Tree Method for High Dimensional Longitudinal Data
Initial FREEtree call which then calls actual FREEtree methods dependi...
Version of FREEtree called when fixed_regress is NULL, uses principal ...
Version of FREEtree called when var_select and fixed_regress are speci...
Method for extracting names of splitting features used in a tree.
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.