anova.rq {quantreg} | R Documentation |

Compute test statistics for two or more quantile regression fits.

anova.rq(object, ...) anova.rqlist(object, ..., test = "Wald", joint = TRUE, score = "wilcoxon")

`object, ...` |
objects of class `rq', originating from a call to `rq'. |

`test` |
A character string specifying the test statistic to use. Can be either `Wald' or `rank'. |

`joint` |
A logical flag indicating whether tests of equality of slopes should be done as joint tests on all slope parameters, or whether (when joint = FALSE) separate tests on each of the slope parameters should be reported. |

`score` |
A character string specifying the score function to use, only needed or applicable for the `rank' form of the test. |

There are two (as yet) distinct forms of the test. In the first the
fitted objects all have the same specified quantile (tau) and the intent
is to test the hypothesis that smaller models are adaquete relative to
the largest specified model. In this case there are two options for
the argument `test', by default a Wald test is computed as in
Bassett and Koenker (1982). If test=`rank' is specified, then a rank
test statistic is computed as described in Gutenbrunner, Jureckova,
Koenker and Portnoy (1993). In the latter case one can also specify
a form for the score function of the rank test, by default the Wilcoxon
score is used, the other options are score=`sign' for median (sign) scores,
or score=`normal' for normal (van der Waerden) scores. A fourth option
is score=`tau' which is a generalization of median scores to an arbitrary
quantile, in this case the quantile is assumed to be the one associated
with the fitting of the specified objects. The computing of
the rank form of the test is carried out in the `rq.test.rank`

function, see `ranks`

for further details on the score function
options.

The Wald form of the test is local in sense that the null hypothesis asserts only that a subset of the covariates are ``insignificant'' at the specified quantile of interest. The rank form of the test can also be used to test the global hypothesis that a subset is ``insignificant'' over an entire range of quantiles. The use of the score function score = "tau" restricts the rank test to the local hypothesis of the Wald test.

In the second form of the test the linear predictor of the fits are all the same, but the specified quantiles (taus) are different. In this case the hypothesis of interest is that the slope coefficients of the models are identical. The test statistic is a variant of the Wald test described in Koenker and Bassett (1982).

By default, the tests return an F-like statistic in the sense that the an asymptotically Chi-squared statistic is divided by its degrees of freedom and the reported p-value is computed for an F statistic based on the numerator degrees of freedom equal to the rank of the null hypothesis and the denominator degrees of freedom is taken to be the sample size minus the number of parameters of the maintained model.

An object of class `"anova"' inheriting from class `"data.frame"'.

An attempt to verify that the models are nested in the first form of the test is made, but this relies on checking set inclusion of the list of variable names and is subject to obvious ambiguities when variable names are generic. The comparison between two or more models will only be valid if they are fitted to the same dataset. This may be a problem if there are missing values and R's default of `na.action = na.omit' is used. The rank version of the nested model tests involves computing the entire regression quantile process using parametric linear programming and thus can be rather slow and memory intensive on problems with more than several thousand observations.

Roger Koenker

[1] Bassett, G. and R. Koenker (1982). Tests of Linear Hypotheses
and L1 Estimation,
*Econometrica*, **50**, 1577–83.

[2] Koenker, R. W. and Bassett, G. W. (1982). Robust Tests for
Heteroscedasticity based on Regression Quantiles,
*Econometrica*, **50**, 43–61.

[3] Gutenbrunner, C., Jureckova, J., Koenker, R, and S. Portnoy (1993).
Tests of Linear Hypotheses based on Regression Rank Scores,
*J. of Nonparametric Statistics*, **2**, 307–331.

The model fitting function `rq`

,
and the functions for testing hypothesis on the entire quantile
regression process `khmaladze.test`

. For further details
on the rank tests see `ranks`

.

data(barro) fit0 <- rq(y.net ~ lgdp2 + fse2 + gedy2 , data = barro) fit1 <- rq(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, data = barro) fit2 <- rq(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, data = barro,tau=.75) fit3 <- rq(y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, data = barro,tau=.25) anova(fit1,fit0) anova(fit1,fit2,fit3) anova(fit1,fit2,fit3,joint=FALSE)

[Package *quantreg* version 3.82 Index]