sensobol (version 1.1.5)

vars_matrices: STAR-VARS sampling strategy

Description

It creates the STAR-VARS matrix needed to compute VARS-TO following Razavi2016a;textualsensobol.

Usage

vars_matrices(star.centers, params, h = 0.1, type = "QRN", ...)

Value

A matrix where each column is a model input and each row a sampling point.

Arguments

star.centers

Positive integer, number of star centers.

params

Character vector with the name of the model inputs.

h

Distance between pairs. The user should select between 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2. Default is h = 0.1.

type

Approach to construct the STAR-VARS. Options are:

• type = "QRN": It uses Sobol1967;textualsensobol Quasi-Random Numbers through a call to the function sobol of the randtoolbox package.

• type = "R": It uses random numbers.

...

Further arguments in sobol.

Details

The user randomly selects \(N_{star}\) points across the factor space using either Sobol' Quasi Random Numbers (type = "QRN") or random numbers (type = "R"). These are the star centres and their location can be denoted as \(\mathbf{s}_v = s_{v_1},...,s_{v_i}, ..., s_{v_k}\), where \(v=1,2,...,N_{star}\). Then, for each star centre, the function generates a cross section of equally spaced points \(\Delta h\) apart for each of the \(k\) model inputs, including and passing through the star centre. The cross section is produced by fixing \(\mathbf{s}_{v_{\sim i}}\) and varying \(s_i\). Finally, for each factor all pairs of points with \(h\) values of \(\Delta h, 2\Delta h, 3\Delta h\) and so on are extracted. The total computational cost of this design is \(N_t=N_{star} (k (\frac{1}{\Delta h} - 1) + 1)\).

References

Examples

# Define settings
star.centers <- 10; params <- paste("X", 1:5, sep = ""); h <- 0.1

# Create STAR-VARS
mat <- vars_matrices(star.centers = star.centers, params = params, h = h)