ROC Curve

A ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds.

Data

We load the packages we will use:

# Libraries
library(pROC)
library(dplyr)
library(ggplot2)
library(OptimalCutpoints)

We use the data frame called aSAH from the package pROC:

# Data
data(aSAH)
aSAH1 <- aSAH[,c("outcome","s100b")]
rocobj <- roc(aSAH1$outcome, aSAH1$s100b, ci=T)


aSAH format:

A data frame containing 113 observations with 2 variables we will use:
outcome MCC type
s100b Concentration of S100B


outcome s100b
Good 0.13
Good 0.14
Good 0.10
Good 0.04
Poor 0.13
Poor 0.10
Good 0.47
Poor 0.16



ggroc function

ggroc(
data, a roc object from the roc function, or a list of roc objects
aes, the name of the aesthetics for geom_line to map to the different ROC curves supplied. Use “group” if you want the curves to appear with the same aestetic, for instance if you are faceting instead
legacy.axes, a logical indicating if the specificity axis (x axis) must be plotted as as decreasing “specificity” (FALSE, the default) or increasing “1 - specificity” (TRUE) as in most legacy software
...
)

ROC curve

# Basic ROC curve
ggroc(rocobj) + theme_minimal()

# Advanced ROC curve
ci.se.rocobj <- ci(rocobj, of="se", boot.n=2000) # confidence interval of the sensitivity
dat.ci <- data.frame(x = as.numeric(rownames(ci.se.rocobj)),
                     lower = ci.se.rocobj[, 1],
                     upper = ci.se.rocobj[, 3])

# selection of a threshold
dat.seuil=coords(rocobj, "best", best.method=c("youden"), ret=c("threshold", "sensitivity", "specificity"))

ggroc(rocobj) + 
  theme_minimal() +
  geom_abline(slope=1, intercept = 1, linetype = "dashed", alpha=0.7, color = "grey") + # add abline and custom
  coord_equal() + # ensures the units are equally scaled on the x-axis and on the y-axis
  geom_ribbon(data = dat.ci, aes(x = x, ymin = lower, ymax = upper), fill = "grey", alpha= 0.2) + # generate confidence interval of sensibility
  ggtitle(paste0("AUC=", round(rocobj$auc, 2), ", ", capture.output(rocobj$ci))) +
  geom_point(data = tibble(se=dat.seuil$sensitivity, sp=dat.seuil$specificity), mapping = aes(x=sp, y=se), colour = "red") +
  annotate("text", x=dat.seuil$specificity-0.16, y=dat.seuil$sensitivity-0.05, 
           label= paste0(round(dat.seuil$threshold, 2), " (", round(dat.seuil$specificity, 2), ",", round(dat.seuil$sensitivity, 2), ")"))




Contact

This document is a work of the statistics team in the Biostatistics and Medical Information Department at Saint-Louis Hospital in Paris (SBIM).
Developed and updated by Noémie Bigot and Anouk Walter-Petrich
noemie.bigot@aphp.fr; anouk.walter-petrich@u-paris.fr

Based on The R Graph Gallery by Yan Holtz.

SBIM