| oakley_Fun {sensobol} | R Documentation |
Oakley & O'Hagan (2004) function
Description
It implements the Oakley and O'Hagan (2004) function.
Usage
oakley_Fun(X)
Arguments
X |
A data frame or numeric matrix where each column is a model input and each row a sample point. |
Details
The function requires 15 model inputs and reads as
y=\mathbf{a}_1^T \bm{x} + \mathbf{a}_2 ^ T \sin(\mathbf{x}) + \mathbf{a}_3 ^ T \cos(\mathbf{x}) + \mathbf{x}^T \mathbf{M}\mathbf{x}\,,
where \mathbf{x}=x_1,x_2,...,x_k, k=15, and values
for \mathbf{a}^T_i,i=1,2,3 and \mathbf{M} are defined by Oakley and O'Hagan (2004). The
transformation of the distribution of the model inputs from U(0, 1) to
N(0, 1)) is conducted internally.
Value
A numeric vector with the model output.
References
Oakley JE, O'Hagan A (2004). “Probabilistic sensitivity analysis of complex models: a Bayesian approach.” Journal of the Royal Statistical Society B, 66(3), 751–769. doi:10.1111/j.1467-9868.2004.05304.x.
Examples
# Define settings
N <- 100; params <- paste("X", 1:15, sep = "")
# Create sample matrix
mat <- sobol_matrices(N = N, params = params)
# Compute Oakley and O'Hagan (2004) function
Y <- oakley_Fun(mat)