morris              package:sensitivity              R Documentation

_T_h_e _M_o_r_r_i_s _O_A_T _S_c_r_e_e_n_i_n_g _M_e_t_h_o_d

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

     'morris' is the implementation of the Morris OAT Screening method.
     This function generates the Morris design of experiments and
     computes the measures of sensitivity mu* and sigma.

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

     morris(model = NULL, factors, levels, R, jump = NULL,
            min = 0, max = 1, scale = TRUE, optim = NULL, ...)

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

   model: the model

 factors: the number of factors, or their names

  levels: the number of levels of the design grid

       R: the number of repetitions of the design, i.e. the number of
          elementary effect computed per factor

    jump: the 'grid jump' coefficient

     min: the minimum values for the factors

     max: the maximum values for the factors

   scale: logical. If 'TRUE', the input and output data are scaled

   optim: optimization of the design for better coverage of the space
          (cf Campolongo 2005), not documented yet (for informations
          feel free to ask the maintainer)

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

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

     '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.

     The number of levels is not necessary the same for each space
     coordinate. It is the case when 'levels' is a single integer.

     'min' and 'max' are boundaries of the region of experimentation.
     They can be single values (the same for each factor) or vectors.

     'jump' is such that:

     Delta[i] = jump[i] * ( max[i] - min[i] ) / ( levels[i] - 1 )

     If 'jump' is given as 'NULL' and the number of levels is even (for
     each component), then 'jump' has the value recommended by Morris:
     jump = levels / 2. If 'jump' is a single value, then it is taken
     the same for each coordinate.

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

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

       x: the design of experiments (input sample)

       y: the response

      ee: the matrix of the elementary effects

      mu: the estimations of the mu* index

   sigma: the estimations of the sigma index

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

     The number of model evaluations is (p + 1) * R where p is the
     number of factors.

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

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

     Morris, M. D., 1991, Factorial sampling plans for preliminary
     computational experiments. _Technometrics_, *33*, 161-174.

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

     # Test case : the non-monotonic function of Morris

     sa <- morris(model = morris.fun, factors = 20, levels = 4, R = 4)
     print(sa)
     plot(sa)

