The R Journal: accepted article

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GsymPoint: An R Package to Estimate the Generalized Symmetry Point, an Optimal Cut-off Point for Binary Classification in Continuous Diagnostic Tests
Mónica López-Ratón, Elisa M. Molanes-López, Emilio Letón and Carmen Cadarso-Suárez

Abstract In clinical practice, it is very useful to select an optimal cutpoint in the scale of a continuous biomarker or diagnostic test for classifying individuals as healthy or diseased. Several methods for choosing optimal cutpoints have been presented in the literature, depending on the ultimate goal. One of these methods, the Generalized Symmetry point, recently introduced, generalizes the Symmetry point by incorporating the misclassification costs. Two statistical approaches have been proposed in the literature for estimating this optimal cutpoint and its associated sensitivity and specificity measures, a parametric method based on the Generalized Pivotal Quantity and a nonparametric method based on Empirical Likelihood. In this paper, we introduce GsymPoint, an R package that implements these methods in a user-friendly environment, allowing the end-user to calculate the Generalized Symmetry point depending on the levels of certain categorical covariates. The practica l use of this package is illustrated using three real biomedical datasets.

Received: 2016-08-25; online 2017-05-10
CRAN packages: GsymPoint, PresenceAbsence, DiagnosisMed, pROC, OptimalCutpoints, GsymPoint


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@article{RJ-2017-015,
  author = {Mónica López-Ratón and Elisa M. Molanes-López and Emilio
          Letón and Carmen Cadarso-Suárez},
  title = {{GsymPoint: An R Package to Estimate the Generalized Symmetry
          Point, an Optimal Cut-off Point for Binary Classification in
          Continuous Diagnostic Tests}},
  year = {2017},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2017/RJ-2017-015/index.html}
}