rrs.test {quantreg}  R Documentation 
Function to compute regression rankscore test of a linear hypothesis
based on the dual quantile regression process. A test of the
hypothesis,
is carried out by estimating the restricted model and constructing
a test based on the dual process under the restricted model. The
details of the test are described in GJKP(1993). The test has a
Raoscore, Lagrangemultiplier interpretation since in effect it
is based on the value of the gradient of unrestricted quantile regression
problem evaluated under the null. This function will eventually be
superseded by a more general anova()
method for rq
.
rrs.test(x0, x1, y, v, score="wilcoxon")
x0 
the matrix of maintained regressors, a column of ones is appended automatically. 
x1 
matrix of covariates under test. 
y 
response variable, may be omitted if v is provided.

v 
object of class "rq.process" generated e.g. by
rq(y ~ x0, tau=1)

score 
Score function for test (see ranks )

See GJKP(1993)
Test statistic sn
is asymptotically Chisquared with rank(X1) dfs.
The vector of ranks is also returned as component rank
.
[1] Gutenbrunner, C., Jureckova, J., Koenker, R. and Portnoy, S. (1993) Tests of linear hypotheses based on regression rank scores. Journal of Nonparametric Statistics, (2), 307331.
[2] Koenker, R. W. and d'Orey (1994). Remark on Alg. AS 229: Computing dual regression quantiles and regression rank scores. Applied Statistics, 43, 410414.
# Test that covariates 2 and 3 belong in stackloss model using Wilcoxon scores. data(stackloss) rrs.test(stack.x[,1], stack.x[,2:3], stack.loss)