bayesQR: Bayesian quantile regression model
In brsantos/baquantreg: Bayesian quantile regression methods

bayesQR

R Documentation

Bayesian quantile regression model

Description

This function estimates a Bayesian quantile regression model The response variable is assumed to follow a asymmetric Laplace distribution.

Usage

bayesQR(
  formula,
  tau = 0.5,
  data,
  itNum,
  thin = 1,
  betaValue = NULL,
  sigmaValue = 1,
  vSampleInit = NULL,
  priorVar = 100,
  hyperSigma = c(0.1, 0.1),
  refresh = 100,
  sigmaSampling = TRUE,
  quiet = T,
  tobit = FALSE,
  recordLat = FALSE
)

Arguments

`formula`	a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.
`tau`	Quantile of interest.
`data`	A data.frame from which to find the variables defined in the formula
`itNum`	Number of iterations.
`thin`	Thinning parameter. Default value is 1.
`betaValue`	Initial values for the parameter beta for the continuous part.
`sigmaValue`	Initial value for the scale parameter.
`vSampleInit`	Initial value for the latent variables.
`priorVar`	Value that multiplies a identity matrix in the elicitation process of the prior variance of the regression parameters.
`hyperSigma`	Vector of size containing the hyperparameters of the inverse gamma distribution for the sigma parameter of the asymmetric Laplace distribution. Default is c(0.1, 0.1), which gives a noninformative prior for sigma.
`refresh`	Interval between printing a message during the iteration process. Default is set to 100.
`sigmaSampling`	If TRUE, the default, the MCMC procedure will draw values from the posterior distribution of sigma. Otherwise, it will fix the value to 1 for all values of the chain.
`quiet`	If TRUE, the default, it does not print messages to check if the MCMC is actually updating. If FALSE, it will use the value of refresh to print messages to control the iteration process.
`tobit`	If TRUE, it will input the censored value for all observations with y = 0, according to the model. If FALSE, the default, it will estimate the parameter without this inputation process.
`recordLat`	If TRUE, it will keep the Markov chain samples for the latent variable. Default is FALSE.