1:3,times=3)),ncol=2,nrow=9,byrow = FALSE);OA #Transfer the "OA" above into a LHD according to Tang (1993) tryOA=OA2LHD(OA) OA;tryOA#create an OA(9,2,3,2) OA=matrix(c(rep(1:3,each=3),rep(1:3,times=3)),ncol=2,nrow=9,byrow = FALSE);OA #Transfer the "OA" above into a LHD according to Tang (1993) tryOA=OA2LHD(OA) OA;tryOA
OASA returns an LHD matrix generated by orthogonal-array-based simulated annealing algorithm (OASA)
OASA( OA, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 5, OC = "phi_p", p = 15, q = 1, maxtime = 5 )OASA( OA, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 5, OC = "phi_p", p = 15, q = 1, maxtime = 5 )
OA |
An orthogonal array matrix. |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
T0 |
A positive number, which stands for the user-defined initial temperature. The default is set to be 10. |
rate |
A positive percentage, which stands for temperature decrease rate, and it should be in (0,1). For example, rate=0.25 means the temperature decreases by 25% each time. The default is set to be 10%. |
Tmin |
A positive number, which stands for the minimium temperature allowed. When current temperature becomes smaller or equal to |
Imax |
A positive integer, which stands for the maximum perturbations the algorithm will try without improvements before temperature is reduced. The default is set to be 5. For the computation complexity consideration, |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
If all inputs are logical, then the output will be an LHD whose sizes are the same as input OA. The assumption is that the elements of OAs must be positive.
Leary, S., Bhaskar, A., and Keane, A. (2003) Optimal orthogonal-array-based latin hypercubes. Journal of Applied Statistics, 30, 585-598.
#create an OA(9,2,3,2) OA=matrix(c(rep(1:3,each=3),rep(1:3,times=3)),ncol=2,nrow=9,byrow = FALSE) #Use above "OA" as the input OA to generate a 9 by 2 maximin distance LHD #with the default setting try=OASA(OA=OA) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 9 by 2 nearly orthogonal LHD try2=OASA(OA=OA,OC="MaxAbsCor") try2 MaxAbsCor(try2) #calculate the maximum absolute correlation.#create an OA(9,2,3,2) OA=matrix(c(rep(1:3,each=3),rep(1:3,times=3)),ncol=2,nrow=9,byrow = FALSE) #Use above "OA" as the input OA to generate a 9 by 2 maximin distance LHD #with the default setting try=OASA(OA=OA) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 9 by 2 nearly orthogonal LHD try2=OASA(OA=OA,OC="MaxAbsCor") try2 MaxAbsCor(try2) #calculate the maximum absolute correlation.
OLHD.B2001 returns a n by k orthogonal Latin hypercube design generated by the construction method of Butler (2001)
OLHD.B2001(n, k)OLHD.B2001(n, k)
n |
An odd prime number that is greater than or equal to 3. |
k |
A positive integer that is smaller than or equal to n-1. |
If all inputs are logical, then the output will be a n by k orthogonal LHD.
Butler, N.A. (2001) Optimal and orthogonal Latin hypercube designs for computer experiments. Biometrika, 88(3), 847-857.
#create an orthogonal LHD with n=11 and k=5 OLHD.B2001(n=11,k=5) #create an orthogonal LHD with n=7 and k=6 OLHD.B2001(n=7,k=6)#create an orthogonal LHD with n=11 and k=5 OLHD.B2001(n=11,k=5) #create an orthogonal LHD with n=7 and k=6 OLHD.B2001(n=7,k=6)
OLHD.C2007 returns a 2^m+1 by m+{m-1 \choose 2} orthogonal Latin hypercube design generated by the construction method of Cioppa and Lucas (2007)
OLHD.C2007(m)OLHD.C2007(m)
m |
A positive integer, and it must be greater than or equal to 2. |
If all inputs are logical, then the output will be an orthogonal LHD with the following run size: n=2^m+1 and the following factor size: k=m+{m-1 \choose 2}.
Cioppa, T.M., and Lucas, T.W. (2007) Efficient nearly orthogonal and space-filling Latin hypercubes. Technometrics, 49(1), 45-55.
#create an orthogonal LHD with m=4. So n=2^m+1=17 and k=4+3=7 OLHD.C2007(m=4) #create an orthogonal LHD with m=5. So n=2^m+1=33 and k=5+6=11 OLHD.C2007(m=5)#create an orthogonal LHD with m=4. So n=2^m+1=17 and k=4+3=7 OLHD.C2007(m=4) #create an orthogonal LHD with m=5. So n=2^m+1=33 and k=5+6=11 OLHD.C2007(m=5)
OLHD.L2009 returns a n^2 by 2fp orthogonal Latin hypercube design generated by the construction method of Lin et al. (2009)
OLHD.L2009(OLHD, OA)OLHD.L2009(OLHD, OA)
OLHD |
An orthogonal Latin hypercube design with run size |
OA |
An orthogonal array, with |
If all inputs are logical, e,g. a n by p orthogonal Latin hypercube design and an OA(n^2,2f,n,2) orthogonal array, then the output will be an orthogonal Latin hypercube design with the following run size: n^2 and the following factor size: 2fp.
Lin, C.D., Mukerjee, R., and Tang, B. (2009) Construction of orthogonal and nearly orthogonal Latin hypercubes. Biometrika, 96(1), 243-247.
#create a 5 by 2 OLHD OLHD=OLHD.C2007(m=2) #create an OA(25,6,5,2) OA=matrix(c(2,2,2,2,2,1,2,1,5,4,3,5,3,2,1,5,4,5,1,5,4,3,2,5, 4,1,3,5,2,3,1,2,3,4,5,2,1,3,5,2,4,3,1,1,1,1,1,1,4,3,2,1,5,5, 5,5,5,5,5,1,4,4,4,4,4,1,3,1,4,2,5,4,3,3,3,3,3,1,3,5,2,4,1,3, 3,4,5,1,2,2,5,4,3,2,1,5,2,3,4,5,1,2,2,5,3,1,4,4,1,4,2,5,3,4, 4,2,5,3,1,4,2,4,1,3,5,3,5,3,1,4,2,4,5,2,4,1,3,3,5,1,2,3,4,2, 4,5,1,2,3,2),ncol=6,nrow=25,byrow=TRUE) #Construct a 25 by 12 OLHD OLHD.L2009(OLHD,OA)#create a 5 by 2 OLHD OLHD=OLHD.C2007(m=2) #create an OA(25,6,5,2) OA=matrix(c(2,2,2,2,2,1,2,1,5,4,3,5,3,2,1,5,4,5,1,5,4,3,2,5, 4,1,3,5,2,3,1,2,3,4,5,2,1,3,5,2,4,3,1,1,1,1,1,1,4,3,2,1,5,5, 5,5,5,5,5,1,4,4,4,4,4,1,3,1,4,2,5,4,3,3,3,3,3,1,3,5,2,4,1,3, 3,4,5,1,2,2,5,4,3,2,1,5,2,3,4,5,1,2,2,5,3,1,4,4,1,4,2,5,3,4, 4,2,5,3,1,4,2,4,1,3,5,3,5,3,1,4,2,4,5,2,4,1,3,3,5,1,2,3,4,2, 4,5,1,2,3,2),ncol=6,nrow=25,byrow=TRUE) #Construct a 25 by 12 OLHD OLHD.L2009(OLHD,OA)
OLHD.S2010 returns a r2^{C+1}+1 or r2^{C+1} by 2^C orthogonal Latin hypercube design generated by the construction method of Sun et al. (2010)
OLHD.S2010(C, r, type = "odd")OLHD.S2010(C, r, type = "odd")
C |
A positive integer. |
r |
A positive integer. |
type |
Design run size type, and it could be either odd or even. If |
If all inputs are logical, then the output will be an orthogonal LHD with the following run size: n=r2^{C+1}+1 or n=r2^{C+1} and the following factor size: k=2^C.
Sun, F., Liu, M.Q., and Lin, D.K. (2010) Construction of orthogonal Latin hypercube designs with flexible run sizes. Journal of Statistical Planning and Inference, 140(11), 3236-3242.
#create an orthogonal LHD with C=3, r=3, type="odd". #So n=3*2^4+1=49 and k=2^3=8 OLHD.S2010(C=3,r=3,type="odd") #create an orthogonal LHD with C=3, r=3, type="even". #So n=3*2^4=48 and k=2^3=8 OLHD.S2010(C=3,r=3,type="even")#create an orthogonal LHD with C=3, r=3, type="odd". #So n=3*2^4+1=49 and k=2^3=8 OLHD.S2010(C=3,r=3,type="odd") #create an orthogonal LHD with C=3, r=3, type="even". #So n=3*2^4=48 and k=2^3=8 OLHD.S2010(C=3,r=3,type="even")
OLHD.Y1998 returns a 2^m+1 by 2m-2 orthogonal Latin hypercube design generated by the construction method of Ye (1998)
OLHD.Y1998(m)OLHD.Y1998(m)
m |
A positive integer, and it must be greater than or equal to 2. |
If all inputs are logical, then the output will be an orthogonal LHD with the following run size: n=2^m+1 and the following factor size: k=2m-2.
Ye, K.Q. (1998) Orthogonal column Latin hypercubes and their application in computer experiments. Journal of the American Statistical Association, 93(444), 1430-1439.
#create an orthogonal LHD with m=3. So n=2^m+1=9 and k=2*m-2=4 OLHD.Y1998(m=3) #create an orthogonal LHD with m=4. So n=2^m+1=17 and k=2*m-2=6 OLHD.Y1998(m=4)#create an orthogonal LHD with m=3. So n=2^m+1=9 and k=2*m-2=4 OLHD.Y1998(m=3) #create an orthogonal LHD with m=4. So n=2^m+1=17 and k=2*m-2=6 OLHD.Y1998(m=4)
phi_p returns the phi_p criterion of an LHD
phi_p(X, p = 15, q = 1)phi_p(X, p = 15, q = 1)
X |
A matrix object. In general, |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
If all inputs are logical, then the output will be a positive number indicating phi_p. \phi_p = (\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}dij^{-p})^{1/p}, where dij = \left\{ \sum_{k=1}^{m} \vert x_{ik}-x_{jk}\vert ^q \right\}^{1/q}
Jin, R., Chen, W., and Sudjianto, A. (2005) An efficient algorithm for constructing optimal design of computer experiments. Journal of Statistical Planning and Inference, 134, 268-287.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the phi_p criterion of toy with default setting phi_p(X=toy) #Calculate the phi_p criterion of toy with p=50 and q=2 phi_p(X=toy,p=50,q=2)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #Calculate the phi_p criterion of toy with default setting phi_p(X=toy) #Calculate the phi_p criterion of toy with p=50 and q=2 phi_p(X=toy,p=50,q=2)
rLHD returns a random n by k Latin hypercube design matrix
rLHD(n, k)rLHD(n, k)
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
If all inputs are positive integer, then the output will be a n by k design matrix.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #another example with 9 rows and 2 columns rLHD(9,2)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy #another example with 9 rows and 2 columns rLHD(9,2)
SA returns a n by k LHD matrix generated by simulated annealing algorithm (SA)
SA( n, k, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 5, OC = "phi_p", p = 15, q = 1, maxtime = 5 )SA( n, k, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 5, OC = "phi_p", p = 15, q = 1, maxtime = 5 )
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
T0 |
A positive number, which stands for the user-defined initial temperature. The default is set to be 10. |
rate |
A positive percentage, which stands for temperature decrease rate, and it should be in (0,1). For example, rate=0.25 means the temperature decreases by 25% each time. The default is set to be 10%. |
Tmin |
A positive number, which stands for the minimium temperature allowed. When current temperature becomes smaller or equal to |
Imax |
A positive integer, which stands for the maximum perturbations the algorithm will try without improvements before temperature is reduced. The default is set to be 5. For the computation complexity consideration, |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
If all inputs are logical, then the output will be a n by k LHD.
Morris, M.D., and Mitchell, T.J. (1995) Exploratory designs for computer experiments. Journal of Statistical Planning and Inference, 43, 381-402.
#generate a 5 by 3 maximin distance LHD with the default setting try=SA(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=SA(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.#generate a 5 by 3 maximin distance LHD with the default setting try=SA(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=SA(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.
SA2008 returns a n by k LHD matrix generated by simulated annealing algorithm with multi-objective optimization approach
SA2008( n, k, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 5, OC = "phi_p", p = 15, q = 1, maxtime = 5 )SA2008( n, k, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 5, OC = "phi_p", p = 15, q = 1, maxtime = 5 )
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
T0 |
A positive number, which stands for the user-defined initial temperature. The default is set to be 10. |
rate |
A positive percentage, which stands for temperature decrease rate, and it should be in (0,1). For example, rate=0.25 means the temperature decreases by 25% each time. The default is set to be 10%. |
Tmin |
A positive number, which stands for the minimium temperature allowed. When current temperature becomes smaller or equal to |
Imax |
A positive integer, which stands for the maximum perturbations the algorithm will try without improvements before temperature is reduced. The default is set to be 5. For the computation complexity consideration, |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
If all inputs are logical, then the output will be a n by k LHD. This modified simulated annealing algorithm reduces columnwise correlations and maximizes minimum distance between design points simultaneously, with a cost of more computational complexity.
Joseph, V.R., and Hung, Y. (2008) Orthogonal-maximin Latin hypercube designs. Statistica Sinica, 18, 171-186.
#generate a 5 by 3 maximin distance LHD with the default setting try=SA2008(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=SA2008(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.#generate a 5 by 3 maximin distance LHD with the default setting try=SA2008(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #Another example #generate a 8 by 4 nearly orthogonal LHD try2=SA2008(n=8,k=4,OC="AvgAbsCor") try2 AvgAbsCor(try2) #calculate the average absolute correlation.
SLHD returns a n by k LHD matrix generated by improved two-stage algorithm
SLHD( n, k, t = 1, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 3, OC = "phi_p", p = 15, q = 1, stage2 = FALSE, maxtime = 5 )SLHD( n, k, t = 1, N = 10, T0 = 10, rate = 0.1, Tmin = 1, Imax = 3, OC = "phi_p", p = 15, q = 1, stage2 = FALSE, maxtime = 5 )
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
t |
A positive integer, which stands for the number of slices. |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
T0 |
A positive number, which stands for the user-defined initial temperature. The default is set to be 10. |
rate |
A positive percentage, which stands for temperature decrease rate, and it should be in (0,1). For example, rate=0.25 means the temperature decreases by 25% each time. The default is set to be 10%. |
Tmin |
A positive number, which stands for the minimium temperature allowed. When current temperature becomes smaller or equal to |
Imax |
A positive integer, which stands for the maximum perturbations the algorithm will try without improvements before temperature is reduced. The default is set to be 5. For the computation complexity consideration, |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
stage2 |
A logic input argument, and it could be either FALSE or TRUE. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
If all inputs are logical, then the output will be a n by k LHD. As mentioned from the original paper, the first stage plays a much more important role since it optimizes the slice level. More resources should be given to the first stage if computational budgets are limited. Let m=n/t, where m is the number of rows for each slice, if (m)^k >> n, the second stage becomes optional. That is the reason why we add a stage2 parameter to let users decide if the second stage is needed.
Ba, S., Myers, W.R., and Brenneman, W.A. (2015) Optimal Sliced Latin Hypercube Designs. Technometrics, 57, 479-487.
#generate a 5 by 3 maximin distance LHD with the default setting try=SLHD(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #generate a 5 by 3 maximin distance LHD with stage II #let stage2=TRUE and other input are the same as above try2=SLHD(n=5,k=3,stage2=TRUE) try2 phi_p(try2) #calculate the phi_p of "try2". #Another example #generate a 8 by 4 nearly orthogonal LHD try3=SLHD(n=8,k=4,OC="AvgAbsCor",stage2=TRUE) try3 AvgAbsCor(try3) #calculate the average absolute correlation.#generate a 5 by 3 maximin distance LHD with the default setting try=SLHD(n=5,k=3) try phi_p(try) #calculate the phi_p of "try". #generate a 5 by 3 maximin distance LHD with stage II #let stage2=TRUE and other input are the same as above try2=SLHD(n=5,k=3,stage2=TRUE) try2 phi_p(try2) #calculate the phi_p of "try2". #Another example #generate a 8 by 4 nearly orthogonal LHD try3=SLHD(n=8,k=4,OC="AvgAbsCor",stage2=TRUE) try3 AvgAbsCor(try3) #calculate the average absolute correlation.
WT returns a matrix after implementing the Williams transformation
WT(X, baseline = 1)WT(X, baseline = 1)
X |
A matrix object. In general, |
baseline |
A integer, which defines the minimum value for each column of the matrix. The default is set to be 1. |
If all inputs are logical, then the output will be a matrix whose sizes are the same as input matrix.
Williams, E. J. (1949) Experimental designs balanced for the estimation of residual effects of treatments. Australian Journal of Chemistry, 2, 149-168.
#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy toy2=toy-1;toy2 #make elements of "toy" become 0,1,2,3,4 #Implementing Williams transformation on both toy and toy2: #The result shows that "WT" function is able to detect the #elements of input matrix and make adjustments. WT(toy) WT(toy2) #Change the baseline WT(toy,baseline=5) WT(toy,baseline=10)#create a toy LHD with 5 rows and 3 columns toy=rLHD(n=5,k=3);toy toy2=toy-1;toy2 #make elements of "toy" become 0,1,2,3,4 #Implementing Williams transformation on both toy and toy2: #The result shows that "WT" function is able to detect the #elements of input matrix and make adjustments. WT(toy) WT(toy2) #Change the baseline WT(toy,baseline=5) WT(toy,baseline=10)