lqm.fit.gs: Quantile Regression Fitting by Gradient Search

In lqmm: Linear Quantile Mixed Models

Quantile Regression Fitting by Gradient Search

Description

This function controls the arguments to be passed to routines written in C for LQM estimation. The optimization algorithm is based on the gradient of the Laplace log–likelihood (Bottai, Orsini and Geraci, 2013).

Usage

lqm.fit.gs(theta, x, y, weights, tau, control)

Arguments

theta	starting values for the regression coefficients.
x	the model matrix.
y	the model response.
weights	the weights used in the fitting process.
tau	the quantile to be estimated.
control	list of control parameters used for optimization (see lqmControl).

Details

See argument fit in lqm for generating a list of arguments to be called by this function.

Value

An object of class list containing the following components:

theta	a vector of coefficients.
scale	the scale parameter.
gradient	the gradient.
logLik	the log–likelihood.
opt	number of iterations when the estimation algorithm stopped.

.

Author(s)

Marco Geraci

References

Bottai M, Orsini N, Geraci M (2014). A Gradient Search Maximization Algorithm for the Asymmetric Laplace Likelihood, Journal of Statistical Computation and Simulation, 85, 1919-1925.

See Also

lqm

Examples

set.seed(123)
n <- 500
test <- data.frame(x = runif(n,0,1))
test$y <- 30 + test$x + rnorm(n)
lqm.ls <- lqm(y ~ x, data = test, fit = FALSE)

do.call("lqm.fit.gs", lqm.ls)

lqmm documentation built on April 6, 2022, 5:09 p.m.