Recursive Partitioning with Nonlinear Models of Change |
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| Authors: | Gabriela Stegmann Ross Jacobucci Sarfaraz Serang Kevin J. Grimm |
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| Affiliation: | 1. Department of Psychology, Arizona State University, Tempe, Arizona, USAGabriela.Stegmann@asu.edu;3. Department of Psychology, University of Notre Dame, Notre Dame, Indiana, USA;4. Department of Psychology, University of Southern California, Los Angeles, California, USA;5. Department of Psychology, Arizona State University, Tempe, Arizona, USA |
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| 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. |
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| Keywords: | Longitudinal recursive partitioning nonlinear mixed-effects models decision trees growthcurve model longitudinal data |
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