Abstract
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.
Original language | English (US) |
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Pages (from-to) | 559-570 |
Number of pages | 12 |
Journal | Multivariate Behavioral Research |
Volume | 53 |
Issue number | 4 |
DOIs | |
State | Published - Jul 4 2018 |
Keywords
- Longitudinal recursive partitioning
- decision trees
- growthcurve model
- longitudinal data
- nonlinear mixed-effects models
ASJC Scopus subject areas
- Statistics and Probability
- Experimental and Cognitive Psychology
- Arts and Humanities (miscellaneous)