ArticlePsychological methods2016
Finding structure in data using multivariate tree boosting.
Article in Psychological methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Protocol for a randomized controlled trial in Cambodian individuals with PTSD: Trauma-Informed Treatment Algorithms for Advancing Novel Outcomes (Project TITAN).Contemporary clinical trials · 2023Article
- Assessing the United Nations sustainable development goals from the inclusive wealth perspective.Scientific reports · 2023Article
- Cognitive Phenotypes of HIV Defined Using a Novel Data-driven Approach.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2022Article
- Predicting How Well Adolescents Get Along with Peers and Teachers: A Machine Learning Approach.Journal of youth and adolescence · 2022Article
- Knockoff boosted tree for model-free variable selection.Bioinformatics (Oxford, England) · 2021Article
- Ensemble machine learning classification of daily living abilities among older people with HIV.EClinicalMedicine · 2021Article
- Functional Traits in Lichen Ecology: A Review of Challenge and Opportunity.Microorganisms · 2021Review
- Deep Phenotyping of HIV Neurocognitive Complications Among Individuals Residing in High-Income Countries.Current topics in behavioral neurosciences · 2021Article
- Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.Journal of acquired immune deficiency syndromes (1999) · 2020Article
- Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.AIDS (London, England) · 2020Article
- Parasite associations predict infection risk: incorporating co-infections in predictive models for neglected tropical diseases.Parasites & vectors · 2020Article
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4 authors.
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Abstract
Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause 2 or more outcome variables to covary. We provide the R package "mvtboost" to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package "gbm" (Ridgeway, 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. (PsycINFO Database Record
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