sensitivity (version 1.14.0)

testmodels: Test Models for Sensitivity Analysis

Description

These functions are standard testcase for sensitivity analysis benchmarks. For a scalar output (see Saltelli et al. 2000, section 2.9):

• the g-function of Sobol' with 8 inputs, X ~ U[0,1];
• the function of Ishigami with 3 inputs, X ~ U[-pi,pi];
• the function of Morris with 20 inputs, X ~ U[0,1].

For functional output cases:

• the Arctangent temporal function with 2 inputs, X ~ U[-7,7] (Auder, 2011). The functional support is on [0,2pi];
• the Cambell1D function with 4 nputs, X ~U[-1,5] (Campbell et al. 2006). The functional support is on [-90,90].

Usage

sobol.fun(X)
ishigami.fun(X)
morris.fun(X)
atantemp.fun(X, q = 100)
campbell1D.fun(X, theta = -90:90)

Arguments

X

a matrix (or data.frame) containing the input sample.

q

for the atantemp() function: the number of discretization steps of the functional output

theta

for the campbell1D() function: the discretization steps (angles in degrees)

Value

A vector of function responses.

References

A. Saltelli, K. Chan and E. M. Scott eds, 2000, Sensitivity Analysis, Wiley.

Examples

## Not run: 
# 
# # Examples for the functional toy fonctions
# 
# # atantemp function
# 
# y0 <- atantemp.fun(matrix(c(-7,0,7,-7,0,7),ncol=2))
# plot(y0[1,],type="l")
# apply(y0,1,lines)
# 
# n <- 100
# X <- matrix(c(runif(2*n,-7,7)),ncol=2)
# y <- atantemp.fun(X)
# x11()
# plot(y0[2,],ylim=c(-2,2),type="l")
# apply(y,1,lines)
# 
# # campbell1D function
# 
# N1=100         # nombre de simulations pour courbes 1D
# min=-1 ; max=5
# nominal=(max+min)/2
# 
# X1 = NULL ; y1 = NULL
# Xnom=matrix(nominal,nr=1,nc=4)
# ynom=campbell1D.fun(Xnom,theta=-90:90)
# x11()
# plot(ynom,ylim=c(8,30),type="l",col="red")
# for (i in 1:N1){
#   X=matrix(runif(4,min=min,max=max),nr=1,nc=4)
#   rbind(X1,X)
#   y=campbell1D.fun(X,theta=-90:90)
#   rbind(y1,y)
#   lines(y)
# }
# 
# ## End(Not run)