Nonlinear Mixed-Effects Modeling Programs in R

被引：30

作者：

Stegmann, Gabriela ^{[1
]}

Jacobucci, Ross ^{[2
]}

Harring, Jeffrey R. ^{[3
]}

Grimm, Kevin J. ^{[1
]}

机构：

[1] Arizona State Univ, Tempe, AZ USA

[2] Univ Notre Dame, Notre Dame, IN 46556 USA

[3] Univ Maryland, College Pk, MD 20742 USA

来源：

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL | 2018年 / 25卷 / 01期

基金：

美国国家科学基金会;

关键词：

nonlinear mixed-effects models; R software; mixed-effects model functions in R; mixed-effects modeling programs in R; MAXIMUM-LIKELIHOOD;

D O I：

10.1080/10705511.2017.1396187

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：160 / 165

页数：6

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