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Description

FREEtree, a tree-based method for high dimensional longitudinal data with correlated features. 'FREEtree' deals with longitudinal data by using a piecewise random effect model. It 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, 'FREEtree' maintains 'interpretability' and improves computational efficiency.

Authors

  • Yuancheng Xu
  • Athanasse Zafirov
  • Dan Kojis
  • Min Tan
  • Mike Alvarez
  • Christina Ramirez

License

This project is licensed under GPL-3

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Version

Install

install.packages('FREEtree')

Monthly Downloads

172

Version

0.1.0

License

GPL-3

Maintainer

Athanasse Zafirov

Last Published

June 25th, 2020

Functions in FREEtree (0.1.0)

FREEtree_PC

Version of FREEtree called when fixed_regress is NULL, uses principal components (PC) as regressors for non-grey modules.
data

A dataset containing simulated feature long and wide data. The last six columns contain outcome variable, patient ID, treatment, time and time squared features.
get_split_names

Method for extracting names of splitting features used in a tree.
FREEtree_time

Version of FREEtree called when var_select and fixed_regress are specified,
FREEtree

Initial FREEtree call which then calls actual FREEtree methods depending on parameters being passed through.