mofat {MOFAT}

MOFAT

Description

This function can be used for generating MOFAT designs.

Usage

mofat(p, l, method = "best")

Arguments

p

number of factors

l

number of base runs

method

choose among "uniform", "projection", and "best"

Details

The mofat function generates the MOFAT design for a given number of factors (p\ge 2) and number of base runs (l \ge 3). The total number of runs in the MOFAT design will be l(p+1). A MOFAT design can be viewed as an optimized version of Morris screening design (Morris 1991) by exploiting its connections with the Monte Carlo-based design of Sobol' (1993). Please see Xiao et al. (2022) for details.

Three choices for the method are given: "uniform", "projection", and "best". Option "uniform" gives l equally-spaced levels for the entire design, which are also balanced. "projection" option adjusts the levels of the two base matrices A and B such that there are 2l or 2l-1 levels in the design depending on l is even or odd. Option "best" (default) chooses the best among the first two options using maximin distance criterion.

Value

design

MOFAT design

Author(s)

Qian Xiao and V. Roshan Joseph

References

Morris, M. D. (1991), “Factorial sampling plans for preliminary computational experiments,” Technometrics, 33, 161–174.

Sobol’, I. M. (1993), “On sensitivity estimation for nonlinear mathematical models,” Mathematical Modeling and Computational Experiments, 1, 407–414.

Xiao, Q., Joseph, V. R., and Ray, D. M. (2022). “Maximum One-Factor-At-A-Time Designs for Screening in Computer Experiments”. Technometrics, to appear.

Examples

#MOFAT with three base runs
mofat(p=10, l=3, method="uniform")
mofat(p=10, l=3, method="projection")

#MOFAT with five base runs
mofat(p=10,l=5)
dim(mofat(p=125,l=5))