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 [EMAIL PROTECTED] 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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