lqmm (version 1.01)

lqmm.fit.gs: Linear Quantile Mixed Models Fitting by Gradient Search

Description

This function controls the arguments to be passed to routines written in C for LQMM estimation. The optimization algorithm is based on the gradient of the Laplace log--likelihood (Bottai, Orsini and Geraci, 2011; Geraci and Bottai, 2011).

Usage

lqmm.fit.gs(theta_0, x, y, z, weights, cov_name, V, W, sigma_0, iota,
	group, control)

Arguments

theta_0

starting values for the linear predictor.

x

the model matrix for fixed effects.

y

the model response.

z

the model matrix for random effects.

weights

the weights used in the fitting process.

cov_name

variance--covariance matrix of the random effects. Default is pdIdent. See details.

V

nodes of the quadrature.

W

weights of the quadrature.

sigma_0

starting value for the scale parameter.

iota

the quantile(s) to be estimated.

group

the grouping factor.

control

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

Value

• An object of class "list" containing the following components:
• theta
  a vector of coefficients, including the "raw" variance--covariance parameters (see cov.lqmm).
• scale
  the scale parameter.
• logLik
  the log--likelihood.
• opt
  number of iterations when the estimation algorithm stopped for lower (theta) and upper (scale) loop.
• .

Details

In lqmm, see argument fit for generating a list of arguments to be called by this function; see argument covariance for alternative variance--covariance matrices.

References

Bottai M, Orsini N and Geraci M (2011). A Gradient Search Maximization Algorithm for Laplace Likelihood. Unpublished manuscript. Geraci M and Bottai M (1 June 2011). Linear Quantile Mixed Models. Unpublished manuscript.

See Also

lqmm

Examples

set.seed(123)

M <- 50
n <- 10
test <- data.frame(x = runif(n*M,0,1), group = rep(1:M,each=n))
test$y <- 10*test$x + rep(rnorm(M, 0, 2), each = n) + rchisq(n*M, 3)
lqmm.ls <- lqmm(fixed = y ~ x, random = ~ 1, group = group,
	data = test, fit = FALSE)

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