qrLMM-package: Package for Quantile Regression for Linear Mixed-Effects...

In qrLMM: Quantile Regression for Linear Mixed-Effects Models

Package for Quantile Regression for Linear Mixed-Effects Model

Description

This package contains a principal function that performs a quantile regression for a Linear Mixed-Effects Model using the Stochastic-Approximation of the EM Algorithm (SAEM) for an unique or a set of quantiles.

Exploiting the nice hierarchical representation of the ALD, our classical approach follows the Stochastic Approximation of the EM(SAEM) algorithm for deriving exact maximum likelihood estimates of the fixed-effects and variance components.

Details

Package:	qrLMM
Type:	Package
Version:	1.0
Date:	2014-12-19
License:	What license is it under?

Author(s)

Christian E. Galarza <[e-mail]> and Victor H. Lachos <[e-mail]>

Maintainer: Christian E. Galarza <[e-mail]>

References

Delyon, B., Lavielle, M. & Moulines, E. (1999). Convergence of a stochastic approximation version of the EM algorithm. Annals of Statistics, pages 94-128.

Koenker, R., Machado, J. (1999). Goodness of fit and related inference processes for quantile regression. J. Amer. Statist. Assoc. 94(3):1296-1309.

Yu, K. & Moyeed, R. (2001). Bayesian quantile regression. Statistics & Probability Letters, 54(4), 437-447.

Yu, K., & Zhang, J. (2005). A three-parameter asymmetric Laplace distribution and its extension. Communications in Statistics-Theory and Methods, 34(9-10), 1867-1879.

See Also

Orthodont, Cholesterol, QRLMM, QRNLMM

Examples

#See examples for the QRLMM function linked above.

qrLMM documentation built on Oct. 25, 2022, 5:05 p.m.