lqm function

lqm fits linear quantile regression model

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

lqm(
  formula,
  data,
  tau = 0.5,
  n.chains = 3,
  n.iter = 10000,
  n.burnin = 5000,
  n.thin = 1,
  n.adapt = NULL,
  save_jagsUI = TRUE,
  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 TRUE by default), the output of jagsUI package is return by the function
• parallel: see jagsUI::jags() function

Returns

A Blqm 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 variable W
• out_jagsUI: only if save_jagsUI=TRUE in argument: list including posterior mean, median, quantiles (2.5%, 25%, 50%, 75%, 97.5%), standard deviation for each parameter and each random effect. Moreover, this list also returns the MCMC draws, the Gelman and Rubin diagnostics (see output of jagsUI objects)

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

#---- Visualize the trace for beta parameters
jagsUI::traceplot(lqm_025$out_jagsUI, parameters = "beta" )

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

Author(s)

Antoine Barbieri