# Calculate AUC using sensitivity and specificity values only

How to calculate AUC, if I have values of sensitivity and specificity for various threshold cutoffs?

I have sensitivity and specificity values for 100 thresholds.

sensitivity: c(0.649193548387097, 0.649193548387097, 0.649193548387097, 0.649193548387097,
0.649193548387097, 0.649193548387097, 0.649193548387097, 0.646586345381526,
0.646586345381526, 0.646586345381526, 0.646586345381526, 0.646586345381526,
0.646586345381526, 0.646586345381526, 0.646586345381526, 0.646586345381526,
0.644, 0.644, 0.644, 0.644, 0.641434262948207, 0.641434262948207,
0.638888888888889, 0.638888888888889, 0.638888888888889, 0.634920634920635,
0.634920634920635, 0.634920634920635, 0.634920634920635, 0.630952380952381,
0.628458498023715, 0.624505928853755, 0.620553359683794, 0.615686274509804,
0.611764705882353, 0.607843137254902, 0.607843137254902, 0.6,
0.6, 0.59765625, 0.59375, 0.5859375, 0.58203125, 0.57421875,
0.57421875, 0.56640625, 0.562015503875969, 0.550387596899225,
0.534883720930233, 0.511627906976744, 0.5, 0.496153846153846,
0.486590038314176, 0.478927203065134, 0.46360153256705, 0.455938697318008,
0.452107279693487, 0.442748091603053, 0.425855513307985, 0.418250950570342,
0.4106463878327, 0.399239543726236, 0.390151515151515, 0.382575757575758,
0.377358490566038, 0.369811320754717, 0.362264150943396, 0.354716981132075,
0.343396226415094, 0.343396226415094, 0.339622641509434, 0.328301886792453,
0.316981132075472, 0.29811320754717, 0.294339622641509, 0.286792452830189,
0.279245283018868, 0.270676691729323, 0.255639097744361, 0.244360902255639,
0.236842105263158, 0.236842105263158, 0.229323308270677, 0.225563909774436,
0.214285714285714, 0.191729323308271, 0.184210526315789, 0.176691729323308,
0.165413533834586, 0.139097744360902, 0.139097744360902, 0.12781954887218,
0.120300751879699, 0.105263157894737, 0.075187969924812, 0.0639097744360902,
0.0601503759398496, 0.0526315789473684, 0.0413533834586466, 0.018796992481203,
0)


specificity : c(0.917961165048544, 0.920581113801453, 0.923708353452438, 0.925337186897881,
0.928743379874819, 0.930288461538462, 0.93371757925072, 0.934772182254197,
0.936272160996646, 0.937739463601533, 0.938872970391595, 0.940867906533143,
0.942435775451951, 0.944893111638955, 0.946969696969697, 0.949881796690307,
0.952290977798772, 0.953235710911667, 0.955209806694955, 0.956235294117647,
0.95815702867889, 0.95868544600939, 0.961556493202063, 0.962043111527648,
0.963951310861423, 0.965420560747664, 0.966449207828518, 0.966930600838379,
0.9674569967457, 0.967951695308871, 0.967951695308871, 0.968474733426055,
0.969401947148818, 0.969401947148818, 0.969907407407407, 0.971322849213691,
0.972735674676525, 0.973684210526316, 0.97372060857538, 0.973756906077348,
0.975598526703499, 0.977000919963201, 0.977512620468105, 0.9780119102153,
0.979405034324943, 0.981235697940503, 0.98124428179323, 0.982167352537723,
0.982632541133455, 0.982648401826484, 0.983135824977211, 0.984069185252617,
0.984993178717599, 0.985467756584923, 0.985934664246824, 0.986406887177164,
0.98733604703754, 0.98869801084991, 0.98961625282167, 0.989625620207488,
0.990081154192967, 0.990085624155025, 0.990540540540541, 0.990540540540541,
0.990995047276002, 0.991449144914491, 0.991899189918992, 0.993252361673414,
0.99370220422852, 0.993707865168539, 0.993713515940727, 0.994616419919246,
0.995513683266039, 0.996410946612831, 0.996859578286227, 0.996860986547085,
0.997311827956989, 0.997315436241611, 0.997316636851521, 0.997763864042934,
0.997763864042934, 0.998211890925346, 0.998212689901698, 0.998212689901698,
0.998212689901698, 0.998214285714286, 0.998661311914324, 0.998661311914324,
0.998661311914324, 0.999107939339875, 0.999107939339875, 0.999108337048596,
0.999108337048596, 0.999108734402852, 0.999109528049866, 0.999554962171785,
1, 1, 1, 1, 1)

threshold:
c(0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1,
0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21,
0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32,
0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43,
0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54,
0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65,
0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76,
0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87,
0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,
0.99, 1)

AUC =round(sum(specificity [1:length(threshold)]*diff(c(0, 1 - sensitivity [1:length(threshold)]))),2)
AUC= 0.95



1)Is this the correct way to find AUC?

2)If I want to plot ROC curve is this code fine?

plot((1-specificity),sensitivity ,xlab = "Sensitivity",ylab = "Specificity",type = "l")


3) Is there some formula to calculate the power of this ROC analysis. So that I know I need minimum samples to calculate AUC?

• Missing values for thresholds? Sep 6, 2019 at 8:14
• The AUROC curve is usually plotted as Sensitivity ~ 1-Specificity, which goes from 0 to 1, that is (0,0) to (1,1). Sep 6, 2019 at 8:40
• After analysis is done, there is no need for power calculation; see this page for example. See this page for discussion and links to further information about confidence intervals, inference, etc with respect to ROC curves. Also, AUROC is not the best measure of the quality of the probability model underlying a classification scheme; see this page and others on proper scoring rules.
– EdM
Sep 6, 2019 at 14:00
• @EdM Thanks for your information. Also if you can share, what would be the best way to calculate AUC using the sensitivity and specificity values? Sep 8, 2019 at 19:32
• The ROC curve should be plotted over ranges of [0,1] for both Sensitivity (y-axis) and (1-Specificity; x-axis). The x-axis of your plot and your attempt to calculate the area under the curve only extend to a value of 0.08. See this page for links to tools designed specifically for calculating AUROC. The C-index, sometimes reported by software for logistic regression and classification, is equivalent to the AUROC.
– EdM
Sep 8, 2019 at 21:01

Everything looks good

1. AUC

This look as like an approximation. There are several ways in which you can calculate the AUC. AUC is mainly for calculating the area under the curve that you have plotted as part of ROC

1. ROC Curve looks good

However the axis seems to be reversed

1. Power of the Curve

The visual power of the ROC becomes more refined with more data points

• Thanks for your information. I have also come across posts which says that AUC can also be calculated using the trapz function. When I try to do trapz(sensitivity, specificity), I gt -0.6. Can you please suggest where did I go wrong Sep 8, 2019 at 19:19
• @DhwaniDholakia the calculation of area under the curve is for sensitivity along the y-axis and (1-specificity), not specificity itself, on the x-axis. Also, as noted in one of my comments on your original question, the calculation needs to be done over the entire extent of [0,1] along the x-axis.
– EdM
Sep 8, 2019 at 21:07

As best as I can tell from the model-building process described on the SO page you linked and in your comments here, the curves that you are trying to generate are not proper ROC curves.

Your approach to this 13-class image recognition problem produced a list of the top three CNN predictions for each image, along with associated probabilities. For each image, you have placed the probability value for the highest-probability class into a vector, which you called model_info$X.st.. on that SO page. According to a comment from you on an answer there, to get the curves described on this page you are applying your "threshold" values to model_info$X.st...

That is not the type of "threshold" that is appropriate for ROC curves.

An ROC curve is produced by changing a "threshold" for some decision rule about a single class membership, and examining how true positives (Sensitivity) and false positives (1-Specificity) change as that threshold is varied. Multi-class ROC curves are essentially based on sets of single-class curves: plots of each single class as positives taking all other classes as negatives, weightings of such single-class plots by class prevalence, or pairwise comparisons among the classes.

The threshold you are using goes over a set of probabilities for whatever class happened to have the highest probability for each image. That has nothing to do with the single-class true positives and false positives that go into an ROC curve. I can see how the type of analysis you are performing might be of some interest, but it is not producing an ROC curve and the area under that curve will not be any established measure of classification performance with which I am familiar.

For a simple and proper scoring rule for a multi-class situation like this, consider the original Brier score. That's a type of mean-square error between the actual class (1 for true class, 0 for all the others) and the predicted class probability, over all classes and images.

Unlike single-class ROC curves, multi-class ROC curves can be sensitive to the distributions of classes in your data set and misclassification costs. So multi-class ROC curves might not be so useful as you might think. If you nevertheless do want to do ROC/AUROC analysis in this situation, see the multi-class ROC curve page and the links from it.