ggroc: The ROC plot

In JohnPickering/risk-assessment-plot-package: Risk Assessment Plot and reclassification metrics

The ROC plot

Description

ggroc plots Sensitivity v 1-Specificity

Usage

ggroc(
  x1,
  x2 = NULL,
  y = NULL,
  carrington_line = FALSE,
  costs = c(0, 0, 1, 1),
  label_number = NULL
)

Arguments

x1	Either a logistic regression fitted using glm (base package) or lrm (rms package) or alculated probabilities (eg through a logistic regression model) of the baseline model. Must be between 0 & 1
x2	Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the new (alternative) model. Must be between 0 & 1
y	Binary of outcome of interest. Must be 0 or 1 (if fitted models are provided this is extracted from the fit which for an rms fit must have x = TRUE, y = TRUE).
carrington_line	The Useful Area is from the roc down to this line. It depends on prevalence and the costs of FP, FN, TP, TN. Default is FALSE. See Carrington et al.
costs	Numeric vectors costs = c(cFP, cFN,cTP, cTN). The costs of FP, FN, TP, TN. Default, c(0,0,1,1), is for there to be no costs for the FP & FN and identical costs for TN and TP. See Carrington et al.
label_number	The number of points on the curve to label.The default has no labels.

References

Carrington AM, Fieguth PW, Mayr F, James ND, Holzinger A, Pickering JW, et al. The ROC Diagonal is not Layperson’s Chance: a New Baseline Shows the Useful Area. Machine Learning and Knowledge Extraction. Vienna, Austria: Springer; 2022. pp. 100–113. Available: 10.1007/978-3-031-14463-9_7.

JohnPickering/risk-assessment-plot-package documentation built on July 3, 2023, 8:41 a.m.