Btqr {Brq} | R Documentation |
Bayesian tobit quantile regression
Description
This function implements the idea of Bayesian tobit quantile regression employing a likelihood function that is based on the asymmetric Laplace distribution (Yu and Stander, 2007). The asymmetric Laplace error distribution is written as scale mixtures of normal distributions as in Reed and Yu (2009).
Usage
Btqr(x,y, tau = 0.5, left = 0, runs = 11000, burn = 1000, thin=1)
Arguments
x |
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y |
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tau |
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left |
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runs |
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burn |
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thin |
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Author(s)
Rahim Alhamzawi
Examples
# Example
set.seed(12345)
x <- abs(rnorm(100))
y <- -0.5 + x +(.25 + .25*x)*rnorm(100)
plot(x,y, type="n")
h <-(y > 0)
points(x[h],y[h],cex=.9,pch=16)
points(x[!h],y[!h],cex=.9,pch=1)
y <- pmax(0,y)
for(tau in (2:8)/9){
fit=Brq(y~x,tau=tau, method="Btqr", left=0, runs=1000, burn=500)$coef
# Note: runs =11000 and burn =1000
Xs=sort(x)
Xc=cbind(1,sort(x))
Xcf=Xc%*%c(fit)
Xcfp=pmax(0,Xcf)
lines(Xs,Xcfp,col="red")}
[Package Brq version 3.0 Index]