lqr: Linear Quantile Regression

In lqmix: Linear Quantile Mixture Models

Linear Quantile Regression

Description

Estimate a linear quantile regression model for independent data (no random coefficients).

Usage

lqr(formula, data, qtl = 0.5, se = TRUE, R = 100, verbose = TRUE,
  seed = NULL, parallel = FALSE, ncores = 2, ...)

Arguments

formula	an object of class formula: a symbolic description of the model to be fitted
data	a data frame containing the variables named in formula
qtl	quantile to be estimated
se	standard error computation
R	number of bootstrap samples for computing standard errors
verbose	if set to FALSE, no printed output is given during the function execution
seed	an integer value for random numbers generation, used for bootstrap standard errors
parallel	if set to TRUE, a parallelized code is use for standard error computation (if se=TRUE)
ncores	number of cores used for computing bootstrap standard errors (if required)
...	not used

Details

The function computes ML estimates for the parameters of a linear quantile regression model for independent observations. Estimates are derived by maximizing the (log-)likelihood of a Laplace regression, where the location parameter is modeled as a function of fixed coefficients only.

If se=TRUE, standard errors based on a bootstrap procedure are computed.

Value

Return an object of class lqr. This is a list containing the following elements:

betaf	a vector containing fixed regression coefficients
scale	the scale parameter
sigma.e	the standard deviation of error terms
lk	the log-likelihood
npar	the total number of model parameters
AIC	the AIC value
BIC	the BIC value
qtl	the estimated quantile
nobs	the total number of observations
se.betaf	the standard errors for the regression coefficients
se.scale	the standard error for the scale parameter
model	the estimated model
mmf	the model matrix associated to the regression coefficients
y	the model response
call	the matched call
formula	the model formula

References

\insertRef

ref:KoeBaslqmix

Examples

out0 = lqr(formula=meas~trt+time+trt:time,data=pain,se=TRUE,R=10)

lqmix documentation built on Nov. 5, 2025, 7:15 p.m.