<!doctype s-function-doc system "s-function-doc.dtd" [
<!entity % S-OLD "INCLUDE">
]
>
<s-function-doc>
<s-topics>
<s-topic>ranks</s-topic>
</s-topics>
<s-title>
Quantile Regression Ranks
</s-title>
<s-description>
Function to compute ranks from the dual (regression rankscore) process.
</s-description>
<s-usage>
<s-old-style-usage>
ranks(v, score="wilcoxon", tau=0.5)
</s-old-style-usage>
</s-usage>
<s-args-required>
<s-arg name= v >
object of class rq.process generated by rq()
</s-arg>
</s-args-required>
<s-args-optional>
<s-arg name= 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.
</s-arg>
<s-arg name= tau >
the optional value of tau if the "tau" score function is used.
</s-arg>
</s-args-optional>
<s-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.
</s-value>
<s-details>
See GJKP(1993) for further details.
</s-details>
<s-section name = "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.
</s-section>
<s-see>
rq, rrs.test
</s-see>
<s-examples>
ranks(rq(stack.loss~stack.x,tau=-1))
</s-examples>
<s-keywords>
<s-keyword>librobust</s-keyword>
<s-keyword>regression</s-keyword>
</s-keywords>
<s-docclass>
function
</s-docclass>
</s-function-doc>
