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Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm

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  • Comets, Emmanuelle
  • Lavenu, Audrey
  • Lavielle, Marc

Abstract

The saemix package for R provides maximum likelihood estimates of parameters in nonlinear mixed effect models, using a modern and efficient estimation algorithm, the stochastic approximation expectation maximisation (SAEM) algorithm. In the present paper we describe the main features of the package, and apply it to several examples to illustrate its use. Making use of S4 classes and methods to provide user-friendly interaction, this package provides a new estimation tool to the R community.

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  • Comets, Emmanuelle & Lavenu, Audrey & Lavielle, Marc, 2017. "Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i03).
    • Handle: RePEc:jss:jstsof:v:080:i03
    DOI: http://hdl.handle.net/10.18637/jss.v080.i03
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    References listed on IDEAS

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    1. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    2. Sonja Greven & Thomas Kneib, 2010. "On the behaviour of marginal and conditional AIC in linear mixed models," Biometrika, Biometrika Trust, vol. 97(4), pages 773-789.
    3. Julie Bertrand & Emmanuelle Comets & Marylore Chenel & France Mentré, 2012. "Some Alternatives to Asymptotic Tests for the Analysis of Pharmacogenetic Data Using Nonlinear Mixed Effects Models," Biometrics, The International Biometric Society, vol. 68(1), pages 146-155, March.
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