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, ...)
ask(x, ...)
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 |

`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)
```

*sensitivity*version 1.30.0 Index]