treeDA-package: Tree-based discriminant analysis

Description Details Author(s) See Also


A package for performing sparse, tree-based discriminant analysis.


This package contains functions for building sparse, tree-structured models for classification. The method is based on the idea that when our predictors are structured according to a tree, we can create an expanded feature space containing both the original leaf predictors as well as node predictors, which correspond to sums or averages across the leaves descending from them. Without some sort of regularization this problem would be unidentifiable, but with the regularization provided by sparse discriminant analysis we get stable solutions.

The package fits a sparse discriminant model in the expanded feature space and translates the results back to the leaf space, so that the interpretation can be purely in terms of the original predictors. The package also includes functions to perform cross validation to pick the sparsity level and plotting commands to visualize the tree and the fitted coefficient vectors.

The main function in this package is treeda, which fits a sparse tree-based discriminant model. Additional functions provided are treedacv, which performs cross-validation to determine the correct sparsity level, and functions to plot the resulting coefficient vectors along the tree (plot_coefficients).


Maintainer: Julia Fukuyama

See Also

Useful links:

treeDA documentation built on May 2, 2019, 5:42 a.m.