

ranks(quantreg)                              R Documentation

_F_u_n_c_t_i_o_n _t_o _c_o_m_p_u_t_e _r_a_n_k_s _f_r_o_m _t_h_e _d_u_a_l _(_r_e_g_r_e_s_s_i_o_n
_r_a_n_k_s_c_o_r_e_) _p_r_o_c_e_s_s

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

_U_s_a_g_e_:

     ranks(v, score="wilcoxon")

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

       v: regression quantile structure for the model of
          interest

   score: The score function desired.  Currently implemented
          score functions are Wilcoxon, Normal, and Sign
          which are asymptotically optimal for the logistic,
          Gaussian and Laplace error models respectively.

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

_V_a_l_u_e_:

     The function returns two components one is the ranks,
     the other is a scale factor which is the L_2 norm of
     the score function.  All score functions should be nor-
     malized to have mean zero.

_S_i_d_e _E_f_f_e_c_t_s_:

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

     Gutenbrunner, C., J. Jureckova,  Koenker, R. and  Port-
     noy, S.(1993) Tests of Linear Hypotheses  based on
     Regression Rank Scores", Journal of Nonparametric
     Statistics, (2), 307-331.

_S_e_e _A_l_s_o_:

     See also rq, rrs.test

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

     data(stackloss)
     ranks(rq(stack.x,stack.loss))

