# lprq

##### locally polynomial quantile regression

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:

The design points at which the evaluation occurs

The estimated function values at these design points

The estimated first derivative values at the design points

##### Note

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

.

##### References

Koenker, R. (2004) Quantile Regression

##### See Also

`rqss`

for a general approach to oonparametric QR fitting.

##### Examples

```
# NOT RUN {
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))
# }
```

*Documentation reproduced from package quantreg, version 5.54, License: GPL (>= 2)*