lqm: 'lqm' fits linear quantile regression model

In AntoineBbi/BQt: BQt: Bayesian Quantile regression for joint modeling

lqm fits linear quantile regression model

Description

Function using JAGS to estimate the linear quantile regression model assuming asymmetric Laplace distribution for residual error.

Usage

lqm(
  formula,
  data,
  tau = 0.5,
  n.chains = 3,
  n.iter = 10000,
  n.burnin = 5000,
  n.thin = 1,
  n.adapt = NULL,
  save_jagsUI = FALSE,
  parallel = FALSE
)

Arguments

formula	formula for the quantile regression including response variable
data	dataset of observed variables
tau	the quantile(s) to be estimated. This must be a number between 0 and 1, otherwise the execution is stopped. If more than one quantile is specified, rounding off to the 4th decimal must give non–duplicated values of tau, otherwise the execution is stopped.
n.chains	the number of parallel chains for the model; default is 1.
n.iter	integer specifying the total number of iterations; default is 10000
n.burnin	integer specifying how many of n.iter to discard as burn-in ; default is 5000
n.thin	integer specifying the thinning of the chains; default is 1
n.adapt	integer specifying the number of iterations to use for adaptation; default is NULL
save_jagsUI	If TRUE (is FALSE by default), the output of jagsUI package is return by the function
parallel	see jagsUI::jags() function

Value

A BQt object which is a list with the following elements:

mean

list of posterior mean for each parameter

median

list of posterior median for each parameter

modes

list of posterior mode for each parameter

StErr

list of standard error for each parameter

StDev

list of standard deviation for each parameter

Rhat

Gelman and Rubin diagnostic for all parameters

ICs

list of the credibility interval at 0.95 for each parameters excepted for covariance parameters in covariance matrix of random effects. Otherwise, use save_jagsUI=TRUE to have the associated quantiles.

data

data included in argument

sims.list

list of the MCMC chains of the parameters and random effects

control

list of arguments giving details about the estimation

W

list including both posterior mean and posterior standard deviation of subject-specific random varaible W

out_jagsUI

only if save_jagsUI=TRUE in argument: list including posterior mean, median, quantiles (2.5 Moreover, this list also returns the MCMC draws, the Gelman and Rubin diagnostics (see output of jagsUI objects)

Author(s)

Antoine Barbieri

Examples

#---- Use data
data(wave)

#---- Fit regression model for the first quartile
lqm_025 <- lqm(formula = h110d~vent_vit_moy,
               data = wave,
               n.iter = 1000,
               n.burnin = 500,
               tau = 0.25)

#---- Get the posterior mean of parameters
lqm_025$mean

#---- Summary of output
summary(lqm_025)

AntoineBbi/BQt documentation built on June 25, 2022, 3:32 p.m.