lprq: locally polynomial quantile regression

In quantreg: Quantile Regression

locally polynomial quantile regression

Description

This is a toy function to illustrate how to do locally polynomial quantile regression univariate smoothing.

Usage

lprq(x, y, h, tau = .5, m = 50)

Arguments

x	The conditioning covariate
y	The response variable
h	The bandwidth parameter
tau	The quantile to be estimated
m	The number of points at which the function is to be estimated

Details

The function obviously only does locally linear fitting but can be easily adapted to locally polynomial fitting of higher order. The author doesn't really approve of this sort of smoothing, being more of a spline person, so the code is left is its (almost) most trivial form.

Value

The function compute a locally weighted linear quantile regression fit at each of the m design points, and returns:

xx	The design points at which the evaluation occurs
fv	The estimated function values at these design points
dev	The estimated first derivative values at the design points

Note

One can also consider using B-spline expansions see bs.

Author(s)

R. Koenker

References

Koenker, R. (2004) Quantile Regression

See Also

rqss for a general approach to oonparametric QR fitting.

Examples

require(MASS)
data(mcycle)
attach(mcycle)
plot(times,accel,xlab = "milliseconds", ylab = "acceleration (in g)")
hs <- c(1,2,3,4)
for(i in hs){
        h = hs[i]
        fit <- lprq(times,accel,h=h,tau=.5)
        lines(fit$xx,fit$fv,lty=i)
        }
legend(50,-70,c("h=1","h=2","h=3","h=4"),lty=1:length(hs))

quantreg documentation built on Oct. 22, 2024, 5:07 p.m.