lqmm.fit.df: Linear Quantile Mixed Models Fitting by Derivative-Free...

In lqmm: Linear Quantile Mixed Models

Linear Quantile Mixed Models Fitting by Derivative-Free Optimization

Description

This function controls the arguments to be passed to optim and optimize for LQMM estimation.

Usage

lqmm.fit.df(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.df (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.
logLik	the log–likelihood.
opt	number of iterations when the estimation algorithm stopped for lower (theta) and upper (scale) loop.

.

Author(s)

Marco Geraci

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.df", lqmm.ls)

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