Quantile Regression Ranks

DESCRIPTION:

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

USAGE:

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

REQUIRED ARGUMENTS:

v
object of class rq.process generated by rq()

OPTIONAL ARGUMENTS:

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.

VALUE:

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.

DETAILS:

See GJKP(1993) for further details.

REFERENCES:

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.

SEE ALSO:

rq, rrs.test

EXAMPLES:

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