CRAN/E | movieROC

movieROC

Visualizing the Decision Rules Underlying Binary Classification

Installation

About

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).

Key Metrics

Version 0.1.0
R ≥ 3.5.0
Published 2024-02-05 242 days ago
Needs compilation? no
License GPL-3
CRAN checks movieROC results

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Maintainer

Maintainer

Sonia Perez-Fernandez

Authors

Sonia Perez-Fernandez

aut / cre

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Depends

R ≥ 3.5.0

Imports

rms
animation
intrval
gtools
e1071
robustbase
Rsolnp
ks
zoo