Brq (version 2.0)

BBrq: Bayesian binary Quantile Regression

Description

This function implements the idea of Bayesian binary quantile regression using a likelihood function that is based on the asymmetric Laplace distribution. The asymmetric Laplace error distribution is written as scale mixtures of normal distributions. In this function, a two-level hierarchical Bayesian model is used. Specifically, I put zero mean Gaussian priors on the regression coefficients with non informative Jeffreys prior distributions for the unknown variances.

Usage

BBrq(formula, tau =0.5, runs =11000, burn =1000)

Arguments

formula

Model formula.

tau

The quantile of interest. Must be between 0 and 1.

runs

Length of desired Gibbs sampler output.

burn

Number of Gibbs sampler iterations before output is saved.

References

[1] Alhamzawi, R. (2014). Model selection in quantile regression models. Journal of Applied Statistics, 42, 445-458.

Examples

# NOT RUN {
#set.seed(1234)
n <- 200
x=rnorm(n)
ystar <-  1 + x + rnorm(n=n, mean=0, sd=1)
y=as.numeric(ystar>0)
fit = BBrq(y~x,tau=0.5,runs=3000, burn=1000)
# Note: runs =11000 and burn =1000
fit$coef
# }