Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Marco Geraci
Title: Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression
Abstract: Inference in quantile analysis has received considerable attention in the recent years. Linear quantile mixed models (Geraci and Bottai 2014) represent a flexible statistical tool to analyze data from sampling designs such as multilevel, spatial, panel or longitudinal, which induce some form of clustering. In this paper, I will show how to estimate conditional quantile functions with random effects using the R package lqmm. Modeling, estimation and inference are discussed in detail using a real data example. A thorough description of the optimization algorithms is also provided.

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Paper: Linear Quantile Mixed Models: The lqmm Package for Laplace Quantile Regression     Download PDF (Downloads: 11206)
Supplements:
lqmm_1.5.tar.gz: R source package Download (Downloads: 406; 469KB)
v57i13.R: R example code from the paper Download (Downloads: 626; 12KB)
DOI: 10.18637/jss.v057.i13

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.