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

In lqmm: Linear Quantile Mixed Models

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, 2014; Geraci and Bottai, 2014).

Usage

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

Arguments

theta_0	starting values for the linear predictor.
x	the model matrix for fixed effects (see details).
y	the model response (see details).
z	the model matrix for random effects (see details).
weights	the weights used in the fitting process (see details).
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.
tau	the quantile(s) to be estimated.
group	the grouping factor (see details).
control	list of control parameters used for optimization (see lqmmControl).

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.

NOTE: the data should be ordered by group when passed to lqmm.fit.gs (such ordering is performed by lqmm).

Value

An object of class "list" containing the following components:

theta	a vector of coefficients, including the "raw" variance–covariance parameters (see VarCorr.lqmm).
scale	the scale parameter.
gradient	the gradient.
logLik	the log–likelihood.
opt	number of iterations when the estimation algorithm stopped for lower (theta) and upper (scale) loop.

.

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 (in press).

Geraci M and Bottai M (2014). Linear quantile mixed models. Statistics and Computing, 24(3), 461–479.

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)

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