FREEtree: 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.
| Version: |
0.1.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
glmertree, pre, WGCNA, MASS |
| Suggests: |
knitr, rmarkdown, testthat (≥ 2.1.0) |
| Published: |
2020-06-25 |
| DOI: |
10.32614/CRAN.package.FREEtree |
| Author: |
Yuancheng Xu [aut],
Athanasse Zafirov [cre],
Christina Ramirez [aut],
Dan Kojis [aut],
Min Tan [aut],
Mike Alvarez [aut] |
| Maintainer: |
Athanasse Zafirov <zafirov at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Citation: |
FREEtree citation info |
| Materials: |
README |
| CRAN checks: |
FREEtree results |
Documentation:
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