MaxPro: Locally Optimal Maximum Projection Designs for Continuous...

In MaxPro: Maximum Projection Designs

Description

Find the locally optimal maximum projection (MaxPro) design in the neighborhood of a given initial design for continuous factors.

Usage

MaxPro(InitialDesign,s=2,iteration=10)

Arguments

InitialDesign	The initial design matrix, which we recommend to be a MaxPro Latin hypercube design generated by the MaxProLHD function
s	Optional, default is “2”. The parameter in defining the s-norm distance (2 corresponds to Euclidean distance)
iteration	Optional, default is “10”. The number of iterations in running the continuous local search

Details

This function applies a continuous optimization algorithm in nloptr (Ypma 2014) to find the locally optimal MaxPro design in the neighborhood of the initial design. A MaxPro Latin hypercube design generated by the MaxProLHD function is a good choice for the initial design. 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	The locally optimal MaxPro design matrix
measure	The MaxPro criterion measure for the locally optimal design

Author(s)

Shan Ba <[e-mail]> and V. Roshan Joseph <[e-mail]>

References

Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments," Biometrika, 102, 371-380.

Ypma, J. (2014) "Introduction to nloptr: an R interface to NLopt", R Package Version 1.0.0.

See Also

MaxProLHD, MaxProRunOrder, MaxProAugment

Examples

InitialDesign<-MaxProLHD(n = 10, p = 4)$Design 
DOX<-MaxPro(InitialDesign)
DOX$Design

MaxPro documentation built on May 2, 2019, 6:59 a.m.