Recursive Partitioning with Nonlinear Models of Change

Gabriela Stegmann, Ross Jacobucci, Sarfaraz Serang, Kevin Grimm

Research output: Contribution to journalArticle

2 Scopus citations

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 languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalMultivariate Behavioral Research
DOIs
StateAccepted/In press - Apr 20 2018

Keywords

  • decision trees
  • growthcurve model
  • longitudinal data
  • Longitudinal recursive partitioning
  • nonlinear mixed-effects models

ASJC Scopus subject areas

  • Statistics and Probability
  • Experimental and Cognitive Psychology
  • Arts and Humanities (miscellaneous)

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