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))