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. The "normal"
          score  function is also sometimes called van der Waerden
          scores. 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', 'rq.test.rank' 'anova.rq'

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

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

