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Md Riaz Ahmed Khan mdriazahmed.khan@jacks.sdstate.edu
Thomas Brandenburger, PhD
thomas.brandenburger@sdstate.edu
Md Riaz Ahmed Khan
mdriazahmed.khan@jacks.sdstate.edu
The goal of ROCit is to easily compute, summarize, and visualize the performance of a binary classifier. ROCit package provides flexibility to easily evaluate threshold-bound metrics, such as, accuracy, misclassification rate, sensitivity, specificity, F-Score. . Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot.
This is a basic example which shows the workflow of assessing a binary classifier.
library(ROCit)
data("Loan")
summary(Loan$Status)
#> CO FP
#> 131 769class <- ifelse(Loan$Status == "FP", 0, 1)score <- Loan$Scorem<- measureit(score = score, class = class,
measure = c("ACC", "SENS", "SPEC", "FSCR"))
mymetrics <- as.data.frame(cbind(Cutoff = m$Cutoff, Depth = m$Depth,
Accuracy = m$ACC, Sensitivity = m$SENS,
Specificity = m$SPEC, `F-Score` = m$FSCR))
head(mymetrics)
#> Cutoff Depth Accuracy Sensitivity Specificity F-Score
#> 1 Inf 0.000000000 0.8544444 0.000000000 1.0000000 NaN
#> 2 269.1711 0.001111111 0.8555556 0.007633588 1.0000000 0.01515152
#> 3 267.3342 0.002222222 0.8544444 0.007633588 0.9986996 0.01503759
#> 4 266.5938 0.003333333 0.8533333 0.007633588 0.9973992 0.01492537
#> 5 265.2553 0.004444444 0.8522222 0.007633588 0.9960988 0.01481481
#> 6 263.8303 0.005555556 0.8533333 0.015267176 0.9960988 0.02941176
tail(mymetrics)
#> Cutoff Depth Accuracy Sensitivity Specificity F-Score
#> 897 103.236891 0.9955556 0.1500000 1 0.00520156 0.2551120
#> 898 93.941202 0.9966667 0.1488889 1 0.00390117 0.2548638
#> 899 70.836943 0.9977778 0.1477778 1 0.00260078 0.2546161
#> 900 -2.673036 0.9988889 0.1466667 1 0.00130039 0.2543689
#> 901 -5.449033 1.0000000 0.1455556 1 0.00000000 0.2541222
#> 902 -Inf 1.0000000 0.1455556 1 0.00000000 0.2541222rocit_object_empirical <- rocit(score = score, class = class)
summary(rocit_object_empirical)
#>
#> Empirical ROC curve
#> Number of postive responses : 131
#> Number of negative responses : 769
#> Area under curve : 0.666315925311945# plot(rocit_object_empirical, YIndex = F, values = F)ci.auc <- ciAUC(rocit_object_empirical, level = 0.95)
print(ci.auc)
#>
#> estimated AUC : 0.666315925311945
#> AUC estimation method : empirical
#>
#> CI of AUC
#> confidence level = 95%
#> lower = 0.612575921206968 upper = 0.720055929416921ci.roc <- ciROC(rocit_object_empirical, level = 0.9)
# plot(ci.roc)gainstable <- gainstable(rocit_object_empirical, ngroup = 8)
print(gainstable, maxdigit = 2)
#> Bucket Obs CObs Depth Resp CResp RespRate CRespRate CCapRate Lift CLift
#> 1 1 112 112 0.12 30 30 0.27 0.27 0.23 1.84 1.84
#> 2 2 113 225 0.25 25 55 0.22 0.24 0.42 1.52 1.68
#> 3 3 113 338 0.38 21 76 0.19 0.22 0.58 1.28 1.54
#> 4 4 112 450 0.50 18 94 0.16 0.21 0.72 1.10 1.44
#> 5 5 112 562 0.62 10 104 0.09 0.19 0.79 0.61 1.27
#> 6 6 113 675 0.75 16 120 0.14 0.18 0.92 0.97 1.22
#> 7 7 113 788 0.88 5 125 0.04 0.16 0.95 0.30 1.09
#> 8 8 112 900 1.00 6 131 0.05 0.15 1.00 0.37 1.00
# plot(gainstable)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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