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movieROC  

Visualizing the Decision Rules Underlying Binary Classification
View on CRAN: Click here


Download and install movieROC package within the R console
Install from CRAN:
install.packages("movieROC")

Install from Github:
library("remotes")
install_github("cran/movieROC")

Install by package version:
library("remotes")
install_version("movieROC", "0.1.0")



Attach the package and use:
library("movieROC")
Maintained by
Sonia Perez-Fernandez
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2024-02-05
Latest Update: 2024-02-05
Description:
Visualization of decision rules for binary classification and Receiver Operating Characteristic (ROC) curve estimation under different generalizations: - making the classification subsets flexible to cover those scenarios where both extremes of the marker are associated with a higher risk of being positive, considering two thresholds (gROC curve); - transforming the marker by a function either defined by the user or resulting from a logistic regression model (hROC curve); - considering a linear transformation with some fixed parameters introduced by the user, dynamic parameters or empirically maximizing TPR for each FPR for a bivariate marker. Also a quadratic transformation with particular coefficients or a function fitted by a logistic regression model can be considered (biROC curve); - considering a linear transformation with some fixed parameters introduced by the user, dynamic parameters or a function fitted by a logistic regression model (multiROC curve). The classification regions behind each point of the ROC curve are displayed in both fixed graphics (plot.buildROC(), plot.regions() or plot.funregions() function) or videos (movieROC() function).
How to cite:
Sonia Perez-Fernandez (2024). movieROC: Visualizing the Decision Rules Underlying Binary Classification. R package version 0.1.0, https://cran.r-project.org/web/packages/movieROC
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