fast               package:sensitivity               R Documentation

_F_o_u_r_i_e_r _A_m_p_l_i_t_u_d_e _S_e_n_s_i_t_i_v_i_t_y _T_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     'fast' is the implementation of the Fourier Amplitude Sensitivity
     Test.

_U_s_a_g_e:

     fast(method = "saltelli99", model = NULL, factors, n, M = 4,
          omega = NULL, q = NULL, q.arg = NULL, ...)

_A_r_g_u_m_e_n_t_s:

  method: the method: "saltelli99" only

   model: the model

 factors: the number of factors, or their names

       n: the sample size

       M: the interference parameter

   omega: the set of frequencies

       q: the names of the quantile functions for the factors
          distributions

   q.arg: the quantile parameters

     ...: any other arguments for 'model' which are passed unchanged
          each time it is called

_D_e_t_a_i_l_s:

     The method "saltelli99" is the so-called extended fast method wich
     provides estimations of both first order and total indices at a
     low computational cost.

     'model' is a function or a predictor (a class with a 'predict'
     method) computing the response 'y' based on the sample given by
     'x'. If no model is specified, the indices will be computed when
     one gives the response.

     'factors' could either be a single number or a vector of character
     strings.

     'n' is the length of the discretization of the s-space (for
     computing Fourier coefficients) and 'M' is the number of harmonics
     to sum (for computing partial variances).

     If the set of frequencies 'omega' is not given, the function use
     the set recommended by the corresponding method. For the method
     "saltelli99", the first frequency is the greater, associated with
     the input variable to assess, and the other frequencies are
     associated with the complementary set.

     If 'q' and 'q.args' are not given, the factors will be considerd
     uniform on [0,1]. 'q' is a list of character strings giving the
     names of the quantile functions (one for each factor), such as
     'qunif', 'qnorm'... 'q' could  also be a single character string
     (the same for all). 'q.arg' is a list of lists, each list being
     additional parameters for the corresponding quantile function. For
     example, the parameters of the quantile 'qunif' could be '(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.

_V_a_l_u_e:

     'fast' returns an object of class '"fast"'. An object of class
     '"fast"' is a list containing the following components:

       x: the factor sample

       y: the response

       S: the estimations of the first-order indices

      St: the estimations of the total indices (method "saltelli99")

_C_o_m_p_u_t_a_t_i_o_n_a_l _c_o_s_t:

     For the method "saltelli99", the number of model evaluations is p
     * n where p is the number of factors.

_R_e_f_e_r_e_n_c_e_s:

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

     Saltelli, A., Chan, K. and Scott, E. M., 2000, _Sensitivity
     analysis_. Wiley.

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

_E_x_a_m_p_l_e_s:

     # Test case : the non-monotonic Ishigami function

     sa <- fast(model = ishigami.fun, factors = 3, n = 1000,
                q = "qunif", q.arg = list(min = -pi, max = pi))
     print(sa)
     plot(sa)

