roc: ROC Curve Analysis

In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

ROC Curve Analysis

Description

Function for ROC (Receiver Operating Characteristic) Curve Analysis. Calculates sensitivity, specificity, AUC, Youden index, and more.

Usage

roc(
  data,
  classvar,
  classpos,
  testvar,
  direction = "greatpos",
  ci = TRUE,
  cimethod = "delong",
  youden = TRUE,
  optimcrit = TRUE,
  pp = FALSE,
  pprob = 0.5,
  costratioFP = 1,
  coords = TRUE,
  plotroc = TRUE,
  plotci = FALSE,
  plotbars = FALSE,
  plotprev = FALSE,
  plotidr = TRUE
)

Arguments

data	The data as a data frame.
classvar	A string naming the variable from data that contains the actual status (gold standard / diagnosis), must be a factor with two levels.
classpos	The level of the classification variable that represents the positive status.
testvar	A string naming the variable from data that contains the test result or measurement, must be numeric.
direction	Specify whether greater values of the test variable indicate a positive test (default) or if lower values indicate a positive test. Note: AUC will always be greater than 0.5, even if it means inverting the predictor internally.
ci	Boolean selection whether to show 95\ Default is 'true'.
cimethod	Method for calculating confidence intervals for the AUC. Default is 'delong'.
youden	Boolean selection whether to calculate Youden index (J = sensitivity + specificity - 1). Default is 'true'.
optimcrit	Boolean selection whether to calculate the optimal criterion value. Default is 'true'.
pp	Boolean selection whether to use a specified disease prevalence for predictive values. Default is 'false'.
pprob	Prior probability (disease prevalence in the population). Requires a value between 0.001 and 0.999, default 0.500.
costratioFP	Cost ratio of false positive to false negative decisions. Default is 1.000 (equal costs).
coords	Boolean selection whether to show the full list of coordinates of the ROC curve. This includes all possible thresholds with sensitivity, specificity, predictive values, and likelihood ratios. Default is 'true'.
plotroc	Boolean selection whether to create a ROC curve plot. Default is 'true'.
plotci	Boolean selection whether to show 95\ the ROC curve. Default is 'false'.
plotbars	Boolean selection whether to create a plot of sensitivity and specificity versus criterion values. Default is 'false'.
plotprev	Boolean selection whether to create a plot of positive and negative predictive values versus disease prevalence. Default is 'false'.
plotidr	Boolean selection whether to create an interactive dot diagram Default is 'true'.

Value

A results object containing:

results$summary					a table
results$optimal					a table
results$coords					a table
results$plotroc					an image
results$plotbars					an image
results$plotprev					an image
results$plotidr					an image

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$summary$asDF

as.data.frame(results$summary)

Examples

# example will be added

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.