srcpcc              package:sensitivity              R Documentation

_L_i_n_e_a_r _S_e_n_s_i_t_i_v_i_t_y _A_n_a_l_y_s_i_s

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

     'srcpcc' computes the standardized regression coefficients (SRC)
     and the partial correlation coefficients (PCC). Analysis can be
     done on the ranks; then the indices are the standardized rank
     regression coefficients (SRRC) and the partial rank correlation
     coefficients (PRCC).

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

     srcpcc(model = NULL, x, pcc = TRUE, rank = FALSE,
           nboot = 0, conf = 0.95, ...)

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

   model: the model

       x: the input sample

     pcc: logical. If 'TRUE', the P(R)CCs are computed

    rank: logical. If 'TRUE', the analysis is done on the ranks

   nboot: the number of bootstrap replicates

    conf: the confidence level for bootstrap confidence intervals

     ...: 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.

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

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

       y: the response

     src: the estimations of the SRC indices (or SRRC if rank analysis
          is requested)

     pcc: if requested, the estimations of the PCC indices (or PRCC if
          rank analysis is requested)

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

     The number of model evaluations is n where n is the size of the
     sample 'x'.

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

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

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

     # linear model : Y = X1 + X2 + X3

     model1 <- function(x) x[, 1] + x[, 2] + x[, 3]

     # a 100-sample with X1 ~ U(0.5, 1.5)
     #                   X2 ~ U(1.5, 4.5)
     #                   X3 ~ U(4.5, 13.5)

     n <- 100
     x <- data.frame(X1 = runif(n, 0.5, 1.5),
                     X2 = runif(n, 1.5, 4.5),
                     X3 = runif(n, 4.5, 13.5))

     # sensitivity analysis

     sa <- srcpcc(model = model1, x = x, nboot = 100)
     print(sa)
     par(mfrow = c(1,2))
     plot(sa, ask = FALSE)

