sensitivity (version 1.12.1)

sobolCert: Monte Carlo Estimation of Sobol' Indices using certified meta-models

Description

sobolCert implements the Monte Carlo estimation of the Sobol' sensitivity indices using certified metamodels using the formulas in Janon et al. (2011).

Usage

sobolCert(model = NULL, X1=NULL, X2=NULL, nboot = 300, conf = 0.95, lambda0 = 0, h = 0)
## S3 method for class 'sobolCert':
print(x, \dots)

Arguments

model

a function defining the model to analyze. This function must return a list whose components are:

• out

{metamodel output.} err{metamodel output error bound, satisfying $$|model_{output} - metamodel_{output}|

Value

• sobolCert returns a list of class "sobolCert", containing the following components:
• call
  the matched call.
• S
  the estimations of the Sobol' sensitivity indices.
• penalty
  (only if lambda0>0) value of the smoothing penalty.

item

• X1
• X2
• nboot
• conf
• lambda0
• h
• x
• ...

References

Janon, A., Nodet M., Prieur C. (2011) Uncertainties assessment in global sensitivity indices estimation from metamodels. To appear in International Journal for Uncertainty Quantification.

See Also

sobol, sobol2002, sobol2007

Examples

# Test case

n <- 1000
X1 <- data.frame(matrix(runif(3 * n), nrow = n))
X2 <- data.frame(matrix(runif(3 * n), nrow = n))

# sensitivity analysis
x=sobolCert(model=function(X) { list(out=X[1]+2*X[2]+X[3]+.001*runif(1),err=.01); }, 
            X1, X2, conf=.99, lambda0=.1, h=.1, nboot=30)
print(x)

x=sobolCert(model=NULL, X1=NULL, X2=NULL, conf=.95)
print(x)