close

Showing 2 of total 2 results (show query)

perezsonia

movieROC:Visualization for Classification Regions for ROC Curve Generalization

Tools for estimating the Receiver Operating Characteristic (ROC) curve 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 resulting from Meisner et al. (2017) approach 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 resulting from Meisner et al. (2017) approach 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).

Maintained by Sonia Perez Fernandez. Last updated 4 years ago.

2.00 score 2 scripts

cran

nsROC:Non-Standard ROC Curve Analysis

Tools for estimating Receiver Operating Characteristic (ROC) curves, building confidence bands, comparing several curves both for dependent and independent data, estimating the cumulative-dynamic ROC curve in presence of censored data, and performing meta-analysis studies, among others.

Maintained by Sonia Perez Fernandez. Last updated 7 years ago.

1 stars 1.30 score

: