[R] Quantile Regression in R
roger koenker
rkoenker at uiuc.edu
Tue Jun 29 17:49:13 CEST 2004
The short answer to your question is that quantile regression
estimates are estimating linear conditional quantile functions,
just like lm() is used to estimate conditional mean functions.
A longer answer would inevitably involve unpleasant suggestions
that you should follow the posting guide:
a.) send questions about packages to the maintainer, not R-help
b.) not attach datasets in modes that are stripped by R-help
c.) make a token effort to read the documentation and related
literature
url: www.econ.uiuc.edu/~roger Roger Koenker
email rkoenker at uiuc.edu Department of Economics
vox: 217-333-4558 University of Illinois
fax: 217-244-6678 Champaign, IL 61820
On Jun 29, 2004, at 10:26 AM, Ali Hirsa wrote:
> I recently learn about Quantile Regression in R.
> I am trying to study two time series (attached) by Quantile Regression
> in R.
> I wrote the following code and do not know how to interpret the lines.
>
> What kind of information can I get from them? Correlation for
> quantiles,
> conditional probabilties (i.e. P(X in Quantile i | Y in Quantile i)) ,
> and etc.
> Many thanks in advance for any help.
>
> Best,
> Ali
>
> library("quantreg")
> #help.start()
>
> Data <- read.table("RESvsMOVE2.dat")
> #
> x <- Data[,2]
> y <- Data[,1]
>
> par(mfrow=c(2,2))
>
> qqnorm(x,main="MOVE Norm Q-Q Plot", xlab="Normal Qunatiles",ylab =
> "MOVE Quantiles")
> qqline(x)
>
> qqnorm(y,main="Residuals Norm Q-Q Plot", xlab="Normal Qunatiles",ylab
> = "Residuals Quantiles")
> qqline(y)
>
> plot(x,y,xlab="MOVE",ylab="Residuals",cex=.5)
>
> xx <- seq(min(x),max(x),.5)
>
> # Just a linear regression
> g <- coef(lm(y~x))
> yy <- (g[1]+g[2]*(xx))
> lines(xx,yy,col="yellow")
>
> taus <- c(.05,.1,.25,.5,.75,.9,.95)
>
> for(tau in taus){
> f <- coef(rq(y~x,tau=tau,method="pfn"))
> yy <- (f[1]+f[2]*(xx))
> if (tau ==.05){
> lines(xx,yy,col="red")
> }
> if (tau ==.95){
> lines(xx,yy,col="green")
> }
> if (tau != .05 & tau != .95){
> lines(xx,yy,col="blue")
> }
> }
>
>
>
>
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>
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