Package: uni.survival.tree 1.5
Takeshi Emura
uni.survival.tree: A Survival Tree Based on Stabilized Score Tests for High-dimensional Covariates
A classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).
Authors:
uni.survival.tree_1.5.tar.gz
uni.survival.tree_1.5.tar.gz(r-4.5-noble)uni.survival.tree_1.5.tar.gz(r-4.4-noble)
uni.survival.tree_1.5.tgz(r-4.4-emscripten)uni.survival.tree_1.5.tgz(r-4.3-emscripten)
uni.survival.tree.pdf |uni.survival.tree.html✨
uni.survival.tree/json (API)
| # Install 'uni.survival.tree' in R: |
| install.packages('uni.survival.tree', repos = c('https://cran.r-universe.dev', '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 3 years agofrom:bca81299a7. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 16 2024 |
| R-4.5-linux | OK | Sep 16 2024 |
Exports:feature.selectedKM.splitrisk.classificationuni.logrankuni.treeX.pathway_discrete.balancedX.pathway_discrete.imbalanced
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| The names of features that are selected in a tree | feature.selected |
| Kaplan-Meier estimator of binary splitting | KM.split |
| The risk ranks of the samples predicted by a tree | risk.classification |
| Univariate binary splits by the logrank test | uni.logrank |
| A survival tree based on stabilized score tests | uni.tree |
| Generate a matrix of gene expressions (discrete version of X.pathway() against to Emura (2012)) in the presence of gene pathways | X.pathway_discrete.balanced |
| Generate a matrix of unbalance gene expressions (discrete version of X.pathway() against to Emura (2012)) in the presence of gene pathways | X.pathway_discrete.imbalanced |