| MaxProLHD {MaxPro} | R Documentation |
Maximum Projection Latin Hypercube Designs for Continuous Factors
Description
Generate the maximum projection (MaxPro) Latin hypercube design for continuous factors based on a simulated annealing algorithm. If nominal, discrete numeric, or ordinal factors exist, please see the function MaxProQQ.
Usage
MaxProLHD(n, p, s=2, temp0=0, nstarts = 1, itermax = 400, total_iter = 1e+06)
Arguments
n |
The number of runs (design points) |
p |
The number of input factors (variables) |
s |
Optional, default is “2”. The parameter in defining the s-norm distance (2 corresponds to Euclidean distance) |
temp0 |
Optional, The initial temperature in the simulated annealing algorithm. Change this value if you want to start with a higher or lower temperature |
nstarts |
Optional, default is “1”. The number of random starts |
itermax |
Optional, default is “400”. The maximum number of non-improving searches allowed under each temperature. Lower this parameter if you want the algorithm to converge faster |
total_iter |
Optional, default is “1e+06”.The maximum total number of iterations. Lower this number if the design is prohibitively large and you want to terminate the algorithm prematurely to report the best design found so far |
Details
This function utilizes a version of the simulated annealing algorithm to efficiently generate the optimal Latin hypercube designs for continuous factors based on the MaxPro criterion. Parameters in the algorithm may need to be properly tuned to achieve global convergence. Please refer to Joseph, Gul and Ba (2015) for details.
Value
The value returned from the function is a list containing the following components:
Design |
Design matrix |
temp0 |
Initial temperature |
measure |
The MaxPro criterion measure |
time_rec |
Time to complete the search |
ntotal |
The total number of iterations |
Author(s)
Shan Ba <shanbatr@gmail.com> and V. Roshan Joseph <roshan@isye.gatech.edu>
References
Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments," Biometrika, 102, 371-380.
See Also
MaxProRunOrder, MaxProAugment, MaxProQQ
Examples
obj<-MaxProLHD(n = 10, p = 4)
obj$Design