Recursive Partitioning with Nonlinear Models of Change.
Multivariate Behav Res
; 53(4): 559-570, 2018.
Article
en En
| MEDLINE
| ID: mdl-29683722
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Interpretación Estadística de Datos
/
Dinámicas no Lineales
Tipo de estudio:
Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Child
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Humans
Idioma:
En
Revista:
Multivariate Behav Res
Año:
2018
Tipo del documento:
Article
País de afiliación:
Estados Unidos