# performance: Function to create performance objects In ROCR: Visualizing the Performance of Scoring Classifiers

## Description

All kinds of predictor evaluations are performed using this function.

## Usage

 `1` ```performance(prediction.obj, measure, x.measure = "cutoff", ...) ```

## Arguments

 `prediction.obj` An object of class `prediction`. `measure` Performance measure to use for the evaluation. A complete list of the performance measures that are available for `measure` and `x.measure` is given in the 'Details' section. `x.measure` A second performance measure. If different from the default, a two-dimensional curve, with `x.measure` taken to be the unit in direction of the x axis, and `measure` to be the unit in direction of the y axis, is created. This curve is parametrized with the cutoff. `...` Optional arguments (specific to individual performance measures).

## Details

Here is the list of available performance measures. Let Y and Yhat be random variables representing the class and the prediction for a randomly drawn sample, respectively. We denote by + and - the positive and negative class, respectively. Further, we use the following abbreviations for empirical quantities: P (\# positive samples), N (\# negative samples), TP (\# true positives), TN (\# true negatives), FP (\# false positives), FN (\# false negatives).

`acc`:

Accuracy. P(Yhat = Y). Estimated as: (TP+TN)/(P+N).

`err`:

Error rate. P(Yhat != Y). Estimated as: (FP+FN)/(P+N).

`fpr`:

False positive rate. P(Yhat = + | Y = -). Estimated as: FP/N.

`fall`:

Fallout. Same as `fpr`.

`tpr`:

True positive rate. P(Yhat = + | Y = +). Estimated as: TP/P.

`rec`:

Recall. Same as `tpr`.

`sens`:

Sensitivity. Same as `tpr`.

`fnr`:

False negative rate. P(Yhat = - | Y = +). Estimated as: FN/P.

`miss`:

Miss. Same as `fnr`.

`tnr`:

True negative rate. P(Yhat = - | Y = -).

`spec`:

Specificity. Same as `tnr`.

`ppv`:

Positive predictive value. P(Y = + | Yhat = +). Estimated as: TP/(TP+FP).

`prec`:

Precision. Same as `ppv`.

`npv`:

Negative predictive value. P(Y = - | Yhat = -). Estimated as: TN/(TN+FN).

`pcfall`:

Prediction-conditioned fallout. P(Y = - | Yhat = +). Estimated as: FP/(TP+FP).

`pcmiss`:

Prediction-conditioned miss. P(Y = + | Yhat = -). Estimated as: FN/(TN+FN).

`rpp`:

Rate of positive predictions. P(Yhat = +). Estimated as: (TP+FP)/(TP+FP+TN+FN).

`rnp`:

Rate of negative predictions. P(Yhat = -). Estimated as: (TN+FN)/(TP+FP+TN+FN).

`phi`:

Phi correlation coefficient. (TP*TN - FP*FN)/(sqrt((TP+FN)*(TN+FP)*(TP+FP)*(TN+FN))). Yields a number between -1 and 1, with 1 indicating a perfect prediction, 0 indicating a random prediction. Values below 0 indicate a worse than random prediction.

`mat`:

Matthews correlation coefficient. Same as `phi`.

`mi`:

Mutual information. I(Yhat, Y) := H(Y) - H(Y | Yhat), where H is the (conditional) entropy. Entropies are estimated naively (no bias correction).

`chisq`:

Chi square test statistic. `?chisq.test` for details. Note that R might raise a warning if the sample size is too small.

`odds`:

Odds ratio. (TP*TN)/(FN*FP). Note that odds ratio produces Inf or NA values for all cutoffs corresponding to FN=0 or FP=0. This can substantially decrease the plotted cutoff region.

`lift`:

Lift value. P(Yhat = + | Y = +)/P(Yhat = +).

`f`:

Precision-recall F measure (van Rijsbergen, 1979). Weighted harmonic mean of precision (P) and recall (R). F = 1/ (alpha*1/P + (1-alpha)*1/R). If alpha=1/2, the mean is balanced. A frequent equivalent formulation is F = (beta^2+1) * P * R / (R + beta^2 * P). In this formulation, the mean is balanced if beta=1. Currently, ROCR only accepts the alpha version as input (e.g. alpha=0.5). If no value for alpha is given, the mean will be balanced by default.

`rch`:

ROC convex hull. A ROC (=`tpr` vs `fpr`) curve with concavities (which represent suboptimal choices of cutoff) removed (Fawcett 2001). Since the result is already a parametric performance curve, it cannot be used in combination with other measures.

`auc`:

Area under the ROC curve. This is equal to the value of the Wilcoxon-Mann-Whitney test statistic and also the probability that the classifier will score are randomly drawn positive sample higher than a randomly drawn negative sample. Since the output of `auc` is cutoff-independent, this measure cannot be combined with other measures into a parametric curve. The partial area under the ROC curve up to a given false positive rate can be calculated by passing the optional parameter `fpr.stop=0.5` (or any other value between 0 and 1) to `performance`.

`aucpr`:

Area under the Precision/Recall curve. Since the output of `aucpr` is cutoff-independent, this measure cannot be combined with other measures into a parametric curve.

`prbe`:

Precision-recall break-even point. The cutoff(s) where precision and recall are equal. At this point, positive and negative predictions are made at the same rate as their prevalence in the data. Since the output of `prbe` is just a cutoff-independent scalar, this measure cannot be combined with other measures into a parametric curve.

`cal`:

Calibration error. The calibration error is the absolute difference between predicted confidence and actual reliability. This error is estimated at all cutoffs by sliding a window across the range of possible cutoffs. The default window size of 100 can be adjusted by passing the optional parameter `window.size=200` to `performance`. E.g., if for several positive samples the output of the classifier is around 0.75, you might expect from a well-calibrated classifier that the fraction of them which is correctly predicted as positive is also around 0.75. In a well-calibrated classifier, the probabilistic confidence estimates are realistic. Only for use with probabilistic output (i.e. scores between 0 and 1).

`mxe`:

Mean cross-entropy. Only for use with probabilistic output. MXE := - 1/(P+N) ∑_{y_i=+} ln(yhat_i) + ∑_{y_i=-} ln(1-yhat_i). Since the output of `mxe` is just a cutoff-independent scalar, this measure cannot be combined with other measures into a parametric curve.

`rmse`:

Root-mean-squared error. Only for use with numerical class labels. RMSE := sqrt(1/(P+N) ∑_i (y_i - yhat_i)^2). Since the output of `rmse` is just a cutoff-independent scalar, this measure cannot be combined with other measures into a parametric curve.

`sar`:

Score combinining performance measures of different characteristics, in the attempt of creating a more "robust" measure (cf. Caruana R., ROCAI2004): SAR = 1/3 * ( Accuracy + Area under the ROC curve + Root mean-squared error ).

`ecost`:

Expected cost. For details on cost curves, cf. Drummond&Holte 2000,2004. `ecost` has an obligatory x axis, the so-called 'probability-cost function'; thus it cannot be combined with other measures. While using `ecost` one is interested in the lower envelope of a set of lines, it might be instructive to plot the whole set of lines in addition to the lower envelope. An example is given in `demo(ROCR)`.

`cost`:

Cost of a classifier when class-conditional misclassification costs are explicitly given. Accepts the optional parameters `cost.fp` and `cost.fn`, by which the costs for false positives and negatives can be adjusted, respectively. By default, both are set to 1.

## Value

An S4 object of class `performance`.

## Note

Here is how to call `performance()` to create some standard evaluation plots:

ROC curves:

measure="tpr", x.measure="fpr".

Precision/recall graphs:

measure="prec", x.measure="rec".

Sensitivity/specificity plots:

measure="sens", x.measure="spec".

Lift charts:

measure="lift", x.measure="rpp".

## Author(s)

Tobias Sing tobias.sing@gmail.com, Oliver Sander osander@gmail.com

## References

A detailed list of references can be found on the ROCR homepage at http://rocr.bioinf.mpi-sb.mpg.de.

`prediction`, `prediction-class`, `performance-class`, `plot.performance`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```# computing a simple ROC curve (x-axis: fpr, y-axis: tpr) library(ROCR) data(ROCR.simple) pred <- prediction( ROCR.simple\$predictions, ROCR.simple\$labels) pred perf <- performance(pred,"tpr","fpr") perf plot(perf) # precision/recall curve (x-axis: recall, y-axis: precision) perf <- performance(pred, "prec", "rec") perf plot(perf) # sensitivity/specificity curve (x-axis: specificity, # y-axis: sensitivity) perf <- performance(pred, "sens", "spec") perf plot(perf) ```

### Example output

```Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

lowess

An object of class "prediction"
Slot "predictions":
[[1]]
[1] 0.612547843 0.364270971 0.432136142 0.140291078 0.384895941 0.244415489
[7] 0.970641299 0.890172812 0.781781371 0.868751832 0.716680598 0.360168796
[13] 0.547983407 0.385240464 0.423739359 0.101699993 0.628095575 0.744769966
[19] 0.657732644 0.490119891 0.072369921 0.172741714 0.105722115 0.890078186
[25] 0.945548941 0.984667270 0.360180429 0.448687336 0.014823599 0.543533783
[31] 0.292368449 0.701561487 0.715459280 0.714985914 0.120604738 0.319672178
[37] 0.911723615 0.757325590 0.090988280 0.529402244 0.257402979 0.589909284
[43] 0.708412104 0.326672910 0.086546283 0.879459891 0.362693564 0.230157183
[49] 0.779771989 0.876086217 0.353281048 0.212014560 0.703293499 0.689075677
[55] 0.627012496 0.240911145 0.402801992 0.134794140 0.120473353 0.665444679
[61] 0.536339509 0.623494622 0.885179651 0.353777439 0.408939895 0.265686095
[67] 0.932159806 0.248500489 0.858876675 0.491735594 0.151350957 0.694457482
[73] 0.496513160 0.123504905 0.499788081 0.310718619 0.907651100 0.340078180
[79] 0.195097957 0.371936985 0.517308606 0.419560072 0.865639036 0.018527600
[85] 0.539086009 0.005422562 0.772728821 0.703885141 0.348213542 0.277656869
[91] 0.458674210 0.059045866 0.133257805 0.083685883 0.531958184 0.429650397
[97] 0.717845453 0.537091350 0.212404891 0.930846938 0.083048377 0.468610247
[103] 0.393378108 0.663367560 0.349540913 0.194398425 0.844415442 0.959417835
[109] 0.211378771 0.943432189 0.598162949 0.834803976 0.576836208 0.380396459
[115] 0.161874325 0.912325837 0.642933593 0.392173971 0.122284044 0.586857799
[121] 0.180631658 0.085993218 0.700501359 0.060413627 0.531464015 0.084254795
[127] 0.448484671 0.938583020 0.531006532 0.785213140 0.905121019 0.748438143
[133] 0.605235403 0.842974300 0.835981859 0.364288579 0.492596896 0.488179708
[139] 0.259278968 0.991096434 0.757364019 0.288258273 0.773336236 0.040906997
[145] 0.110241034 0.760726142 0.984599159 0.253271061 0.697235328 0.620501132
[151] 0.814586047 0.300973098 0.378092079 0.016694412 0.698826511 0.658692553
[157] 0.470206008 0.501489336 0.239143340 0.050999138 0.088450984 0.107031842
[163] 0.746588080 0.480100183 0.336592126 0.579511087 0.118555284 0.233160827
[169] 0.461150807 0.370549294 0.770178504 0.537336015 0.463227453 0.790240205
[175] 0.883431431 0.745110673 0.007746305 0.012653524 0.868331219 0.439399995
[181] 0.540221346 0.567043171 0.035815400 0.806543942 0.248707470 0.696702150
[187] 0.081439129 0.336315317 0.126480399 0.636728451 0.030235062 0.268138293
[193] 0.983494405 0.728536415 0.739554341 0.522384507 0.858970526 0.383807972
[199] 0.606960209 0.138387070

Slot "labels":
[[1]]
[1] 1 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 1 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1 0 0 1
[38] 1 0 1 0 1 0 1 0 1 0 1 0 1 1 0 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0
[75] 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 1 0 0 1 1
[112] 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 1 0 1 0 1 1
[149] 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0
[186] 0 0 1 0 0 0 1 0 1 1 0 1 0 1 0
Levels: 0 < 1

Slot "cutoffs":
[[1]]
[1]         Inf 0.991096434 0.984667270 0.984599159 0.983494405 0.970641299
[7] 0.959417835 0.945548941 0.943432189 0.938583020 0.932159806 0.930846938
[13] 0.912325837 0.911723615 0.907651100 0.905121019 0.890172812 0.890078186
[19] 0.885179651 0.883431431 0.879459891 0.876086217 0.868751832 0.868331219
[25] 0.865639036 0.858970526 0.858876675 0.844415442 0.842974300 0.835981859
[31] 0.834803976 0.814586047 0.806543942 0.790240205 0.785213140 0.781781371
[37] 0.779771989 0.773336236 0.772728821 0.770178504 0.760726142 0.757364019
[43] 0.757325590 0.748438143 0.746588080 0.745110673 0.744769966 0.739554341
[49] 0.728536415 0.717845453 0.716680598 0.715459280 0.714985914 0.708412104
[55] 0.703885141 0.703293499 0.701561487 0.700501359 0.698826511 0.697235328
[61] 0.696702150 0.694457482 0.689075677 0.665444679 0.663367560 0.658692553
[67] 0.657732644 0.642933593 0.636728451 0.628095575 0.627012496 0.623494622
[73] 0.620501132 0.612547843 0.606960209 0.605235403 0.598162949 0.589909284
[79] 0.586857799 0.579511087 0.576836208 0.567043171 0.547983407 0.543533783
[85] 0.540221346 0.539086009 0.537336015 0.537091350 0.536339509 0.531958184
[91] 0.531464015 0.531006532 0.529402244 0.522384507 0.517308606 0.501489336
[97] 0.499788081 0.496513160 0.492596896 0.491735594 0.490119891 0.488179708
[103] 0.480100183 0.470206008 0.468610247 0.463227453 0.461150807 0.458674210
[109] 0.448687336 0.448484671 0.439399995 0.432136142 0.429650397 0.423739359
[115] 0.419560072 0.408939895 0.402801992 0.393378108 0.392173971 0.385240464
[121] 0.384895941 0.383807972 0.380396459 0.378092079 0.371936985 0.370549294
[127] 0.364288579 0.364270971 0.362693564 0.360180429 0.360168796 0.353777439
[133] 0.353281048 0.349540913 0.348213542 0.340078180 0.336592126 0.336315317
[139] 0.326672910 0.319672178 0.310718619 0.300973098 0.292368449 0.288258273
[145] 0.277656869 0.268138293 0.265686095 0.259278968 0.257402979 0.253271061
[151] 0.248707470 0.248500489 0.244415489 0.240911145 0.239143340 0.233160827
[157] 0.230157183 0.212404891 0.212014560 0.211378771 0.195097957 0.194398425
[163] 0.180631658 0.172741714 0.161874325 0.151350957 0.140291078 0.138387070
[169] 0.134794140 0.133257805 0.126480399 0.123504905 0.122284044 0.120604738
[175] 0.120473353 0.118555284 0.110241034 0.107031842 0.105722115 0.101699993
[181] 0.090988280 0.088450984 0.086546283 0.085993218 0.084254795 0.083685883
[187] 0.083048377 0.081439129 0.072369921 0.060413627 0.059045866 0.050999138
[193] 0.040906997 0.035815400 0.030235062 0.018527600 0.016694412 0.014823599
[199] 0.012653524 0.007746305 0.005422562

Slot "fp":
[[1]]
[1]   0   0   0   0   1   1   2   3   3   3   3   3   3   3   4   4   4   4
[19]   4   4   4   4   5   5   5   5   5   5   5   5   5   5   5   5   5   5
[37]   6   6   6   6   7   7   7   7   7   7   7   7   7   7   7   7   7   8
[55]   9   9   9   9   9   9  10  10  11  11  11  11  11  11  12  12  12  12
[73]  12  12  12  13  13  13  13  13  14  14  14  14  14  15  15  15  15  15
[91]  15  15  15  16  16  16  17  18  19  20  21  22  23  24  25  26  27  28
[109]  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46
[127]  47  47  48  49  50  51  51  52  53  54  55  55  55  56  57  58  59  60
[145]  60  60  61  62  63  63  64  65  65  66  67  68  68  69  70  71  72  73
[163]  74  75  76  77  78  79  80  80  81  82  83  84  85  86  86  87  88  89
[181]  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 106
[199] 106 107 107

Slot "tp":
[[1]]
[1]  0  1  2  3  3  4  4  4  5  6  7  8  9 10 10 11 12 13 14 15 16 17 17 18 19
[26] 20 21 22 23 24 25 26 27 28 29 30 30 31 32 33 33 34 35 36 37 38 39 40 41 42
[51] 43 44 45 45 45 46 47 48 49 50 50 51 51 52 53 54 55 56 56 57 58 59 60 61 62
[76] 62 63 64 65 66 66 67 68 69 70 70 71 72 73 74 75 76 77 77 78 79 79 79 79 79
[101] 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79 79
[126] 79 79 80 80 80 80 80 81 81 81 81 81 82 83 83 83 83 83 83 84 85 85 85 85 86
[151] 86 86 87 87 87 87 88 88 88 88 88 88 88 88 88 88 88 88 88 89 89 89 89 89 89
[176] 89 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 90 91 92 92
[201] 93

Slot "tn":
[[1]]
[1] 107 107 107 107 106 106 105 104 104 104 104 104 104 104 103 103 103 103
[19] 103 103 103 103 102 102 102 102 102 102 102 102 102 102 102 102 102 102
[37] 101 101 101 101 100 100 100 100 100 100 100 100 100 100 100 100 100  99
[55]  98  98  98  98  98  98  97  97  96  96  96  96  96  96  95  95  95  95
[73]  95  95  95  94  94  94  94  94  93  93  93  93  93  92  92  92  92  92
[91]  92  92  92  91  91  91  90  89  88  87  86  85  84  83  82  81  80  79
[109]  78  77  76  75  74  73  72  71  70  69  68  67  66  65  64  63  62  61
[127]  60  60  59  58  57  56  56  55  54  53  52  52  52  51  50  49  48  47
[145]  47  47  46  45  44  44  43  42  42  41  40  39  39  38  37  36  35  34
[163]  33  32  31  30  29  28  27  27  26  25  24  23  22  21  21  20  19  18
[181]  17  16  15  14  13  12  11  10   9   8   7   6   5   4   3   2   1   1
[199]   1   0   0

Slot "fn":
[[1]]
[1] 93 92 91 90 90 89 89 89 88 87 86 85 84 83 83 82 81 80 79 78 77 76 76 75 74
[26] 73 72 71 70 69 68 67 66 65 64 63 63 62 61 60 60 59 58 57 56 55 54 53 52 51
[51] 50 49 48 48 48 47 46 45 44 43 43 42 42 41 40 39 38 37 37 36 35 34 33 32 31
[76] 31 30 29 28 27 27 26 25 24 23 23 22 21 20 19 18 17 16 16 15 14 14 14 14 14
[101] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[126] 14 14 13 13 13 13 13 12 12 12 12 12 11 10 10 10 10 10 10  9  8  8  8  8  7
[151]  7  7  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  4  4  4  4  4  4
[176]  4  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  1  1
[201]  0

Slot "n.pos":
[[1]]
[1] 93

Slot "n.neg":
[[1]]
[1] 107

Slot "n.pos.pred":
[[1]]
[1]   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
[19]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35
[37]  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53
[55]  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71
[73]  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89
[91]  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107
[109] 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125
[127] 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143
[145] 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161
[163] 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179
[181] 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197
[199] 198 199 200

Slot "n.neg.pred":
[[1]]
[1] 200 199 198 197 196 195 194 193 192 191 190 189 188 187 186 185 184 183
[19] 182 181 180 179 178 177 176 175 174 173 172 171 170 169 168 167 166 165
[37] 164 163 162 161 160 159 158 157 156 155 154 153 152 151 150 149 148 147
[55] 146 145 144 143 142 141 140 139 138 137 136 135 134 133 132 131 130 129
[73] 128 127 126 125 124 123 122 121 120 119 118 117 116 115 114 113 112 111
[91] 110 109 108 107 106 105 104 103 102 101 100  99  98  97  96  95  94  93
[109]  92  91  90  89  88  87  86  85  84  83  82  81  80  79  78  77  76  75
[127]  74  73  72  71  70  69  68  67  66  65  64  63  62  61  60  59  58  57
[145]  56  55  54  53  52  51  50  49  48  47  46  45  44  43  42  41  40  39
[163]  38  37  36  35  34  33  32  31  30  29  28  27  26  25  24  23  22  21
[181]  20  19  18  17  16  15  14  13  12  11  10   9   8   7   6   5   4   3
[199]   2   1   0

An object of class "performance"
Slot "x.name":
[1] "False positive rate"

Slot "y.name":
[1] "True positive rate"

Slot "alpha.name":
[1] "Cutoff"

Slot "x.values":
[[1]]
[1] 0.000000000 0.000000000 0.000000000 0.000000000 0.009345794 0.009345794
[7] 0.018691589 0.028037383 0.028037383 0.028037383 0.028037383 0.028037383
[13] 0.028037383 0.028037383 0.037383178 0.037383178 0.037383178 0.037383178
[19] 0.037383178 0.037383178 0.037383178 0.037383178 0.046728972 0.046728972
[25] 0.046728972 0.046728972 0.046728972 0.046728972 0.046728972 0.046728972
[31] 0.046728972 0.046728972 0.046728972 0.046728972 0.046728972 0.046728972
[37] 0.056074766 0.056074766 0.056074766 0.056074766 0.065420561 0.065420561
[43] 0.065420561 0.065420561 0.065420561 0.065420561 0.065420561 0.065420561
[49] 0.065420561 0.065420561 0.065420561 0.065420561 0.065420561 0.074766355
[55] 0.084112150 0.084112150 0.084112150 0.084112150 0.084112150 0.084112150
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[193] 0.953271028 0.962616822 0.971962617 0.981308411 0.990654206 0.990654206
[199] 0.990654206 1.000000000 1.000000000

Slot "y.values":
[[1]]
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Slot "alpha.values":
[[1]]
[1]         Inf 0.991096434 0.984667270 0.984599159 0.983494405 0.970641299
[7] 0.959417835 0.945548941 0.943432189 0.938583020 0.932159806 0.930846938
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[43] 0.757325590 0.748438143 0.746588080 0.745110673 0.744769966 0.739554341
[49] 0.728536415 0.717845453 0.716680598 0.715459280 0.714985914 0.708412104
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[193] 0.040906997 0.035815400 0.030235062 0.018527600 0.016694412 0.014823599
[199] 0.012653524 0.007746305 0.005422562

An object of class "performance"
Slot "x.name":
[1] "Recall"

Slot "y.name":
[1] "Precision"

Slot "alpha.name":
[1] "Cutoff"

Slot "x.values":
[[1]]
[1] 0.00000000 0.01075269 0.02150538 0.03225806 0.03225806 0.04301075
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[55] 0.48387097 0.49462366 0.50537634 0.51612903 0.52688172 0.53763441
[61] 0.53763441 0.54838710 0.54838710 0.55913978 0.56989247 0.58064516
[67] 0.59139785 0.60215054 0.60215054 0.61290323 0.62365591 0.63440860
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[79] 0.69892473 0.70967742 0.70967742 0.72043011 0.73118280 0.74193548
[85] 0.75268817 0.75268817 0.76344086 0.77419355 0.78494624 0.79569892
[91] 0.80645161 0.81720430 0.82795699 0.82795699 0.83870968 0.84946237
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[193] 0.96774194 0.96774194 0.96774194 0.96774194 0.96774194 0.97849462
[199] 0.98924731 0.98924731 1.00000000

Slot "y.values":
[[1]]
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Slot "alpha.values":
[[1]]
[1]         Inf 0.991096434 0.984667270 0.984599159 0.983494405 0.970641299
[7] 0.959417835 0.945548941 0.943432189 0.938583020 0.932159806 0.930846938
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[37] 0.779771989 0.773336236 0.772728821 0.770178504 0.760726142 0.757364019
[43] 0.757325590 0.748438143 0.746588080 0.745110673 0.744769966 0.739554341
[49] 0.728536415 0.717845453 0.716680598 0.715459280 0.714985914 0.708412104
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[127] 0.364288579 0.364270971 0.362693564 0.360180429 0.360168796 0.353777439
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[151] 0.248707470 0.248500489 0.244415489 0.240911145 0.239143340 0.233160827
[157] 0.230157183 0.212404891 0.212014560 0.211378771 0.195097957 0.194398425
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[175] 0.120473353 0.118555284 0.110241034 0.107031842 0.105722115 0.101699993
[181] 0.090988280 0.088450984 0.086546283 0.085993218 0.084254795 0.083685883
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[193] 0.040906997 0.035815400 0.030235062 0.018527600 0.016694412 0.014823599
[199] 0.012653524 0.007746305 0.005422562

An object of class "performance"
Slot "x.name":
[1] "Specificity"

Slot "y.name":
[1] "Sensitivity"

Slot "alpha.name":
[1] "Cutoff"

Slot "x.values":
[[1]]
[1] 1.000000000 1.000000000 1.000000000 1.000000000 0.990654206 0.990654206
[7] 0.981308411 0.971962617 0.971962617 0.971962617 0.971962617 0.971962617
[13] 0.971962617 0.971962617 0.962616822 0.962616822 0.962616822 0.962616822
[19] 0.962616822 0.962616822 0.962616822 0.962616822 0.953271028 0.953271028
[25] 0.953271028 0.953271028 0.953271028 0.953271028 0.953271028 0.953271028
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[37] 0.943925234 0.943925234 0.943925234 0.943925234 0.934579439 0.934579439
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[49] 0.934579439 0.934579439 0.934579439 0.934579439 0.934579439 0.925233645
[55] 0.915887850 0.915887850 0.915887850 0.915887850 0.915887850 0.915887850
[61] 0.906542056 0.906542056 0.897196262 0.897196262 0.897196262 0.897196262
[67] 0.897196262 0.897196262 0.887850467 0.887850467 0.887850467 0.887850467
[73] 0.887850467 0.887850467 0.887850467 0.878504673 0.878504673 0.878504673
[79] 0.878504673 0.878504673 0.869158879 0.869158879 0.869158879 0.869158879
[85] 0.869158879 0.859813084 0.859813084 0.859813084 0.859813084 0.859813084
[91] 0.859813084 0.859813084 0.859813084 0.850467290 0.850467290 0.850467290
[97] 0.841121495 0.831775701 0.822429907 0.813084112 0.803738318 0.794392523
[103] 0.785046729 0.775700935 0.766355140 0.757009346 0.747663551 0.738317757
[109] 0.728971963 0.719626168 0.710280374 0.700934579 0.691588785 0.682242991
[115] 0.672897196 0.663551402 0.654205607 0.644859813 0.635514019 0.626168224
[121] 0.616822430 0.607476636 0.598130841 0.588785047 0.579439252 0.570093458
[127] 0.560747664 0.560747664 0.551401869 0.542056075 0.532710280 0.523364486
[133] 0.523364486 0.514018692 0.504672897 0.495327103 0.485981308 0.485981308
[139] 0.485981308 0.476635514 0.467289720 0.457943925 0.448598131 0.439252336
[145] 0.439252336 0.439252336 0.429906542 0.420560748 0.411214953 0.411214953
[151] 0.401869159 0.392523364 0.392523364 0.383177570 0.373831776 0.364485981
[157] 0.364485981 0.355140187 0.345794393 0.336448598 0.327102804 0.317757009
[163] 0.308411215 0.299065421 0.289719626 0.280373832 0.271028037 0.261682243
[169] 0.252336449 0.252336449 0.242990654 0.233644860 0.224299065 0.214953271
[175] 0.205607477 0.196261682 0.196261682 0.186915888 0.177570093 0.168224299
[181] 0.158878505 0.149532710 0.140186916 0.130841121 0.121495327 0.112149533
[187] 0.102803738 0.093457944 0.084112150 0.074766355 0.065420561 0.056074766
[193] 0.046728972 0.037383178 0.028037383 0.018691589 0.009345794 0.009345794
[199] 0.009345794 0.000000000 0.000000000

Slot "y.values":
[[1]]
[1] 0.00000000 0.01075269 0.02150538 0.03225806 0.03225806 0.04301075
[7] 0.04301075 0.04301075 0.05376344 0.06451613 0.07526882 0.08602151
[13] 0.09677419 0.10752688 0.10752688 0.11827957 0.12903226 0.13978495
[19] 0.15053763 0.16129032 0.17204301 0.18279570 0.18279570 0.19354839
[25] 0.20430108 0.21505376 0.22580645 0.23655914 0.24731183 0.25806452
[31] 0.26881720 0.27956989 0.29032258 0.30107527 0.31182796 0.32258065
[37] 0.32258065 0.33333333 0.34408602 0.35483871 0.35483871 0.36559140
[43] 0.37634409 0.38709677 0.39784946 0.40860215 0.41935484 0.43010753
[49] 0.44086022 0.45161290 0.46236559 0.47311828 0.48387097 0.48387097
[55] 0.48387097 0.49462366 0.50537634 0.51612903 0.52688172 0.53763441
[61] 0.53763441 0.54838710 0.54838710 0.55913978 0.56989247 0.58064516
[67] 0.59139785 0.60215054 0.60215054 0.61290323 0.62365591 0.63440860
[73] 0.64516129 0.65591398 0.66666667 0.66666667 0.67741935 0.68817204
[79] 0.69892473 0.70967742 0.70967742 0.72043011 0.73118280 0.74193548
[85] 0.75268817 0.75268817 0.76344086 0.77419355 0.78494624 0.79569892
[91] 0.80645161 0.81720430 0.82795699 0.82795699 0.83870968 0.84946237
[97] 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237
[103] 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237
[109] 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237
[115] 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237
[121] 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237 0.84946237
[127] 0.84946237 0.86021505 0.86021505 0.86021505 0.86021505 0.86021505
[133] 0.87096774 0.87096774 0.87096774 0.87096774 0.87096774 0.88172043
[139] 0.89247312 0.89247312 0.89247312 0.89247312 0.89247312 0.89247312
[145] 0.90322581 0.91397849 0.91397849 0.91397849 0.91397849 0.92473118
[151] 0.92473118 0.92473118 0.93548387 0.93548387 0.93548387 0.93548387
[157] 0.94623656 0.94623656 0.94623656 0.94623656 0.94623656 0.94623656
[163] 0.94623656 0.94623656 0.94623656 0.94623656 0.94623656 0.94623656
[169] 0.94623656 0.95698925 0.95698925 0.95698925 0.95698925 0.95698925
[175] 0.95698925 0.95698925 0.96774194 0.96774194 0.96774194 0.96774194
[181] 0.96774194 0.96774194 0.96774194 0.96774194 0.96774194 0.96774194
[187] 0.96774194 0.96774194 0.96774194 0.96774194 0.96774194 0.96774194
[193] 0.96774194 0.96774194 0.96774194 0.96774194 0.96774194 0.97849462
[199] 0.98924731 0.98924731 1.00000000

Slot "alpha.values":
[[1]]
[1]         Inf 0.991096434 0.984667270 0.984599159 0.983494405 0.970641299
[7] 0.959417835 0.945548941 0.943432189 0.938583020 0.932159806 0.930846938
[13] 0.912325837 0.911723615 0.907651100 0.905121019 0.890172812 0.890078186
[19] 0.885179651 0.883431431 0.879459891 0.876086217 0.868751832 0.868331219
[25] 0.865639036 0.858970526 0.858876675 0.844415442 0.842974300 0.835981859
[31] 0.834803976 0.814586047 0.806543942 0.790240205 0.785213140 0.781781371
[37] 0.779771989 0.773336236 0.772728821 0.770178504 0.760726142 0.757364019
[43] 0.757325590 0.748438143 0.746588080 0.745110673 0.744769966 0.739554341
[49] 0.728536415 0.717845453 0.716680598 0.715459280 0.714985914 0.708412104
[55] 0.703885141 0.703293499 0.701561487 0.700501359 0.698826511 0.697235328
[61] 0.696702150 0.694457482 0.689075677 0.665444679 0.663367560 0.658692553
[67] 0.657732644 0.642933593 0.636728451 0.628095575 0.627012496 0.623494622
[73] 0.620501132 0.612547843 0.606960209 0.605235403 0.598162949 0.589909284
[79] 0.586857799 0.579511087 0.576836208 0.567043171 0.547983407 0.543533783
[85] 0.540221346 0.539086009 0.537336015 0.537091350 0.536339509 0.531958184
[91] 0.531464015 0.531006532 0.529402244 0.522384507 0.517308606 0.501489336
[97] 0.499788081 0.496513160 0.492596896 0.491735594 0.490119891 0.488179708
[103] 0.480100183 0.470206008 0.468610247 0.463227453 0.461150807 0.458674210
[109] 0.448687336 0.448484671 0.439399995 0.432136142 0.429650397 0.423739359
[115] 0.419560072 0.408939895 0.402801992 0.393378108 0.392173971 0.385240464
[121] 0.384895941 0.383807972 0.380396459 0.378092079 0.371936985 0.370549294
[127] 0.364288579 0.364270971 0.362693564 0.360180429 0.360168796 0.353777439
[133] 0.353281048 0.349540913 0.348213542 0.340078180 0.336592126 0.336315317
[139] 0.326672910 0.319672178 0.310718619 0.300973098 0.292368449 0.288258273
[145] 0.277656869 0.268138293 0.265686095 0.259278968 0.257402979 0.253271061
[151] 0.248707470 0.248500489 0.244415489 0.240911145 0.239143340 0.233160827
[157] 0.230157183 0.212404891 0.212014560 0.211378771 0.195097957 0.194398425
[163] 0.180631658 0.172741714 0.161874325 0.151350957 0.140291078 0.138387070
[169] 0.134794140 0.133257805 0.126480399 0.123504905 0.122284044 0.120604738
[175] 0.120473353 0.118555284 0.110241034 0.107031842 0.105722115 0.101699993
[181] 0.090988280 0.088450984 0.086546283 0.085993218 0.084254795 0.083685883
[187] 0.083048377 0.081439129 0.072369921 0.060413627 0.059045866 0.050999138
[193] 0.040906997 0.035815400 0.030235062 0.018527600 0.016694412 0.014823599
[199] 0.012653524 0.007746305 0.005422562
```

ROCR documentation built on May 2, 2020, 5:05 p.m.