qrNLMM-package {qrNLMM}

Package for Quantile Regression for Linear Mixed-Effects Model

Description

This package contains a principal function that performs a quantile regression for a Nonlinear 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:	qrNLMM
Type:	Package
Version:	1.0
Date:	2015-01-30
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

Galarza, C.E., Castro, L.M., Louzada, F. & Lachos, V. (2020) Quantile regression for nonlinear mixed effects models: a likelihood based perspective. Stat Papers 61, 1281-1307. doi:10.1007/s00362-018-0988-y

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

Soybean, HIV, QRNLMM, lqr , group.plots

Examples

#See examples for the QRNLMM function linked above.