Estimate a linear quantile regression model with no random coefficients
lqr(formula, data, qtl = 0.5, se = TRUE, R = 50, verbose = TRUE,
parallel = FALSE, ...)Return an object of class
lqr. This is a list containing the following elements:
a vector containing fixed regression coefficients
the scale parameter
the standard deviation of error terms
the log-likelihood
the total number of model parameters
the AIC value
the BIC value
the estimated quantile
the total number of observations
the standard errors for fixed regression coefficients
the standard error for the scale parameter
the estimated model
the matched call
an object of class formula: a symbolic description of the model to be fitted
a data frame containing the variables named in formula and time
quantile to be estimated
standard error computation
number of bootstrap samples for computing standard errors
if set to FALSE, no printed output is given during the function execution
if set to TRUE, a parallelized code is use for standard error computation (if se=TRUE)
further arguments to be passed to or from methods
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.
ref:lqrlqmix
out0 = lqr(formula=meas~trt+time+trt:time,data=pain,se=TRUE,R=10)
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