lqm.fit.gs {lqmm} | R Documentation |
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 |
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
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)