a Department of Psychology , Arizona State University , Tempe , Arizona , USA.
b Department of Psychology , University of Notre Dame , Notre Dame , Indiana , USA.
Multivariate Behav Res. 2018 Jul-Aug;53(4):559-570. doi: 10.1080/00273171.2018.1461602. Epub 2018 Apr 23.
In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear longitudinal trajectories. At each node, a user-specified linear or nonlinear mixed-effects model is estimated. This method is an extension of Abdolell et al.'s (2002) longitudinal recursive partitioning while permitting a nonlinear mixed-effects model in addition to a linear mixed-effects model in each node. We give an overview of recursive partitioning, nonlinear mixed-effects models for longitudinal data, describe nLRP, and illustrate its use with empirical data from the Early Childhood Longitudinal Study-Kindergarten Cohort.
在本文中，我们介绍非线性纵向递归分区（nLRP）和 R 包 longRpart2 来进行分析。该方法实现递归分区（也称为决策树），以便根据个体（即聚类）水平协变量对数据进行分割，目的是预测非线性纵向轨迹的差异。在每个节点上，估计用户指定的线性或非线性混合效应模型。这种方法是 Abdolell 等人（2002）纵向递归分区的扩展，同时允许在每个节点中除了线性混合效应模型之外还使用非线性混合效应模型。我们概述了递归分区、纵向数据的非线性混合效应模型，描述了 nLRP，并使用早期儿童纵向研究-幼儿园队列的实证数据说明了其用法。
