Find the accuracy at each possible cutoff. Actuals should be binary,
where 1 = present and 0 = absent.
Arguments
- predicted
vector of continuous predicted values.
- actual
vector of binary actual values (
1= present and0= absent).- UH
(optional) utility of hits (true positives), specified as a value from 0-1, where 1 is the most highly valued and 0 is the least valued.
- UM
(optional) utility of misses (false negatives), specified as a value from 0-1, where 1 is the most highly valued and 0 is the least valued.
- UCR
(optional) utility of correct rejections (true negatives), specified as a value from 0-1, where 1 is the most highly valued and 0 is the least valued.
- UFA
(optional) utility of false positives (false positives), specified as a value from 0-1, where 1 is the most highly valued and 0 is the least valued.
Value
cutoff= the cutoff specifiedTP= true positivesTN= true negativesFP= false positivesFN= false negativesSR= selection ratioBR= base ratepercentAccuracy= percent accuracypercentAccuracyByChance= percent accuracy by chancepercentAccuracyPredictingFromBaseRate= percent accuracy from predicting from the base rateRIOC= relative improvement over chancerelativeImprovementOverPredictingFromBaseRate= relative improvement over predicting from the base rateSN= sensitivtySP= specificityTPrate= true positive rateTNrate= true negative rateFNrate= false negative rateFPrate= false positive rateHR= hit rateFAR= false alarm ratePPV= positive predictive valueNPV= negative predictive valueFDR= false discovery rateFOR= false omission rateyoudenJ= Youden's J statisticbalancedAccuracy= balanced accuracyf1Score= F1-scoremcc= Matthews correlation coefficientdiagnosticOddsRatio= diagnostic odds ratiopositiveLikelihoodRatio= positive likelihood rationegativeLikelhoodRatio= negative likelihood ratiodPrimeSDT= d-Prime index from signal detection theorybetaSDT= beta index from signal detection theorycSDT= c index from signal detection theoryaSDT= a index from signal detection theorybSDT= b index from signal detection theorydifferenceBetweenPredictedAndObserved= difference between predicted and observed valuesinformationGain= information gainoverallUtility= overall utility (if utilities were specified)
Details
Compute accuracy indices of predicted values in relation to actual values at each possible cutoff by specifying the predicted values and actual values. The target condition is considered present at or above each cutoff value. Optionally, you can specify the utility of hits, misses, correct rejections, and false alarms to calculate the overall utility of each possible cutoff.
See also
Other accuracy:
accuracyAtCutoff(),
accuracyOverall(),
nomogrammer(),
optimalCutoff(),
posttestOdds()
Examples
# Prepare Data
data("USArrests")
USArrests$highMurderState <- NA
USArrests$highMurderState[which(USArrests$Murder >= 10)] <- 1
USArrests$highMurderState[which(USArrests$Murder < 10)] <- 0
# Calculate Accuracy
accuracyAtEachCutoff(predicted = USArrests$Assault,
actual = USArrests$highMurderState)
#> cutoff TP TN FP FN SR BR percentAccuracy percentAccuracyByChance
#> 1 45.00 16 0 34 0 1.00 0.32 32 32.00
#> 2 46.00 16 1 33 0 0.98 0.32 34 32.72
#> 3 48.00 16 2 32 0 0.96 0.32 36 33.44
#> 4 53.00 16 3 31 0 0.94 0.32 38 34.16
#> 5 56.00 16 4 30 0 0.92 0.32 40 34.88
#> 6 57.00 16 5 29 0 0.90 0.32 42 35.60
#> 7 72.00 16 6 28 0 0.88 0.32 44 36.32
#> 8 81.00 16 7 27 0 0.86 0.32 46 37.04
#> 9 83.00 16 8 26 0 0.84 0.32 48 37.76
#> 10 86.00 16 9 25 0 0.82 0.32 50 38.48
#> 11 102.00 16 10 24 0 0.80 0.32 52 39.20
#> 12 106.00 16 11 23 0 0.78 0.32 54 39.92
#> 13 109.00 16 12 22 0 0.76 0.32 56 40.64
#> 14 110.00 16 14 20 0 0.72 0.32 60 42.08
#> 15 113.00 16 15 19 0 0.70 0.32 62 42.80
#> 16 115.00 16 16 18 0 0.68 0.32 64 43.52
#> 17 120.00 16 17 17 0 0.66 0.32 66 44.24
#> 18 145.00 16 20 14 0 0.60 0.32 72 46.40
#> 19 149.00 16 21 13 0 0.58 0.32 74 47.12
#> 20 151.00 16 22 12 0 0.56 0.32 76 47.84
#> 21 156.00 16 23 11 0 0.54 0.32 78 48.56
#> 22 159.00 16 24 10 0 0.52 0.32 80 49.28
#> 23 161.00 16 26 8 0 0.48 0.32 84 50.72
#> 24 174.00 16 27 7 0 0.46 0.32 86 51.44
#> 25 178.00 16 28 6 0 0.44 0.32 88 52.16
#> 26 188.00 16 29 5 0 0.42 0.32 90 52.88
#> 27 190.00 15 29 5 1 0.40 0.32 88 53.60
#> 28 201.00 15 30 4 1 0.38 0.32 90 54.32
#> 29 204.00 14 30 4 2 0.36 0.32 88 55.04
#> 30 211.00 14 31 3 2 0.34 0.32 90 55.76
#> 31 236.00 13 31 3 3 0.32 0.32 88 56.48
#> 32 238.00 12 31 3 4 0.30 0.32 86 57.20
#> 33 249.00 12 32 2 4 0.28 0.32 88 57.92
#> 34 252.00 10 32 2 6 0.24 0.32 84 59.36
#> 35 254.00 9 32 2 7 0.22 0.32 82 60.08
#> 36 255.00 8 32 2 8 0.20 0.32 80 60.80
#> 37 259.00 7 32 2 9 0.18 0.32 78 61.52
#> 38 263.00 6 32 2 10 0.16 0.32 76 62.24
#> 39 276.00 5 32 2 11 0.14 0.32 74 62.96
#> 40 279.00 5 33 1 11 0.12 0.32 76 63.68
#> 41 285.00 4 33 1 12 0.10 0.32 74 64.40
#> 42 294.00 3 33 1 13 0.08 0.32 72 65.12
#> 43 300.00 3 34 0 13 0.06 0.32 74 65.84
#> 44 335.00 2 34 0 14 0.04 0.32 72 66.56
#> 45 337.00 1 34 0 15 0.02 0.32 70 67.28
#> 46 337.01 0 34 0 16 0.00 0.32 68 68.00
#> percentAccuracyPredictingFromBaseRate RIOC
#> 1 68 NA
#> 2 68 1.00000000
#> 3 68 1.00000000
#> 4 68 1.00000000
#> 5 68 1.00000000
#> 6 68 1.00000000
#> 7 68 1.00000000
#> 8 68 1.00000000
#> 9 68 1.00000000
#> 10 68 1.00000000
#> 11 68 1.00000000
#> 12 68 1.00000000
#> 13 68 1.00000000
#> 14 68 1.00000000
#> 15 68 1.00000000
#> 16 68 1.00000000
#> 17 68 1.00000000
#> 18 68 1.00000000
#> 19 68 1.00000000
#> 20 68 1.00000000
#> 21 68 1.00000000
#> 22 68 1.00000000
#> 23 68 1.00000000
#> 24 68 1.00000000
#> 25 68 1.00000000
#> 26 68 1.00000000
#> 27 68 0.89583333
#> 28 68 0.89919355
#> 29 68 0.80468750
#> 30 68 0.81060606
#> 31 68 0.72426471
#> 32 68 0.64285714
#> 33 68 0.65277778
#> 34 68 0.50657895
#> 35 68 0.43910256
#> 36 68 0.37500000
#> 37 68 0.31402439
#> 38 68 0.25595238
#> 39 68 0.20058140
#> 40 68 0.21875000
#> 41 68 0.16666667
#> 42 68 0.11684783
#> 43 68 0.13563830
#> 44 68 0.08854167
#> 45 68 0.04336735
#> 46 68 0.00000000
#> relativeImprovementOverPredictingFromBaseRate SN SP TPrate
#> 1 -0.56250 1.0000 0.00000000 1.0000
#> 2 -0.53125 1.0000 0.02941176 1.0000
#> 3 -0.50000 1.0000 0.05882353 1.0000
#> 4 -0.46875 1.0000 0.08823529 1.0000
#> 5 -0.43750 1.0000 0.11764706 1.0000
#> 6 -0.40625 1.0000 0.14705882 1.0000
#> 7 -0.37500 1.0000 0.17647059 1.0000
#> 8 -0.34375 1.0000 0.20588235 1.0000
#> 9 -0.31250 1.0000 0.23529412 1.0000
#> 10 -0.28125 1.0000 0.26470588 1.0000
#> 11 -0.25000 1.0000 0.29411765 1.0000
#> 12 -0.21875 1.0000 0.32352941 1.0000
#> 13 -0.18750 1.0000 0.35294118 1.0000
#> 14 -0.12500 1.0000 0.41176471 1.0000
#> 15 -0.09375 1.0000 0.44117647 1.0000
#> 16 -0.06250 1.0000 0.47058824 1.0000
#> 17 -0.03125 1.0000 0.50000000 1.0000
#> 18 0.06250 1.0000 0.58823529 1.0000
#> 19 0.09375 1.0000 0.61764706 1.0000
#> 20 0.12500 1.0000 0.64705882 1.0000
#> 21 0.15625 1.0000 0.67647059 1.0000
#> 22 0.18750 1.0000 0.70588235 1.0000
#> 23 0.25000 1.0000 0.76470588 1.0000
#> 24 0.28125 1.0000 0.79411765 1.0000
#> 25 0.31250 1.0000 0.82352941 1.0000
#> 26 0.34375 1.0000 0.85294118 1.0000
#> 27 0.31250 0.9375 0.85294118 0.9375
#> 28 0.34375 0.9375 0.88235294 0.9375
#> 29 0.31250 0.8750 0.88235294 0.8750
#> 30 0.34375 0.8750 0.91176471 0.8750
#> 31 0.31250 0.8125 0.91176471 0.8125
#> 32 0.28125 0.7500 0.91176471 0.7500
#> 33 0.31250 0.7500 0.94117647 0.7500
#> 34 0.25000 0.6250 0.94117647 0.6250
#> 35 0.21875 0.5625 0.94117647 0.5625
#> 36 0.18750 0.5000 0.94117647 0.5000
#> 37 0.15625 0.4375 0.94117647 0.4375
#> 38 0.12500 0.3750 0.94117647 0.3750
#> 39 0.09375 0.3125 0.94117647 0.3125
#> 40 0.12500 0.3125 0.97058824 0.3125
#> 41 0.09375 0.2500 0.97058824 0.2500
#> 42 0.06250 0.1875 0.97058824 0.1875
#> 43 0.09375 0.1875 1.00000000 0.1875
#> 44 0.06250 0.1250 1.00000000 0.1250
#> 45 0.03125 0.0625 1.00000000 0.0625
#> 46 0.00000 0.0000 1.00000000 0.0000
#> TNrate FNrate FPrate HR FAR PPV NPV FDR
#> 1 0.00000000 0.0000 1.00000000 1.0000 1.00000000 0.3200000 NA 0.6800000
#> 2 0.02941176 0.0000 0.97058824 1.0000 0.97058824 0.3265306 1.0000000 0.6734694
#> 3 0.05882353 0.0000 0.94117647 1.0000 0.94117647 0.3333333 1.0000000 0.6666667
#> 4 0.08823529 0.0000 0.91176471 1.0000 0.91176471 0.3404255 1.0000000 0.6595745
#> 5 0.11764706 0.0000 0.88235294 1.0000 0.88235294 0.3478261 1.0000000 0.6521739
#> 6 0.14705882 0.0000 0.85294118 1.0000 0.85294118 0.3555556 1.0000000 0.6444444
#> 7 0.17647059 0.0000 0.82352941 1.0000 0.82352941 0.3636364 1.0000000 0.6363636
#> 8 0.20588235 0.0000 0.79411765 1.0000 0.79411765 0.3720930 1.0000000 0.6279070
#> 9 0.23529412 0.0000 0.76470588 1.0000 0.76470588 0.3809524 1.0000000 0.6190476
#> 10 0.26470588 0.0000 0.73529412 1.0000 0.73529412 0.3902439 1.0000000 0.6097561
#> 11 0.29411765 0.0000 0.70588235 1.0000 0.70588235 0.4000000 1.0000000 0.6000000
#> 12 0.32352941 0.0000 0.67647059 1.0000 0.67647059 0.4102564 1.0000000 0.5897436
#> 13 0.35294118 0.0000 0.64705882 1.0000 0.64705882 0.4210526 1.0000000 0.5789474
#> 14 0.41176471 0.0000 0.58823529 1.0000 0.58823529 0.4444444 1.0000000 0.5555556
#> 15 0.44117647 0.0000 0.55882353 1.0000 0.55882353 0.4571429 1.0000000 0.5428571
#> 16 0.47058824 0.0000 0.52941176 1.0000 0.52941176 0.4705882 1.0000000 0.5294118
#> 17 0.50000000 0.0000 0.50000000 1.0000 0.50000000 0.4848485 1.0000000 0.5151515
#> 18 0.58823529 0.0000 0.41176471 1.0000 0.41176471 0.5333333 1.0000000 0.4666667
#> 19 0.61764706 0.0000 0.38235294 1.0000 0.38235294 0.5517241 1.0000000 0.4482759
#> 20 0.64705882 0.0000 0.35294118 1.0000 0.35294118 0.5714286 1.0000000 0.4285714
#> 21 0.67647059 0.0000 0.32352941 1.0000 0.32352941 0.5925926 1.0000000 0.4074074
#> 22 0.70588235 0.0000 0.29411765 1.0000 0.29411765 0.6153846 1.0000000 0.3846154
#> 23 0.76470588 0.0000 0.23529412 1.0000 0.23529412 0.6666667 1.0000000 0.3333333
#> 24 0.79411765 0.0000 0.20588235 1.0000 0.20588235 0.6956522 1.0000000 0.3043478
#> 25 0.82352941 0.0000 0.17647059 1.0000 0.17647059 0.7272727 1.0000000 0.2727273
#> 26 0.85294118 0.0000 0.14705882 1.0000 0.14705882 0.7619048 1.0000000 0.2380952
#> 27 0.85294118 0.0625 0.14705882 0.9375 0.14705882 0.7500000 0.9666667 0.2500000
#> 28 0.88235294 0.0625 0.11764706 0.9375 0.11764706 0.7894737 0.9677419 0.2105263
#> 29 0.88235294 0.1250 0.11764706 0.8750 0.11764706 0.7777778 0.9375000 0.2222222
#> 30 0.91176471 0.1250 0.08823529 0.8750 0.08823529 0.8235294 0.9393939 0.1764706
#> 31 0.91176471 0.1875 0.08823529 0.8125 0.08823529 0.8125000 0.9117647 0.1875000
#> 32 0.91176471 0.2500 0.08823529 0.7500 0.08823529 0.8000000 0.8857143 0.2000000
#> 33 0.94117647 0.2500 0.05882353 0.7500 0.05882353 0.8571429 0.8888889 0.1428571
#> 34 0.94117647 0.3750 0.05882353 0.6250 0.05882353 0.8333333 0.8421053 0.1666667
#> 35 0.94117647 0.4375 0.05882353 0.5625 0.05882353 0.8181818 0.8205128 0.1818182
#> 36 0.94117647 0.5000 0.05882353 0.5000 0.05882353 0.8000000 0.8000000 0.2000000
#> 37 0.94117647 0.5625 0.05882353 0.4375 0.05882353 0.7777778 0.7804878 0.2222222
#> 38 0.94117647 0.6250 0.05882353 0.3750 0.05882353 0.7500000 0.7619048 0.2500000
#> 39 0.94117647 0.6875 0.05882353 0.3125 0.05882353 0.7142857 0.7441860 0.2857143
#> 40 0.97058824 0.6875 0.02941176 0.3125 0.02941176 0.8333333 0.7500000 0.1666667
#> 41 0.97058824 0.7500 0.02941176 0.2500 0.02941176 0.8000000 0.7333333 0.2000000
#> 42 0.97058824 0.8125 0.02941176 0.1875 0.02941176 0.7500000 0.7173913 0.2500000
#> 43 1.00000000 0.8125 0.00000000 0.1875 0.00000000 1.0000000 0.7234043 0.0000000
#> 44 1.00000000 0.8750 0.00000000 0.1250 0.00000000 1.0000000 0.7083333 0.0000000
#> 45 1.00000000 0.9375 0.00000000 0.0625 0.00000000 1.0000000 0.6938776 0.0000000
#> 46 1.00000000 1.0000 0.00000000 0.0000 0.00000000 NA 0.6800000 NA
#> FOR youdenJ balancedAccuracy f1Score mcc
#> 1 NA 0.00000000 0.5000000 0.4848485 NA
#> 2 0.00000000 0.02941176 0.5147059 0.4923077 0.09799919
#> 3 0.00000000 0.05882353 0.5294118 0.5000000 0.14002801
#> 4 0.00000000 0.08823529 0.5441176 0.5079365 0.17331344
#> 5 0.00000000 0.11764706 0.5588235 0.5161290 0.20228869
#> 6 0.00000000 0.14705882 0.5735294 0.5245902 0.22866478
#> 7 0.00000000 0.17647059 0.5882353 0.5333333 0.25332020
#> 8 0.00000000 0.20588235 0.6029412 0.5423729 0.27678040
#> 9 0.00000000 0.23529412 0.6176471 0.5517241 0.29939248
#> 10 0.00000000 0.26470588 0.6323529 0.5614035 0.32140295
#> 11 0.00000000 0.29411765 0.6470588 0.5714286 0.34299717
#> 12 0.00000000 0.32352941 0.6617647 0.5818182 0.36432131
#> 13 0.00000000 0.35294118 0.6764706 0.5925926 0.38549554
#> 14 0.00000000 0.41176471 0.7058824 0.6153846 0.42779263
#> 15 0.00000000 0.44117647 0.7205882 0.6274510 0.44908871
#> 16 0.00000000 0.47058824 0.7352941 0.6400000 0.47058824
#> 17 0.00000000 0.50000000 0.7500000 0.6530612 0.49236596
#> 18 0.00000000 0.58823529 0.7941176 0.6956522 0.56011203
#> 19 0.00000000 0.61764706 0.8088235 0.7111111 0.58375576
#> 20 0.00000000 0.64705882 0.8235294 0.7272727 0.60806899
#> 21 0.00000000 0.67647059 0.8382353 0.7441860 0.63314411
#> 22 0.00000000 0.70588235 0.8529412 0.7619048 0.65908204
#> 23 0.00000000 0.76470588 0.8823529 0.8000000 0.71400555
#> 24 0.00000000 0.79411765 0.8970588 0.8205128 0.74325613
#> 25 0.00000000 0.82352941 0.9117647 0.8421053 0.77390599
#> 26 0.00000000 0.85294118 0.9264706 0.8648649 0.80613891
#> 27 0.03333333 0.79044118 0.8952206 0.8333333 0.75265055
#> 28 0.03225806 0.81985294 0.9099265 0.8571429 0.78791208
#> 29 0.06250000 0.75735294 0.8786765 0.8235294 0.73601476
#> 30 0.06060606 0.78676471 0.8933824 0.8484848 0.77475233
#> 31 0.08823529 0.72426471 0.8621324 0.8125000 0.72426471
#> 32 0.11428571 0.66176471 0.8308824 0.7741935 0.67363307
#> 33 0.11111111 0.69117647 0.8455882 0.8000000 0.71808049
#> 34 0.15789474 0.56617647 0.7830882 0.7142857 0.61839909
#> 35 0.17948718 0.50367647 0.7518382 0.6666667 0.56718204
#> 36 0.20000000 0.44117647 0.7205882 0.6153846 0.51449576
#> 37 0.21951220 0.37867647 0.6893382 0.5600000 0.45978478
#> 38 0.23809524 0.31617647 0.6580882 0.5000000 0.40230864
#> 39 0.25581395 0.25367647 0.6268382 0.4347826 0.34103299
#> 40 0.25000000 0.28308824 0.6415441 0.4545455 0.40636782
#> 41 0.26666667 0.22058824 0.6102941 0.3809524 0.34299717
#> 42 0.28260870 0.15808824 0.5790441 0.3000000 0.27182543
#> 43 0.27659574 0.18750000 0.5937500 0.3157895 0.36829105
#> 44 0.29166667 0.12500000 0.5625000 0.2222222 0.29755952
#> 45 0.30612245 0.06250000 0.5312500 0.1176471 0.20824828
#> 46 0.32000000 0.00000000 0.5000000 NA NA
#> diagnosticOddsRatio positiveLikelihoodRatio negativeLikelihoodRatio
#> 1 NA 1.000000 NA
#> 2 NA 1.030303 0.00000000
#> 3 NA 1.062500 0.00000000
#> 4 NA 1.096774 0.00000000
#> 5 NA 1.133333 0.00000000
#> 6 NA 1.172414 0.00000000
#> 7 NA 1.214286 0.00000000
#> 8 NA 1.259259 0.00000000
#> 9 NA 1.307692 0.00000000
#> 10 NA 1.360000 0.00000000
#> 11 NA 1.416667 0.00000000
#> 12 NA 1.478261 0.00000000
#> 13 NA 1.545455 0.00000000
#> 14 NA 1.700000 0.00000000
#> 15 NA 1.789474 0.00000000
#> 16 NA 1.888889 0.00000000
#> 17 NA 2.000000 0.00000000
#> 18 NA 2.428571 0.00000000
#> 19 NA 2.615385 0.00000000
#> 20 NA 2.833333 0.00000000
#> 21 NA 3.090909 0.00000000
#> 22 NA 3.400000 0.00000000
#> 23 NA 4.250000 0.00000000
#> 24 NA 4.857143 0.00000000
#> 25 NA 5.666667 0.00000000
#> 26 NA 6.800000 0.00000000
#> 27 87.000000 6.375000 0.07327586
#> 28 112.500000 7.968750 0.07083333
#> 29 52.500000 7.437500 0.14166667
#> 30 72.333333 9.916667 0.13709677
#> 31 44.777778 9.208333 0.20564516
#> 32 31.000000 8.500000 0.27419355
#> 33 48.000000 12.750000 0.26562500
#> 34 26.666667 10.625000 0.39843750
#> 35 20.571429 9.562500 0.46484375
#> 36 16.000000 8.500000 0.53125000
#> 37 12.444444 7.437500 0.59765625
#> 38 9.600000 6.375000 0.66406250
#> 39 7.272727 5.312500 0.73046875
#> 40 15.000000 10.625000 0.70833333
#> 41 11.000000 8.500000 0.77272727
#> 42 7.615385 6.375000 0.83712121
#> 43 NA NA 0.81250000
#> 44 NA NA 0.87500000
#> 45 NA NA 0.93750000
#> 46 NA NA 1.00000000
#> dPrimeSDT betaSDT cSDT aSDT bSDT
#> 1 NA NA NA NA NA
#> 2 NA 0.0000000 NA 0.7573529 0.02857143
#> 3 NA 0.0000000 NA 0.7647059 0.05555556
#> 4 NA 0.0000000 NA 0.7720588 0.08108108
#> 5 NA 0.0000000 NA 0.7794118 0.10526316
#> 6 NA 0.0000000 NA 0.7867647 0.12820513
#> 7 NA 0.0000000 NA 0.7941176 0.15000000
#> 8 NA 0.0000000 NA 0.8014706 0.17073171
#> 9 NA 0.0000000 NA 0.8088235 0.19047619
#> 10 NA 0.0000000 NA 0.8161765 0.20930233
#> 11 NA 0.0000000 NA 0.8235294 0.22727273
#> 12 NA 0.0000000 NA 0.8308824 0.24444444
#> 13 NA 0.0000000 NA 0.8382353 0.26086957
#> 14 NA 0.0000000 NA 0.8529412 0.29166667
#> 15 NA 0.0000000 NA 0.8602941 0.30612245
#> 16 NA 0.0000000 NA 0.8676471 0.32000000
#> 17 NA 0.0000000 NA 0.8750000 0.33333333
#> 18 NA 0.0000000 NA 0.8970588 0.37777778
#> 19 NA 0.0000000 NA 0.9044118 0.39534884
#> 20 NA 0.0000000 NA 0.9117647 0.41463415
#> 21 NA 0.0000000 NA 0.9191176 0.43589744
#> 22 NA 0.0000000 NA 0.9264706 0.45945946
#> 23 NA 0.0000000 NA 0.9411765 0.51515152
#> 24 NA 0.0000000 NA 0.9485294 0.54838710
#> 25 NA 0.0000000 NA 0.9558824 0.58620690
#> 26 NA 0.0000000 NA 0.9632353 0.62962963
#> 27 2.583252 0.5344995 -0.24249457 0.9384191 0.78703704
#> 28 2.720952 0.6234551 -0.17364456 0.9476103 0.85000000
#> 29 2.337181 1.0435544 0.01824103 0.9246324 1.02000000
#> 30 2.502052 1.2864643 0.10067643 0.9356618 1.10869565
#> 31 2.238849 1.6820865 0.23227784 0.9145221 1.29347826
#> 32 2.026192 1.9859180 0.33860624 0.8933824 1.47826087
#> 33 2.239216 2.7093703 0.44511836 0.9080882 1.61904762
#> 34 1.883366 3.2330273 0.62304355 0.8694853 2.02380952
#> 35 1.722037 3.3595638 0.70370789 0.8501838 2.22619048
#> 36 1.564726 3.4013910 0.78236324 0.8308824 2.42857143
#> 37 1.407416 3.3595638 0.86101858 0.8110557 2.51331497
#> 38 1.246087 3.2330273 0.94168292 0.7898284 2.58525346
#> 39 1.075950 3.0184193 1.02675144 0.7663603 2.62114537
#> 40 1.400734 5.2892304 1.18914319 0.7972426 3.22784810
#> 41 1.215020 4.7476784 1.28199986 0.7757353 3.40000000
#> 42 1.002363 4.0213169 1.38832826 0.7503064 3.44839858
#> 43 NA NA NA 0.7968750 6.33333333
#> 44 NA NA NA 0.7812500 9.00000000
#> 45 NA NA NA 0.7656250 17.00000000
#> 46 NA NA NA NA NA
#> differenceBetweenPredictedAndObserved informationGain
#> 1 49.6000 NA
#> 2 49.6000 NA
#> 3 49.6000 NA
#> 4 49.6000 NA
#> 5 49.6000 NA
#> 6 75.8000 NA
#> 7 75.8000 NA
#> 8 75.8000 NA
#> 9 75.8000 NA
#> 10 75.8000 NA
#> 11 107.2000 NA
#> 12 107.2000 NA
#> 13 107.2000 NA
#> 14 107.2000 NA
#> 15 117.6000 NA
#> 16 117.6000 NA
#> 17 117.6000 NA
#> 18 153.1667 NA
#> 19 153.1667 NA
#> 20 153.1667 NA
#> 21 153.1667 NA
#> 22 175.0000 NA
#> 23 175.0000 NA
#> 24 175.0000 NA
#> 25 175.0000 NA
#> 26 175.0000 NA
#> 27 207.8000 0.45336483
#> 28 207.8000 0.49476883
#> 29 207.8000 0.41340219
#> 30 207.8000 0.45810281
#> 31 207.8000 0.38881826
#> 32 247.6000 0.32890663
#> 33 247.6000 0.37636708
#> 34 247.6000 0.27014667
#> 35 265.6000 0.22430896
#> 36 265.6000 0.18245336
#> 37 265.6000 0.14421850
#> 38 265.6000 0.10941594
#> 39 265.6000 0.07803185
#> 40 265.6000 0.11245402
#> 41 309.4000 0.07921198
#> 42 309.4000 0.04921665
#> 43 309.4000 NA
#> 44 309.4000 NA
#> 45 309.4000 NA
#> 46 NA NA
accuracyAtEachCutoff(predicted = USArrests$Assault,
actual = USArrests$highMurderState,
UH = 1, UM = 0, UCR = .9, UFA = 0)
#> cutoff TP TN FP FN SR BR percentAccuracy percentAccuracyByChance
#> 1 45.00 16 0 34 0 1.00 0.32 32 32.00
#> 2 46.00 16 1 33 0 0.98 0.32 34 32.72
#> 3 48.00 16 2 32 0 0.96 0.32 36 33.44
#> 4 53.00 16 3 31 0 0.94 0.32 38 34.16
#> 5 56.00 16 4 30 0 0.92 0.32 40 34.88
#> 6 57.00 16 5 29 0 0.90 0.32 42 35.60
#> 7 72.00 16 6 28 0 0.88 0.32 44 36.32
#> 8 81.00 16 7 27 0 0.86 0.32 46 37.04
#> 9 83.00 16 8 26 0 0.84 0.32 48 37.76
#> 10 86.00 16 9 25 0 0.82 0.32 50 38.48
#> 11 102.00 16 10 24 0 0.80 0.32 52 39.20
#> 12 106.00 16 11 23 0 0.78 0.32 54 39.92
#> 13 109.00 16 12 22 0 0.76 0.32 56 40.64
#> 14 110.00 16 14 20 0 0.72 0.32 60 42.08
#> 15 113.00 16 15 19 0 0.70 0.32 62 42.80
#> 16 115.00 16 16 18 0 0.68 0.32 64 43.52
#> 17 120.00 16 17 17 0 0.66 0.32 66 44.24
#> 18 145.00 16 20 14 0 0.60 0.32 72 46.40
#> 19 149.00 16 21 13 0 0.58 0.32 74 47.12
#> 20 151.00 16 22 12 0 0.56 0.32 76 47.84
#> 21 156.00 16 23 11 0 0.54 0.32 78 48.56
#> 22 159.00 16 24 10 0 0.52 0.32 80 49.28
#> 23 161.00 16 26 8 0 0.48 0.32 84 50.72
#> 24 174.00 16 27 7 0 0.46 0.32 86 51.44
#> 25 178.00 16 28 6 0 0.44 0.32 88 52.16
#> 26 188.00 16 29 5 0 0.42 0.32 90 52.88
#> 27 190.00 15 29 5 1 0.40 0.32 88 53.60
#> 28 201.00 15 30 4 1 0.38 0.32 90 54.32
#> 29 204.00 14 30 4 2 0.36 0.32 88 55.04
#> 30 211.00 14 31 3 2 0.34 0.32 90 55.76
#> 31 236.00 13 31 3 3 0.32 0.32 88 56.48
#> 32 238.00 12 31 3 4 0.30 0.32 86 57.20
#> 33 249.00 12 32 2 4 0.28 0.32 88 57.92
#> 34 252.00 10 32 2 6 0.24 0.32 84 59.36
#> 35 254.00 9 32 2 7 0.22 0.32 82 60.08
#> 36 255.00 8 32 2 8 0.20 0.32 80 60.80
#> 37 259.00 7 32 2 9 0.18 0.32 78 61.52
#> 38 263.00 6 32 2 10 0.16 0.32 76 62.24
#> 39 276.00 5 32 2 11 0.14 0.32 74 62.96
#> 40 279.00 5 33 1 11 0.12 0.32 76 63.68
#> 41 285.00 4 33 1 12 0.10 0.32 74 64.40
#> 42 294.00 3 33 1 13 0.08 0.32 72 65.12
#> 43 300.00 3 34 0 13 0.06 0.32 74 65.84
#> 44 335.00 2 34 0 14 0.04 0.32 72 66.56
#> 45 337.00 1 34 0 15 0.02 0.32 70 67.28
#> 46 337.01 0 34 0 16 0.00 0.32 68 68.00
#> percentAccuracyPredictingFromBaseRate RIOC
#> 1 68 NA
#> 2 68 1.00000000
#> 3 68 1.00000000
#> 4 68 1.00000000
#> 5 68 1.00000000
#> 6 68 1.00000000
#> 7 68 1.00000000
#> 8 68 1.00000000
#> 9 68 1.00000000
#> 10 68 1.00000000
#> 11 68 1.00000000
#> 12 68 1.00000000
#> 13 68 1.00000000
#> 14 68 1.00000000
#> 15 68 1.00000000
#> 16 68 1.00000000
#> 17 68 1.00000000
#> 18 68 1.00000000
#> 19 68 1.00000000
#> 20 68 1.00000000
#> 21 68 1.00000000
#> 22 68 1.00000000
#> 23 68 1.00000000
#> 24 68 1.00000000
#> 25 68 1.00000000
#> 26 68 1.00000000
#> 27 68 0.89583333
#> 28 68 0.89919355
#> 29 68 0.80468750
#> 30 68 0.81060606
#> 31 68 0.72426471
#> 32 68 0.64285714
#> 33 68 0.65277778
#> 34 68 0.50657895
#> 35 68 0.43910256
#> 36 68 0.37500000
#> 37 68 0.31402439
#> 38 68 0.25595238
#> 39 68 0.20058140
#> 40 68 0.21875000
#> 41 68 0.16666667
#> 42 68 0.11684783
#> 43 68 0.13563830
#> 44 68 0.08854167
#> 45 68 0.04336735
#> 46 68 0.00000000
#> relativeImprovementOverPredictingFromBaseRate SN SP TPrate
#> 1 -0.56250 1.0000 0.00000000 1.0000
#> 2 -0.53125 1.0000 0.02941176 1.0000
#> 3 -0.50000 1.0000 0.05882353 1.0000
#> 4 -0.46875 1.0000 0.08823529 1.0000
#> 5 -0.43750 1.0000 0.11764706 1.0000
#> 6 -0.40625 1.0000 0.14705882 1.0000
#> 7 -0.37500 1.0000 0.17647059 1.0000
#> 8 -0.34375 1.0000 0.20588235 1.0000
#> 9 -0.31250 1.0000 0.23529412 1.0000
#> 10 -0.28125 1.0000 0.26470588 1.0000
#> 11 -0.25000 1.0000 0.29411765 1.0000
#> 12 -0.21875 1.0000 0.32352941 1.0000
#> 13 -0.18750 1.0000 0.35294118 1.0000
#> 14 -0.12500 1.0000 0.41176471 1.0000
#> 15 -0.09375 1.0000 0.44117647 1.0000
#> 16 -0.06250 1.0000 0.47058824 1.0000
#> 17 -0.03125 1.0000 0.50000000 1.0000
#> 18 0.06250 1.0000 0.58823529 1.0000
#> 19 0.09375 1.0000 0.61764706 1.0000
#> 20 0.12500 1.0000 0.64705882 1.0000
#> 21 0.15625 1.0000 0.67647059 1.0000
#> 22 0.18750 1.0000 0.70588235 1.0000
#> 23 0.25000 1.0000 0.76470588 1.0000
#> 24 0.28125 1.0000 0.79411765 1.0000
#> 25 0.31250 1.0000 0.82352941 1.0000
#> 26 0.34375 1.0000 0.85294118 1.0000
#> 27 0.31250 0.9375 0.85294118 0.9375
#> 28 0.34375 0.9375 0.88235294 0.9375
#> 29 0.31250 0.8750 0.88235294 0.8750
#> 30 0.34375 0.8750 0.91176471 0.8750
#> 31 0.31250 0.8125 0.91176471 0.8125
#> 32 0.28125 0.7500 0.91176471 0.7500
#> 33 0.31250 0.7500 0.94117647 0.7500
#> 34 0.25000 0.6250 0.94117647 0.6250
#> 35 0.21875 0.5625 0.94117647 0.5625
#> 36 0.18750 0.5000 0.94117647 0.5000
#> 37 0.15625 0.4375 0.94117647 0.4375
#> 38 0.12500 0.3750 0.94117647 0.3750
#> 39 0.09375 0.3125 0.94117647 0.3125
#> 40 0.12500 0.3125 0.97058824 0.3125
#> 41 0.09375 0.2500 0.97058824 0.2500
#> 42 0.06250 0.1875 0.97058824 0.1875
#> 43 0.09375 0.1875 1.00000000 0.1875
#> 44 0.06250 0.1250 1.00000000 0.1250
#> 45 0.03125 0.0625 1.00000000 0.0625
#> 46 0.00000 0.0000 1.00000000 0.0000
#> TNrate FNrate FPrate HR FAR PPV NPV FDR
#> 1 0.00000000 0.0000 1.00000000 1.0000 1.00000000 0.3200000 NA 0.6800000
#> 2 0.02941176 0.0000 0.97058824 1.0000 0.97058824 0.3265306 1.0000000 0.6734694
#> 3 0.05882353 0.0000 0.94117647 1.0000 0.94117647 0.3333333 1.0000000 0.6666667
#> 4 0.08823529 0.0000 0.91176471 1.0000 0.91176471 0.3404255 1.0000000 0.6595745
#> 5 0.11764706 0.0000 0.88235294 1.0000 0.88235294 0.3478261 1.0000000 0.6521739
#> 6 0.14705882 0.0000 0.85294118 1.0000 0.85294118 0.3555556 1.0000000 0.6444444
#> 7 0.17647059 0.0000 0.82352941 1.0000 0.82352941 0.3636364 1.0000000 0.6363636
#> 8 0.20588235 0.0000 0.79411765 1.0000 0.79411765 0.3720930 1.0000000 0.6279070
#> 9 0.23529412 0.0000 0.76470588 1.0000 0.76470588 0.3809524 1.0000000 0.6190476
#> 10 0.26470588 0.0000 0.73529412 1.0000 0.73529412 0.3902439 1.0000000 0.6097561
#> 11 0.29411765 0.0000 0.70588235 1.0000 0.70588235 0.4000000 1.0000000 0.6000000
#> 12 0.32352941 0.0000 0.67647059 1.0000 0.67647059 0.4102564 1.0000000 0.5897436
#> 13 0.35294118 0.0000 0.64705882 1.0000 0.64705882 0.4210526 1.0000000 0.5789474
#> 14 0.41176471 0.0000 0.58823529 1.0000 0.58823529 0.4444444 1.0000000 0.5555556
#> 15 0.44117647 0.0000 0.55882353 1.0000 0.55882353 0.4571429 1.0000000 0.5428571
#> 16 0.47058824 0.0000 0.52941176 1.0000 0.52941176 0.4705882 1.0000000 0.5294118
#> 17 0.50000000 0.0000 0.50000000 1.0000 0.50000000 0.4848485 1.0000000 0.5151515
#> 18 0.58823529 0.0000 0.41176471 1.0000 0.41176471 0.5333333 1.0000000 0.4666667
#> 19 0.61764706 0.0000 0.38235294 1.0000 0.38235294 0.5517241 1.0000000 0.4482759
#> 20 0.64705882 0.0000 0.35294118 1.0000 0.35294118 0.5714286 1.0000000 0.4285714
#> 21 0.67647059 0.0000 0.32352941 1.0000 0.32352941 0.5925926 1.0000000 0.4074074
#> 22 0.70588235 0.0000 0.29411765 1.0000 0.29411765 0.6153846 1.0000000 0.3846154
#> 23 0.76470588 0.0000 0.23529412 1.0000 0.23529412 0.6666667 1.0000000 0.3333333
#> 24 0.79411765 0.0000 0.20588235 1.0000 0.20588235 0.6956522 1.0000000 0.3043478
#> 25 0.82352941 0.0000 0.17647059 1.0000 0.17647059 0.7272727 1.0000000 0.2727273
#> 26 0.85294118 0.0000 0.14705882 1.0000 0.14705882 0.7619048 1.0000000 0.2380952
#> 27 0.85294118 0.0625 0.14705882 0.9375 0.14705882 0.7500000 0.9666667 0.2500000
#> 28 0.88235294 0.0625 0.11764706 0.9375 0.11764706 0.7894737 0.9677419 0.2105263
#> 29 0.88235294 0.1250 0.11764706 0.8750 0.11764706 0.7777778 0.9375000 0.2222222
#> 30 0.91176471 0.1250 0.08823529 0.8750 0.08823529 0.8235294 0.9393939 0.1764706
#> 31 0.91176471 0.1875 0.08823529 0.8125 0.08823529 0.8125000 0.9117647 0.1875000
#> 32 0.91176471 0.2500 0.08823529 0.7500 0.08823529 0.8000000 0.8857143 0.2000000
#> 33 0.94117647 0.2500 0.05882353 0.7500 0.05882353 0.8571429 0.8888889 0.1428571
#> 34 0.94117647 0.3750 0.05882353 0.6250 0.05882353 0.8333333 0.8421053 0.1666667
#> 35 0.94117647 0.4375 0.05882353 0.5625 0.05882353 0.8181818 0.8205128 0.1818182
#> 36 0.94117647 0.5000 0.05882353 0.5000 0.05882353 0.8000000 0.8000000 0.2000000
#> 37 0.94117647 0.5625 0.05882353 0.4375 0.05882353 0.7777778 0.7804878 0.2222222
#> 38 0.94117647 0.6250 0.05882353 0.3750 0.05882353 0.7500000 0.7619048 0.2500000
#> 39 0.94117647 0.6875 0.05882353 0.3125 0.05882353 0.7142857 0.7441860 0.2857143
#> 40 0.97058824 0.6875 0.02941176 0.3125 0.02941176 0.8333333 0.7500000 0.1666667
#> 41 0.97058824 0.7500 0.02941176 0.2500 0.02941176 0.8000000 0.7333333 0.2000000
#> 42 0.97058824 0.8125 0.02941176 0.1875 0.02941176 0.7500000 0.7173913 0.2500000
#> 43 1.00000000 0.8125 0.00000000 0.1875 0.00000000 1.0000000 0.7234043 0.0000000
#> 44 1.00000000 0.8750 0.00000000 0.1250 0.00000000 1.0000000 0.7083333 0.0000000
#> 45 1.00000000 0.9375 0.00000000 0.0625 0.00000000 1.0000000 0.6938776 0.0000000
#> 46 1.00000000 1.0000 0.00000000 0.0000 0.00000000 NA 0.6800000 NA
#> FOR youdenJ balancedAccuracy f1Score mcc
#> 1 NA 0.00000000 0.5000000 0.4848485 NA
#> 2 0.00000000 0.02941176 0.5147059 0.4923077 0.09799919
#> 3 0.00000000 0.05882353 0.5294118 0.5000000 0.14002801
#> 4 0.00000000 0.08823529 0.5441176 0.5079365 0.17331344
#> 5 0.00000000 0.11764706 0.5588235 0.5161290 0.20228869
#> 6 0.00000000 0.14705882 0.5735294 0.5245902 0.22866478
#> 7 0.00000000 0.17647059 0.5882353 0.5333333 0.25332020
#> 8 0.00000000 0.20588235 0.6029412 0.5423729 0.27678040
#> 9 0.00000000 0.23529412 0.6176471 0.5517241 0.29939248
#> 10 0.00000000 0.26470588 0.6323529 0.5614035 0.32140295
#> 11 0.00000000 0.29411765 0.6470588 0.5714286 0.34299717
#> 12 0.00000000 0.32352941 0.6617647 0.5818182 0.36432131
#> 13 0.00000000 0.35294118 0.6764706 0.5925926 0.38549554
#> 14 0.00000000 0.41176471 0.7058824 0.6153846 0.42779263
#> 15 0.00000000 0.44117647 0.7205882 0.6274510 0.44908871
#> 16 0.00000000 0.47058824 0.7352941 0.6400000 0.47058824
#> 17 0.00000000 0.50000000 0.7500000 0.6530612 0.49236596
#> 18 0.00000000 0.58823529 0.7941176 0.6956522 0.56011203
#> 19 0.00000000 0.61764706 0.8088235 0.7111111 0.58375576
#> 20 0.00000000 0.64705882 0.8235294 0.7272727 0.60806899
#> 21 0.00000000 0.67647059 0.8382353 0.7441860 0.63314411
#> 22 0.00000000 0.70588235 0.8529412 0.7619048 0.65908204
#> 23 0.00000000 0.76470588 0.8823529 0.8000000 0.71400555
#> 24 0.00000000 0.79411765 0.8970588 0.8205128 0.74325613
#> 25 0.00000000 0.82352941 0.9117647 0.8421053 0.77390599
#> 26 0.00000000 0.85294118 0.9264706 0.8648649 0.80613891
#> 27 0.03333333 0.79044118 0.8952206 0.8333333 0.75265055
#> 28 0.03225806 0.81985294 0.9099265 0.8571429 0.78791208
#> 29 0.06250000 0.75735294 0.8786765 0.8235294 0.73601476
#> 30 0.06060606 0.78676471 0.8933824 0.8484848 0.77475233
#> 31 0.08823529 0.72426471 0.8621324 0.8125000 0.72426471
#> 32 0.11428571 0.66176471 0.8308824 0.7741935 0.67363307
#> 33 0.11111111 0.69117647 0.8455882 0.8000000 0.71808049
#> 34 0.15789474 0.56617647 0.7830882 0.7142857 0.61839909
#> 35 0.17948718 0.50367647 0.7518382 0.6666667 0.56718204
#> 36 0.20000000 0.44117647 0.7205882 0.6153846 0.51449576
#> 37 0.21951220 0.37867647 0.6893382 0.5600000 0.45978478
#> 38 0.23809524 0.31617647 0.6580882 0.5000000 0.40230864
#> 39 0.25581395 0.25367647 0.6268382 0.4347826 0.34103299
#> 40 0.25000000 0.28308824 0.6415441 0.4545455 0.40636782
#> 41 0.26666667 0.22058824 0.6102941 0.3809524 0.34299717
#> 42 0.28260870 0.15808824 0.5790441 0.3000000 0.27182543
#> 43 0.27659574 0.18750000 0.5937500 0.3157895 0.36829105
#> 44 0.29166667 0.12500000 0.5625000 0.2222222 0.29755952
#> 45 0.30612245 0.06250000 0.5312500 0.1176471 0.20824828
#> 46 0.32000000 0.00000000 0.5000000 NA NA
#> diagnosticOddsRatio positiveLikelihoodRatio negativeLikelihoodRatio
#> 1 NA 1.000000 NA
#> 2 NA 1.030303 0.00000000
#> 3 NA 1.062500 0.00000000
#> 4 NA 1.096774 0.00000000
#> 5 NA 1.133333 0.00000000
#> 6 NA 1.172414 0.00000000
#> 7 NA 1.214286 0.00000000
#> 8 NA 1.259259 0.00000000
#> 9 NA 1.307692 0.00000000
#> 10 NA 1.360000 0.00000000
#> 11 NA 1.416667 0.00000000
#> 12 NA 1.478261 0.00000000
#> 13 NA 1.545455 0.00000000
#> 14 NA 1.700000 0.00000000
#> 15 NA 1.789474 0.00000000
#> 16 NA 1.888889 0.00000000
#> 17 NA 2.000000 0.00000000
#> 18 NA 2.428571 0.00000000
#> 19 NA 2.615385 0.00000000
#> 20 NA 2.833333 0.00000000
#> 21 NA 3.090909 0.00000000
#> 22 NA 3.400000 0.00000000
#> 23 NA 4.250000 0.00000000
#> 24 NA 4.857143 0.00000000
#> 25 NA 5.666667 0.00000000
#> 26 NA 6.800000 0.00000000
#> 27 87.000000 6.375000 0.07327586
#> 28 112.500000 7.968750 0.07083333
#> 29 52.500000 7.437500 0.14166667
#> 30 72.333333 9.916667 0.13709677
#> 31 44.777778 9.208333 0.20564516
#> 32 31.000000 8.500000 0.27419355
#> 33 48.000000 12.750000 0.26562500
#> 34 26.666667 10.625000 0.39843750
#> 35 20.571429 9.562500 0.46484375
#> 36 16.000000 8.500000 0.53125000
#> 37 12.444444 7.437500 0.59765625
#> 38 9.600000 6.375000 0.66406250
#> 39 7.272727 5.312500 0.73046875
#> 40 15.000000 10.625000 0.70833333
#> 41 11.000000 8.500000 0.77272727
#> 42 7.615385 6.375000 0.83712121
#> 43 NA NA 0.81250000
#> 44 NA NA 0.87500000
#> 45 NA NA 0.93750000
#> 46 NA NA 1.00000000
#> dPrimeSDT betaSDT cSDT aSDT bSDT
#> 1 NA NA NA NA NA
#> 2 NA 0.0000000 NA 0.7573529 0.02857143
#> 3 NA 0.0000000 NA 0.7647059 0.05555556
#> 4 NA 0.0000000 NA 0.7720588 0.08108108
#> 5 NA 0.0000000 NA 0.7794118 0.10526316
#> 6 NA 0.0000000 NA 0.7867647 0.12820513
#> 7 NA 0.0000000 NA 0.7941176 0.15000000
#> 8 NA 0.0000000 NA 0.8014706 0.17073171
#> 9 NA 0.0000000 NA 0.8088235 0.19047619
#> 10 NA 0.0000000 NA 0.8161765 0.20930233
#> 11 NA 0.0000000 NA 0.8235294 0.22727273
#> 12 NA 0.0000000 NA 0.8308824 0.24444444
#> 13 NA 0.0000000 NA 0.8382353 0.26086957
#> 14 NA 0.0000000 NA 0.8529412 0.29166667
#> 15 NA 0.0000000 NA 0.8602941 0.30612245
#> 16 NA 0.0000000 NA 0.8676471 0.32000000
#> 17 NA 0.0000000 NA 0.8750000 0.33333333
#> 18 NA 0.0000000 NA 0.8970588 0.37777778
#> 19 NA 0.0000000 NA 0.9044118 0.39534884
#> 20 NA 0.0000000 NA 0.9117647 0.41463415
#> 21 NA 0.0000000 NA 0.9191176 0.43589744
#> 22 NA 0.0000000 NA 0.9264706 0.45945946
#> 23 NA 0.0000000 NA 0.9411765 0.51515152
#> 24 NA 0.0000000 NA 0.9485294 0.54838710
#> 25 NA 0.0000000 NA 0.9558824 0.58620690
#> 26 NA 0.0000000 NA 0.9632353 0.62962963
#> 27 2.583252 0.5344995 -0.24249457 0.9384191 0.78703704
#> 28 2.720952 0.6234551 -0.17364456 0.9476103 0.85000000
#> 29 2.337181 1.0435544 0.01824103 0.9246324 1.02000000
#> 30 2.502052 1.2864643 0.10067643 0.9356618 1.10869565
#> 31 2.238849 1.6820865 0.23227784 0.9145221 1.29347826
#> 32 2.026192 1.9859180 0.33860624 0.8933824 1.47826087
#> 33 2.239216 2.7093703 0.44511836 0.9080882 1.61904762
#> 34 1.883366 3.2330273 0.62304355 0.8694853 2.02380952
#> 35 1.722037 3.3595638 0.70370789 0.8501838 2.22619048
#> 36 1.564726 3.4013910 0.78236324 0.8308824 2.42857143
#> 37 1.407416 3.3595638 0.86101858 0.8110557 2.51331497
#> 38 1.246087 3.2330273 0.94168292 0.7898284 2.58525346
#> 39 1.075950 3.0184193 1.02675144 0.7663603 2.62114537
#> 40 1.400734 5.2892304 1.18914319 0.7972426 3.22784810
#> 41 1.215020 4.7476784 1.28199986 0.7757353 3.40000000
#> 42 1.002363 4.0213169 1.38832826 0.7503064 3.44839858
#> 43 NA NA NA 0.7968750 6.33333333
#> 44 NA NA NA 0.7812500 9.00000000
#> 45 NA NA NA 0.7656250 17.00000000
#> 46 NA NA NA NA NA
#> differenceBetweenPredictedAndObserved informationGain overallUtility
#> 1 49.6000 NA 0.320
#> 2 49.6000 NA 0.338
#> 3 49.6000 NA 0.356
#> 4 49.6000 NA 0.374
#> 5 49.6000 NA 0.392
#> 6 75.8000 NA 0.410
#> 7 75.8000 NA 0.428
#> 8 75.8000 NA 0.446
#> 9 75.8000 NA 0.464
#> 10 75.8000 NA 0.482
#> 11 107.2000 NA 0.500
#> 12 107.2000 NA 0.518
#> 13 107.2000 NA 0.536
#> 14 107.2000 NA 0.572
#> 15 117.6000 NA 0.590
#> 16 117.6000 NA 0.608
#> 17 117.6000 NA 0.626
#> 18 153.1667 NA 0.680
#> 19 153.1667 NA 0.698
#> 20 153.1667 NA 0.716
#> 21 153.1667 NA 0.734
#> 22 175.0000 NA 0.752
#> 23 175.0000 NA 0.788
#> 24 175.0000 NA 0.806
#> 25 175.0000 NA 0.824
#> 26 175.0000 NA 0.842
#> 27 207.8000 0.45336483 0.822
#> 28 207.8000 0.49476883 0.840
#> 29 207.8000 0.41340219 0.820
#> 30 207.8000 0.45810281 0.838
#> 31 207.8000 0.38881826 0.818
#> 32 247.6000 0.32890663 0.798
#> 33 247.6000 0.37636708 0.816
#> 34 247.6000 0.27014667 0.776
#> 35 265.6000 0.22430896 0.756
#> 36 265.6000 0.18245336 0.736
#> 37 265.6000 0.14421850 0.716
#> 38 265.6000 0.10941594 0.696
#> 39 265.6000 0.07803185 0.676
#> 40 265.6000 0.11245402 0.694
#> 41 309.4000 0.07921198 0.674
#> 42 309.4000 0.04921665 0.654
#> 43 309.4000 NA 0.672
#> 44 309.4000 NA 0.652
#> 45 309.4000 NA 0.632
#> 46 NA NA 0.612
