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, ...)
"tell"(x, y = NULL, ...)
"print"(x, ...)
"plot"(x, ylim = c(0, 1), ...)predict method,
defining the model to analyze."fast99" storing the state of the
sensitivity study (parameters, data, estimates).model which are passed
unchanged each time it is called.fast99 returns a list of class "fast99", containing all
the input arguments detailed before, plus the following components: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).
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)
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