dataset · 2024

RGS: Recursive Gradient Scanning Algorithm

Cited by 0Abstract and details only

Abstract

Provides a recursive gradient scanning algorithm for discretizing continuous variables in Logistic and Cox regression models. This algorithm is especially effective in identifying optimal cut-points for variables with U-shaped relationships to 'lnOR' (the natural logarithm of the odds ratio) or 'lnHR' (the natural logarithm of the hazard ratio), thereby enhancing model fit, interpretability, and predictive power. By iteratively scanning and calculating gradient changes, the method accurately pinpoints critical cut-points within nonlinear relationships, transforming continuous variables into categorical ones. This approach improves risk classification and regression analysis performance, increasing interpretability and practical relevance in clinical and risk management settings.

Cite this paper

APA 7

Yang, S., & Yi, F. (2024). RGS: Recursive Gradient Scanning Algorithm. https://doi.org/10.32614/cran.package.rgs

MLA 9

Yang, Shuo, and Fei Yi. “RGS: Recursive Gradient Scanning Algorithm.” 2024, https://doi.org/10.32614/cran.package.rgs.

Chicago

Yang, Shuo, and Fei Yi. 2024. “RGS: Recursive Gradient Scanning Algorithm.” https://doi.org/10.32614/cran.package.rgs.

Harvard

Yang, S. and Yi, F. (2024) ‘RGS: Recursive Gradient Scanning Algorithm’. Available at: https://doi.org/10.32614/cran.package.rgs.

BibTeX

@misc{yang2024recursive,
  title = {{RGS: Recursive Gradient Scanning Algorithm}},
  author = {Yang, Shuo and Yi, Fei},
  year = {2024},
  doi = {10.32614/cran.package.rgs}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.