lqr {lqmix}

Linear Quantile Regression

Description

Estimate a linear quantile regression model with no random coefficients

Usage

lqr(formula, data, qtl = 0.5, se = TRUE, R = 50, verbose = TRUE, ...)

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 and time

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

...

further arguments to be passed to or from methods

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 fixed regression coefficients

se.scale

the standard error for the scale parameter

model

the estimated model

call

the matched call

References

Geraci M, Bottai M (2007). “Quantile regression for longitudinal data using the asymmetric Laplace distribution.” Biostatistics, 8, 140-54.

Examples

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