decoupling {sensitivity} R Documentation

## Decoupling Simulations and Estimations

### Description

tell and ask are S3 generic methods for decoupling simulations and sensitivity measures estimations. In general, they are not used by the end-user for a simple R model, but rather for an external computational code. Most of the sensitivity analyses objects of this package overload tell, whereas ask is overloaded for iterative methods only. extract is used as a post-treatment of a sobolshap_knn object

### Usage

tell(x, y = NULL, ...)
extract(x, ...)


### Arguments

 x a typed list storing the state of the sensitivity study (parameters, data, estimates), as returned by sensitivity analyses objects constructors, such as src, morris, etc. y a vector of model responses. ... additional arguments, depending on the method used.

### Details

When a sensitivity analysis method is called with no model (i.e. argument model = NULL), it generates an incomplete object x that stores the design of experiments (field X), allowing the user to launch "by hand" the corresponding simulations. The method tell allows to pass these simulation results to the incomplete object x, thereafter estimating the sensitivity measures.

The extract method is useful if in a first step the Shapley effects have been computed and thus sensitivity indices for all possible subsets are available. The resulting sobolshap_knn object can be post-treated by extract to get first-order and total Sobol indices very easily.

When the method is iterative, the data to simulate are not stored in the sensitivity analysis object x, but generated at each iteration with the ask method; see for example sb.

### Value

tell doesn't return anything. It computes the sensitivity measures, and stores them in the list x. Side effect: tell modifies its argument x.

ask returns the set of data to simulate.

extract returns an object, from a sobolshap_knn object, containing first-order and total Sobol indices.

### Author(s)

Gilles Pujol and Bertrand Iooss

### Examples

# Example of use of fast99 with "model = NULL"
x <- fast99(model = NULL, factors = 3, n = 1000,
q = "qunif", q.arg = list(min = -pi, max = pi))
y <- ishigami.fun(x\$X)
tell(x, y)
print(x)
plot(x)


[Package sensitivity version 1.28.0 Index]