quantreg (version 3.03)

rq.fit.fn: Quantile Regression Fitting via Interior Point Methods

Description

This is a lower level routine called by rq() to compute quantile regression methods using the Frisch-Newton algorithm.

Usage

rq.fit.fn(x, y, tau=0.5, int=F, beta=0.99995, eps=1e-06)

Arguments

x

The design matrix

y

The response vector

tau

The quantile of interest, must lie in (0,1)

int

logical flag, if T an intercept should be appended to x, if not, not.

beta

technical step length parameter -- alter at your own risk!

eps

tolerance parameter for convergence. In cases of multiple optimal solutions there may be some discrepancy between solutions produced by method "fn" and method "br". This is due to the fact that "fn" tends to

Value

• returns an object of class "rq", which can be passed to summary.rq to obtain standard errors, etc.

Details

The details of the algorithm are explained in Koenker and Portnoy (1997). The basic idea can be traced back to the log-barrier methods proposed by Frisch in the 1950's for constrained optimization. But the current implementation is based on proposals by Mehrotra and others in the recent (explosive) literature on interior point methods for solving linear programming problems. This version of the algorithm is designed for fairly large problems, for very large problems see rq.fit.pfn.

References

Koenker, R. and S. Portnoy (1997). The Gaussian Hare and the Laplacian Tortoise: Computability of squared-error vs. absolute-error estimators, with discussion, Statistical Science, 12, 279-300.

See Also

rq, rq.fit.br, rq.fit.pfn