rq.process.object {quantreg} | R Documentation |
These are objects of class rq.process.
They represent the fit of a linear conditional quantile function model.
These arrays are computed by parametric linear programming methods using using the exterior point (simplex-type) methods of the Koenkerd'Orey algorithm based on Barrodale and Roberts median regression algorithm.
This class of objects is returned from the rq
function
to represent a fitted linear quantile regression model.
The "rq.process"
class of objects has
methods for the following generic
functions:
effects
, formula
, labels
, model.frame
, model.matrix
, plot
, predict
, print
, print.summary
, summary
The following components must be included in a legitimate rq.process
object.
sol
dsol
[1] Koenker, R. W. and Bassett, G. W. (1978). Regression quantiles, Econometrica, 46, 3350.
[2] Koenker, R. W. and d'Orey (1987, 1994). Computing Regression Quantiles. Applied Statistics, 36, 383393, and 43, 410414.
[3] Gutenbrunner, C. Jureckova, J. (1991). Regression quantile and regression rank score process in the linear model and derived statistics, Annals of Statistics, 20, 305330.
[4] Gutenbrunner, C., Jureckova, J., Koenker, R. and Portnoy, S. (1994) Tests of linear hypotheses based on regression rank scores. Journal of Nonparametric Statistics, (2), 307331.
[5] Portnoy, S. (1991). Asymptotic behavior of the number of regression quantile breakpoints, SIAM Journal of Scientific and Statistical Computing, 12, 867883.
rq
.