measure {MOFAT} | R Documentation |
This function can be used for computing screening measures.
measure(design, y)
design |
design matrix, which should have the Sobol' design structure |
y |
response vector |
The measure
function computes the screening measures such as the total Sobol' indices (Sobol' 1993)
and \mu^*
measure of Campolongo et al. (2007). The design matrix should have the Sobol' design structure.
Please see Xiao et al. (2022) for details.
t |
Total Sobol' index |
mustar |
|
Qian Xiao and V. Roshan Joseph
Sobol’, I. M. (1993), “On sensitivity estimation for nonlinear mathematical models,” Mathematical Modeling and Computational Experiments, 1, 407–414.
Campolongo, F., Cariboni, J., and Saltelli, A. (2007), “An effective screening design for sensitivity analysis of large models,” Environmental modelling and software, 22, 1509–1518.
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.
#Friedman function
fun <- function (X)
{
Y <- 10*sin(pi*X[1]*X[2]) + 20*(X[3] - 0.5)^2 + 10*X[4] + 5*X[5]
return(Y)
}
design = mofat(p=10, l=3)
y = apply(design, 1, fun)
#Screening measures
measure(design, y)