lqmm (version 1.01)

lqm.fit.gs: 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, 2011).

Usage

lqm.fit.gs(theta, x, y, weights, iota, 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.

iota

the quantile to be estimated.

control

list of control parameters used for optimization (see lqmControl).

Value

• An object of class list containing the following components:
• theta
  a vector of coefficients.
• scale
  the scale parameter.
• logLik
  the log--likelihood.
• opt
  number of iterations when the estimation algorithm stopped.
• .

Details

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

References

Bottai M, Orsini N and Geraci M (2011). A Gradient Search Maximization Algorithm for Laplace Likelihood. Unpublished manuscript.

See Also

lqm

Examples

set.seed(12356)
n <- 200
p <- 1:3/4
test <- data.frame(x = runif(n,0,1))
test$y <- 30 + test$x + rnorm(n)
lqm.ls <- lqm(y ~ x, data = test, iota = p, fit = FALSE)

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