Package: tfCox 0.1.0
tfCox: Fits Piecewise Polynomial with Data-Adaptive Knots in Cox Model
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Authors:
tfCox_0.1.0.tar.gz
tfCox_0.1.0.zip(r-4.7-x86_64)tfCox_0.1.0.zip(r-4.7-arm64)tfCox_0.1.0.zip(r-4.6-x86_64)tfCox_0.1.0.zip(r-4.6-arm64)tfCox_0.1.0.zip(r-4.5-x86_64)
tfCox_0.1.0.tgz(r-4.6-x86_64)tfCox_0.1.0.tgz(r-4.6-arm64)tfCox_0.1.0.tgz(r-4.5-x86_64)tfCox_0.1.0.tgz(r-4.5-arm64)
tfCox_0.1.0.tar.gz(r-4.7-arm64)tfCox_0.1.0.tar.gz(r-4.7-x86_64)tfCox_0.1.0.tar.gz(r-4.6-arm64)tfCox_0.1.0.tar.gz(r-4.6-x86_64)
tfCox_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
tfCox/json (API)
| # Install 'tfCox' in R: |
| install.packages('tfCox', repos = c('https://wujiacheng.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 from:2ee11a883a. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 128 | ||
| linux-devel-x86_64 | OK | 110 | ||
| source / vignettes | OK | 202 | ||
| linux-release-arm64 | OK | 84 | ||
| linux-release-x86_64 | OK | 103 | ||
| macos-release-arm64 | OK | 159 | ||
| macos-release-x86_64 | OK | 233 | ||
| macos-oldrel-arm64 | OK | 163 | ||
| macos-oldrel-x86_64 | OK | 284 | ||
| windows-devel-arm64 | OK | 351 | ||
| windows-devel-x86_64 | OK | 159 | ||
| windows-release-arm64 | OK | 349 | ||
| windows-release-x86_64 | OK | 120 | ||
| windows-oldrel-x86_64 | OK | 118 | ||
| wasm-release | OK | 292 |
Exports:cv_tfCoxnegloglikplot.cv_tfCoxplot.sim_datplot.tfCoxpredict_best_lambdapredict.tfCoxsim_datsummary.cv_tfCoxsummary.tfCoxtfCoxtfCox_choose_lambda
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit the Additive Trend Filtering Cox Model | tfCox-package |
| Fit Trend Filtering Cox model and Choose Tuning Parameter via K-Fold Cross-Validation | cv_tfCox |
| Calculate the negative log likelihood from Cox model. | negloglik |
| Plots Cross-Validation Curve for Object of Class "cv_tfCox" | plot.cv_tfCox |
| Plot the true covariate effects | plot.sim_dat |
| Plot Fitted Functions from Class "tfCox" | plot.tfCox |
| Predict from the optimal lambda from tfCox_choose_lambda | predict_best_lambda |
| Predict for a New Covariate Matrix and fit from 'tfCox' | predict.tfCox |
| Simulate Data from a Variety of Functional Scenarios | sim_dat |
| Summarize 'cv_tfCox' object | summary.cv_tfCox |
| Summarize 'tfCox' object | summary.tfCox |
| Fit the additive trend filtering Cox model with a range of tuning parameters | tfCox |
| Choose the tuning parameter lambda using training and testing dataset | tfCox_choose_lambda |