Package: SurvivalClusteringTree 1.1.1
Lu You
SurvivalClusteringTree: Clustering Analysis Using Survival Tree and Forest Algorithms
An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.
Authors:
SurvivalClusteringTree_1.1.1.tar.gz
SurvivalClusteringTree_1.1.1.tar.gz(r-4.5-noble)SurvivalClusteringTree_1.1.1.tar.gz(r-4.4-noble)
SurvivalClusteringTree_1.1.1.tgz(r-4.4-emscripten)SurvivalClusteringTree_1.1.1.tgz(r-4.3-emscripten)
SurvivalClusteringTree.pdf |SurvivalClusteringTree.html✨
SurvivalClusteringTree/json (API)
| # Install 'SurvivalClusteringTree' in R: |
| install.packages('SurvivalClusteringTree', repos = 'https://cloud.r-project.org') |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 10 months agofrom:d74f63e1aa. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 21 2025 |
| R-4.5-linux-x86_64 | OK | Mar 21 2025 |
| R-4.4-linux-x86_64 | OK | Mar 21 2025 |
Exports:plot_survival_treepredict_distance_forestpredict_distance_forest_matrixpredict_distance_treepredict_distance_tree_matrixpredict_weightspredict_weights_matrixsurvival_forestsurvival_forest_matrixsurvival_treesurvival_tree_matrix
Dependencies:clicommonmarkcurldplyrfansiformula.toolsgenericsgluegridtextjpeglatticelifecyclemagrittrmarkdownMatrixoperator.toolspillarpkgconfigpngR6RcppRcppArmadillorlangstringistringrsurvivaltibbletidyselectutf8vctrswithrxfunxml2
Citation
To cite package ‘SurvivalClusteringTree’ in publications use:
You L, Ferrat L, Parikh H, Huo Y, Yang Y (2024). SurvivalClusteringTree: Clustering Analysis Using Survival Tree and Forest Algorithms. R package version 1.1.1, https://CRAN.R-project.org/package=SurvivalClusteringTree.
Corresponding BibTeX entry:
@Manual{,
title = {SurvivalClusteringTree: Clustering Analysis Using Survival
Tree and Forest Algorithms},
author = {Lu You and Lauric Ferrat and Hemang Parikh and Yanan Huo
and Yuting Yang},
year = {2024},
note = {R package version 1.1.1},
url = {https://CRAN.R-project.org/package=SurvivalClusteringTree},
}
Readme and manuals
SurvivalClusteringTree
Outcome-guided clustering by recursive partitioning methods.
Installation
remotes::install_github("luyouepiusf/SurvivalClusteringTree")
Introduction
Suppose that we are given a dataset of samples with survival outcomes and sample features. The main objective of the method is to identify several clusters of samples with similar survival outcomes and similar features. The package implements a novel statistical machine learning method to perform the task.
Algorithm
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Perform bootstrap aggregation. For each bootstrap dataset, build a survival tree by recursively sweeping through all variables to find the best split in the sample space, and repeat until no further splits can be found.
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Define a distance between each pair of samples in the original dataset with respect to the survival tree. The distances between samples in the same terminal node are 0. For samples that are not in the same terminal node, there is a unique path on the tree connecting the two terminal nodes, the sum of the absolute log-rank test z-scores on the path is the distance between them.
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Aggregate the distances by averaging pairwise distances as the final distance.
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Apply hierarchical clustering analysis to the distance matrix to identify clusters.
