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 theoryinformationGain= information gainoverallUtility= overall utility (if utilities were specified)differenceBetweenPredictedAndObserved= difference between predicted and observed values
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 informationGain
#> 1 NA NA NA NA NA NA
#> 2 NA 0.0000000 NA 0.7573529 0.02857143 NA
#> 3 NA 0.0000000 NA 0.7647059 0.05555556 NA
#> 4 NA 0.0000000 NA 0.7720588 0.08108108 NA
#> 5 NA 0.0000000 NA 0.7794118 0.10526316 NA
#> 6 NA 0.0000000 NA 0.7867647 0.12820513 NA
#> 7 NA 0.0000000 NA 0.7941176 0.15000000 NA
#> 8 NA 0.0000000 NA 0.8014706 0.17073171 NA
#> 9 NA 0.0000000 NA 0.8088235 0.19047619 NA
#> 10 NA 0.0000000 NA 0.8161765 0.20930233 NA
#> 11 NA 0.0000000 NA 0.8235294 0.22727273 NA
#> 12 NA 0.0000000 NA 0.8308824 0.24444444 NA
#> 13 NA 0.0000000 NA 0.8382353 0.26086957 NA
#> 14 NA 0.0000000 NA 0.8529412 0.29166667 NA
#> 15 NA 0.0000000 NA 0.8602941 0.30612245 NA
#> 16 NA 0.0000000 NA 0.8676471 0.32000000 NA
#> 17 NA 0.0000000 NA 0.8750000 0.33333333 NA
#> 18 NA 0.0000000 NA 0.8970588 0.37777778 NA
#> 19 NA 0.0000000 NA 0.9044118 0.39534884 NA
#> 20 NA 0.0000000 NA 0.9117647 0.41463415 NA
#> 21 NA 0.0000000 NA 0.9191176 0.43589744 NA
#> 22 NA 0.0000000 NA 0.9264706 0.45945946 NA
#> 23 NA 0.0000000 NA 0.9411765 0.51515152 NA
#> 24 NA 0.0000000 NA 0.9485294 0.54838710 NA
#> 25 NA 0.0000000 NA 0.9558824 0.58620690 NA
#> 26 NA 0.0000000 NA 0.9632353 0.62962963 NA
#> 27 2.583252 0.5344995 -0.24249457 0.9384191 0.78703704 0.45336483
#> 28 2.720952 0.6234551 -0.17364456 0.9476103 0.85000000 0.49476883
#> 29 2.337181 1.0435544 0.01824103 0.9246324 1.02000000 0.41340219
#> 30 2.502052 1.2864643 0.10067643 0.9356618 1.10869565 0.45810281
#> 31 2.238849 1.6820865 0.23227784 0.9145221 1.29347826 0.38881826
#> 32 2.026192 1.9859180 0.33860624 0.8933824 1.47826087 0.32890663
#> 33 2.239216 2.7093703 0.44511836 0.9080882 1.61904762 0.37636708
#> 34 1.883366 3.2330273 0.62304355 0.8694853 2.02380952 0.27014667
#> 35 1.722037 3.3595638 0.70370789 0.8501838 2.22619048 0.22430896
#> 36 1.564726 3.4013910 0.78236324 0.8308824 2.42857143 0.18245336
#> 37 1.407416 3.3595638 0.86101858 0.8110557 2.51331497 0.14421850
#> 38 1.246087 3.2330273 0.94168292 0.7898284 2.58525346 0.10941594
#> 39 1.075950 3.0184193 1.02675144 0.7663603 2.62114537 0.07803185
#> 40 1.400734 5.2892304 1.18914319 0.7972426 3.22784810 0.11245402
#> 41 1.215020 4.7476784 1.28199986 0.7757353 3.40000000 0.07921198
#> 42 1.002363 4.0213169 1.38832826 0.7503064 3.44839858 0.04921665
#> 43 NA NA NA 0.7968750 6.33333333 NA
#> 44 NA NA NA 0.7812500 9.00000000 NA
#> 45 NA NA NA 0.7656250 17.00000000 NA
#> 46 NA NA NA NA NA NA
#> differenceBetweenPredictedAndObserved
#> 1 49.6000
#> 2 49.6000
#> 3 49.6000
#> 4 49.6000
#> 5 49.6000
#> 6 75.8000
#> 7 75.8000
#> 8 75.8000
#> 9 75.8000
#> 10 75.8000
#> 11 107.2000
#> 12 107.2000
#> 13 107.2000
#> 14 107.2000
#> 15 117.6000
#> 16 117.6000
#> 17 117.6000
#> 18 153.1667
#> 19 153.1667
#> 20 153.1667
#> 21 153.1667
#> 22 175.0000
#> 23 175.0000
#> 24 175.0000
#> 25 175.0000
#> 26 175.0000
#> 27 207.8000
#> 28 207.8000
#> 29 207.8000
#> 30 207.8000
#> 31 207.8000
#> 32 247.6000
#> 33 247.6000
#> 34 247.6000
#> 35 265.6000
#> 36 265.6000
#> 37 265.6000
#> 38 265.6000
#> 39 265.6000
#> 40 265.6000
#> 41 309.4000
#> 42 309.4000
#> 43 309.4000
#> 44 309.4000
#> 45 309.4000
#> 46 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 informationGain
#> 1 NA NA NA NA NA NA
#> 2 NA 0.0000000 NA 0.7573529 0.02857143 NA
#> 3 NA 0.0000000 NA 0.7647059 0.05555556 NA
#> 4 NA 0.0000000 NA 0.7720588 0.08108108 NA
#> 5 NA 0.0000000 NA 0.7794118 0.10526316 NA
#> 6 NA 0.0000000 NA 0.7867647 0.12820513 NA
#> 7 NA 0.0000000 NA 0.7941176 0.15000000 NA
#> 8 NA 0.0000000 NA 0.8014706 0.17073171 NA
#> 9 NA 0.0000000 NA 0.8088235 0.19047619 NA
#> 10 NA 0.0000000 NA 0.8161765 0.20930233 NA
#> 11 NA 0.0000000 NA 0.8235294 0.22727273 NA
#> 12 NA 0.0000000 NA 0.8308824 0.24444444 NA
#> 13 NA 0.0000000 NA 0.8382353 0.26086957 NA
#> 14 NA 0.0000000 NA 0.8529412 0.29166667 NA
#> 15 NA 0.0000000 NA 0.8602941 0.30612245 NA
#> 16 NA 0.0000000 NA 0.8676471 0.32000000 NA
#> 17 NA 0.0000000 NA 0.8750000 0.33333333 NA
#> 18 NA 0.0000000 NA 0.8970588 0.37777778 NA
#> 19 NA 0.0000000 NA 0.9044118 0.39534884 NA
#> 20 NA 0.0000000 NA 0.9117647 0.41463415 NA
#> 21 NA 0.0000000 NA 0.9191176 0.43589744 NA
#> 22 NA 0.0000000 NA 0.9264706 0.45945946 NA
#> 23 NA 0.0000000 NA 0.9411765 0.51515152 NA
#> 24 NA 0.0000000 NA 0.9485294 0.54838710 NA
#> 25 NA 0.0000000 NA 0.9558824 0.58620690 NA
#> 26 NA 0.0000000 NA 0.9632353 0.62962963 NA
#> 27 2.583252 0.5344995 -0.24249457 0.9384191 0.78703704 0.45336483
#> 28 2.720952 0.6234551 -0.17364456 0.9476103 0.85000000 0.49476883
#> 29 2.337181 1.0435544 0.01824103 0.9246324 1.02000000 0.41340219
#> 30 2.502052 1.2864643 0.10067643 0.9356618 1.10869565 0.45810281
#> 31 2.238849 1.6820865 0.23227784 0.9145221 1.29347826 0.38881826
#> 32 2.026192 1.9859180 0.33860624 0.8933824 1.47826087 0.32890663
#> 33 2.239216 2.7093703 0.44511836 0.9080882 1.61904762 0.37636708
#> 34 1.883366 3.2330273 0.62304355 0.8694853 2.02380952 0.27014667
#> 35 1.722037 3.3595638 0.70370789 0.8501838 2.22619048 0.22430896
#> 36 1.564726 3.4013910 0.78236324 0.8308824 2.42857143 0.18245336
#> 37 1.407416 3.3595638 0.86101858 0.8110557 2.51331497 0.14421850
#> 38 1.246087 3.2330273 0.94168292 0.7898284 2.58525346 0.10941594
#> 39 1.075950 3.0184193 1.02675144 0.7663603 2.62114537 0.07803185
#> 40 1.400734 5.2892304 1.18914319 0.7972426 3.22784810 0.11245402
#> 41 1.215020 4.7476784 1.28199986 0.7757353 3.40000000 0.07921198
#> 42 1.002363 4.0213169 1.38832826 0.7503064 3.44839858 0.04921665
#> 43 NA NA NA 0.7968750 6.33333333 NA
#> 44 NA NA NA 0.7812500 9.00000000 NA
#> 45 NA NA NA 0.7656250 17.00000000 NA
#> 46 NA NA NA NA NA NA
#> overallUtility differenceBetweenPredictedAndObserved
#> 1 0.320 49.6000
#> 2 0.338 49.6000
#> 3 0.356 49.6000
#> 4 0.374 49.6000
#> 5 0.392 49.6000
#> 6 0.410 75.8000
#> 7 0.428 75.8000
#> 8 0.446 75.8000
#> 9 0.464 75.8000
#> 10 0.482 75.8000
#> 11 0.500 107.2000
#> 12 0.518 107.2000
#> 13 0.536 107.2000
#> 14 0.572 107.2000
#> 15 0.590 117.6000
#> 16 0.608 117.6000
#> 17 0.626 117.6000
#> 18 0.680 153.1667
#> 19 0.698 153.1667
#> 20 0.716 153.1667
#> 21 0.734 153.1667
#> 22 0.752 175.0000
#> 23 0.788 175.0000
#> 24 0.806 175.0000
#> 25 0.824 175.0000
#> 26 0.842 175.0000
#> 27 0.822 207.8000
#> 28 0.840 207.8000
#> 29 0.820 207.8000
#> 30 0.838 207.8000
#> 31 0.818 207.8000
#> 32 0.798 247.6000
#> 33 0.816 247.6000
#> 34 0.776 247.6000
#> 35 0.756 265.6000
#> 36 0.736 265.6000
#> 37 0.716 265.6000
#> 38 0.696 265.6000
#> 39 0.676 265.6000
#> 40 0.694 265.6000
#> 41 0.674 309.4000
#> 42 0.654 309.4000
#> 43 0.672 309.4000
#> 44 0.652 309.4000
#> 45 0.632 309.4000
#> 46 0.612 NA
