Nonlinear Mixed-Effects Modeling Programs in R

Gabriela Stegmann, Ross Jacobucci, Jeffrey R. Harring, Kevin Grimm

Research output: Contribution to journalReview article

3 Citations (Scopus)

Abstract

In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using Stan). We briefly describe the approaches used, provide a sample code, and highlight strengths and weaknesses of each.

Original languageEnglish (US)
Pages (from-to)160-165
Number of pages6
JournalStructural Equation Modeling
Volume25
Issue number1
DOIs
StatePublished - Jan 2 2018

Fingerprint

Nonlinear Mixed Effects Model
Linear Mixed Effects Model
Mixed Effects
Expectation Maximization
Stochastic Approximation
Nonlinear Effects
Bayesian Model
Linear Program
Regression Model
Software
Modeling
Estimate
regression
Review

Keywords

  • mixed-effects model functions in R
  • mixed-effects modeling programs in R
  • nonlinear mixed-effects models
  • R software

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

Cite this

Nonlinear Mixed-Effects Modeling Programs in R. / Stegmann, Gabriela; Jacobucci, Ross; Harring, Jeffrey R.; Grimm, Kevin.

In: Structural Equation Modeling, Vol. 25, No. 1, 02.01.2018, p. 160-165.

Research output: Contribution to journalReview article

Stegmann, Gabriela ; Jacobucci, Ross ; Harring, Jeffrey R. ; Grimm, Kevin. / Nonlinear Mixed-Effects Modeling Programs in R. In: Structural Equation Modeling. 2018 ; Vol. 25, No. 1. pp. 160-165.
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