ranks                package:quantreg                R Documentation

_Q_u_a_n_t_i_l_e _R_e_g_r_e_s_s_i_o_n _R_a_n_k_s

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

     Function to compute ranks from the dual (regression rankscore)
     process.

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

     ranks(v, score="wilcoxon", tau=0.5)

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

       v: object of class `"rq.process"' generated by `rq()' 

   score: The score function desired.  Currently  implemented score 
          functions   are `"wilcoxon"', `"normal"', and `"sign"' which
          are asymptotically optimal  for   the  logistic,  Gaussian 
          and Laplace location shift models respectively. Also
          implemented are the `"tau"' which generalizes sign scores to
          an arbitrary quantile, and `"interquartile"' which is
          appropriate for tests of scale shift. 

     tau: the optional value of `tau' if the `"tau"' score function is
          used. 

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

     See GJKP(1993) for further details.

_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 normalized to have mean zero.

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

     Gutenbrunner, C., J. Jureckova,  Koenker, R. and  Portnoy, 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:

     `rq', `rrs.test'

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

     data(stackloss)
     ranks(rq(stack.loss ~ stack.x, tau=-1))

