Tree Method for High Dimensional Longitudinal Data

This tree-based method deals with high dimensional longitudinal data with correlated features through the use of a piecewise random effect model. FREE tree also exploits the network structure of the features, by first clustering them using Weighted Gene Co-expression Network Analysis ('WGCNA'). It then conducts a screening step within each cluster of features and a selecting step among the surviving features, which provides a relatively unbiased way to do feature selection. By using dominant principle components as regression variables at each leaf and the original features as splitting variables at splitting nodes, FREE tree delivers easily interpretable results while improving computational efficiency.


Reference manual

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0.1.0 by Athanasse Zafirov, a year ago

Browse source code at

Authors: Yuancheng Xu [aut] , Athanasse Zafirov [cre] , Christina Ramirez [aut] , Dan Kojis [aut] , Min Tan [aut] , Mike Alvarez [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports glmertree, pre, WGCNA, MASS

Suggests knitr, rmarkdown, testthat

See at CRAN