fast99 {sensitivity} | R Documentation |

`fast99`

implements the so-called "extended-FAST" method
(Saltelli et al. 1999). This method allows the estimation of first
order and total Sobol' indices for all the factors (alltogether
*2p* indices, where *p* is the number of factors) at a
total cost of *n * p* simulations.

fast99(model = NULL, factors, n, M = 4, omega = NULL, q = NULL, q.arg = NULL, ...) ## S3 method for class 'fast99': tell(x, y = NULL, ...) ## S3 method for class 'fast99': print(x, ...) ## S3 method for class 'fast99': plot(x, ylim = c(0, 1), ...)

`model` |
a function, or a model with a `predict` method,
defining the model to analyze. |

`factors` |
an integer giving the number of factors, or a vector of character strings giving their names. |

`n` |
an integer giving the sample size, i.e. the length of the discretization of the s-space (see Cukier et al.). |

`M` |
an integer specifying the interference parameter, i.e. the number of harmonics to sum in the Fourier series decomposition (see Cukier et al.). |

`omega` |
a vector giving the set of frequencies, one frequency for each factor (see details below). |

`q` |
a vector of quantile functions names corresponding to wanted factors distributions (see details below). |

`q.arg` |
a list of quantile functions parameters (see details below). |

`x` |
a list of class `"fast99"` storing the state of the
sensitivity study (parameters, data, estimates). |

`y` |
a vector of model responses. |

`ylim` |
y-coordinate plotting limits. |

`...` |
any other arguments for `model` which are passed
unchanged each time it is called. |

If not given, the set of frequencies `omega`

is taken from
Saltelli et al. The first frequency of the vector `omega`

is
assigned to each factor *X_i* in turn (corresponding to the
estimation of Sobol' indices *S_i* and *ST_i*),
other frequencies being assigned to the remaining factors.

If the arguments `q`

and `q.args`

are not given, the factors
are taken uniformly distributed on *[0,1]*. The
argument `q`

must be list of character strings, giving the names
of the quantile functions (one for each factor), such as `qunif`

,
`qnorm`

... It can also be a single character string, meaning
same distribution for all. The argument `q.arg`

must be a list of
lists, each one being additional parameters for the corresponding
quantile function. For example, the parameters of the quantile
function `qunif`

could be `list(min=1, max=2)`

, giving an
uniform distribution on *[1,2]*. If `q`

is a single
character string, then `q.arg`

must be a single list (rather than
a list of one list).

`fast99`

returns a list of class `"fast99"`

, containing all
the input arguments detailed before, plus the following components:

`call` |
the matched call. |

`X` |
a `data.frame` containing the factors sample values. |

`y` |
a vector of model responses. |

`V` |
the estimation of variance. |

`D1` |
the estimations of Variances of the Conditional Expectations (VCE) with respect to each factor. |

`Dt` |
the estimations of VCE with respect to each factor
complementary set of factors ("all but Xi"). |

A. Saltelli, S. Tarantola and K. Chan, 1999, *A quantitative, model
independent method for global sensitivity analysis of model
output*, Technometrics, 41, 39–56.

R. I. Cukier, H. B. Levine and K. E. Schuler, 1978, *Nonlinear
sensitivity analysis of multiparameter model
systems*. J. Comput. Phys., 26, 1–42.

# Test case : the non-monotonic Ishigami function x <- fast99(model = ishigami.fun, factors = 3, n = 1000, q = "qunif", q.arg = list(min = -pi, max = pi)) print(x) plot(x)

[Package *sensitivity* version 1.4-0 Index]