calculateROC: Calculate ROC Curve Statistics
In MetaIntegrator: Meta-Analysis of Gene Expression Data
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
View source: R/ROC_and_search_functions.R
Description
Calculates receiver operating characteristic curve data, including AUC (using trapezoidal method). Takes only a vector of labels and a vector of predictions.
Usage
1
calculateROC(labels, predictions, AUConly = FALSE)
Arguments
labels
Vector of labels; must have exactly two unique values (ie, cases and controls).
predictions
Vector of predictions (for instance, test scores) to be evaluated for ability to separate the two classes. Must be exactly the same length as labels.
AUConly
Return all ROC values, or just the AUC.
Details
The code borrows its core ROC calculations from the ROCR package. AUC is calculated by the trapezoidal method. AUC standard errors are calculated according to Hanley's method.
Value
Assuming AUConly=F, returns a list of values:
roc
dataframe consisting of two columns, FPR and TPR, meant for plotting
auc
area under the curve
auc.CI
95% confidence interval for AUC
Author(s)
Timothy E. Sweeney
References
The code borrows its core ROC calculations from the ROCR package.
See Also
Examples
1
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# expect an AUC near 0.5 with random test
labels <- c(rep(0, 500), rep(1, 500))
scores <- runif(1000)
calculateROC(labels, scores)
#With the real data, AUC should be around 0.85606
scoreResults <- calculateScore(tinyMetaObject$filterResults[[1]], tinyMetaObject$originalData[[1]])
rocRes <- calculateROC(predictions=scoreResults, labels=tinyMetaObject$originalData[[1]]$class)
print(rocRes$auc[[1]])
Example output
$roc
x y
1 0.000 0.000
2 0.000 0.002
3 0.000 0.004
4 0.000 0.006
5 0.000 0.008
6 0.002 0.008
7 0.002 0.010
8 0.004 0.010
9 0.004 0.012
10 0.004 0.014
11 0.004 0.016
12 0.004 0.018
13 0.004 0.020
14 0.004 0.022
15 0.006 0.022
16 0.008 0.022
17 0.010 0.022
18 0.012 0.022
19 0.012 0.024
20 0.012 0.026
21 0.012 0.028
22 0.014 0.028
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24 0.016 0.030
25 0.018 0.030
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27 0.020 0.032
28 0.020 0.034
29 0.020 0.036
30 0.022 0.036
31 0.022 0.038
32 0.024 0.038
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34 0.026 0.040
35 0.028 0.040
36 0.028 0.042
37 0.028 0.044
38 0.028 0.046
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838 0.848 0.826
839 0.848 0.828
840 0.850 0.828
841 0.850 0.830
842 0.850 0.832
843 0.852 0.832
844 0.854 0.832
845 0.854 0.834
846 0.854 0.836
847 0.854 0.838
848 0.854 0.840
849 0.856 0.840
850 0.856 0.842
851 0.856 0.844
852 0.858 0.844
853 0.858 0.846
854 0.860 0.846
855 0.862 0.846
856 0.862 0.848
857 0.864 0.848
858 0.866 0.848
859 0.866 0.850
860 0.868 0.850
861 0.870 0.850
862 0.872 0.850
863 0.872 0.852
864 0.874 0.852
865 0.874 0.854
866 0.874 0.856
867 0.874 0.858
868 0.874 0.860
869 0.874 0.862
870 0.874 0.864
871 0.874 0.866
872 0.874 0.868
873 0.876 0.868
874 0.878 0.868
875 0.880 0.868
876 0.882 0.868
877 0.884 0.868
878 0.886 0.868
879 0.886 0.870
880 0.888 0.870
881 0.888 0.872
882 0.888 0.874
883 0.890 0.874
884 0.892 0.874
885 0.894 0.874
886 0.896 0.874
887 0.896 0.876
888 0.898 0.876
889 0.900 0.876
890 0.900 0.878
891 0.900 0.880
892 0.900 0.882
893 0.900 0.884
894 0.900 0.886
895 0.902 0.886
896 0.902 0.888
897 0.904 0.888
898 0.904 0.890
899 0.904 0.892
900 0.906 0.892
901 0.906 0.894
902 0.906 0.896
903 0.906 0.898
904 0.908 0.898
905 0.908 0.900
906 0.910 0.900
907 0.910 0.902
908 0.912 0.902
909 0.914 0.902
910 0.914 0.904
911 0.916 0.904
912 0.918 0.904
913 0.920 0.904
914 0.922 0.904
915 0.922 0.906
916 0.922 0.908
917 0.922 0.910
918 0.924 0.910
919 0.924 0.912
920 0.926 0.912
921 0.926 0.914
922 0.928 0.914
923 0.930 0.914
924 0.930 0.916
925 0.932 0.916
926 0.932 0.918
927 0.934 0.918
928 0.934 0.920
929 0.934 0.922
930 0.936 0.922
931 0.938 0.922
932 0.940 0.922
933 0.942 0.922
934 0.942 0.924
935 0.942 0.926
936 0.942 0.928
937 0.944 0.928
938 0.946 0.928
939 0.948 0.928
940 0.950 0.928
941 0.952 0.928
942 0.952 0.930
943 0.952 0.932
944 0.952 0.934
945 0.952 0.936
946 0.952 0.938
947 0.954 0.938
948 0.954 0.940
949 0.956 0.940
950 0.956 0.942
951 0.958 0.942
952 0.960 0.942
953 0.960 0.944
954 0.962 0.944
955 0.964 0.944
956 0.964 0.946
957 0.964 0.948
958 0.964 0.950
959 0.966 0.950
960 0.966 0.952
961 0.966 0.954
962 0.968 0.954
963 0.968 0.956
964 0.968 0.958
965 0.970 0.958
966 0.972 0.958
967 0.972 0.960
968 0.974 0.960
969 0.976 0.960
970 0.976 0.962
971 0.976 0.964
972 0.976 0.966
973 0.976 0.968
974 0.978 0.968
975 0.980 0.968
976 0.982 0.968
977 0.982 0.970
978 0.982 0.972
979 0.984 0.972
980 0.986 0.972
981 0.988 0.972
982 0.990 0.972
983 0.990 0.974
984 0.990 0.976
985 0.990 0.978
986 0.992 0.978
987 0.992 0.980
988 0.992 0.982
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998 0.998 0.996
999 0.998 0.998
1000 1.000 0.998
1001 1.000 1.000
$auc
[,1]
[1,] 0.5049
$auc.CI
[1] 0.4690997 0.5407003
Used 12 of 12 pos genes, and 12 of 12 neg genes
[1] 0.8560606
MetaIntegrator documentation built on March 26, 2020, 6:29 p.m.
R Package Documentation
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Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/ROC_and_search_functions.R
Description
Calculates receiver operating characteristic curve data, including AUC (using trapezoidal method). Takes only a vector of labels and a vector of predictions.
Usage
1 | calculateROC(labels, predictions, AUConly = FALSE)
|
Arguments
labels |
Vector of labels; must have exactly two unique values (ie, cases and controls). |
predictions |
Vector of predictions (for instance, test scores) to be evaluated for ability to separate the two classes. Must be exactly the same length as labels. |
AUConly |
Return all ROC values, or just the AUC. |
Details
The code borrows its core ROC calculations from the ROCR package. AUC is calculated by the trapezoidal method. AUC standard errors are calculated according to Hanley's method.
Value
Assuming AUConly=F, returns a list of values:
roc |
dataframe consisting of two columns, FPR and TPR, meant for plotting |
auc |
area under the curve |
auc.CI |
95% confidence interval for AUC |
Author(s)
Timothy E. Sweeney
References
The code borrows its core ROC calculations from the ROCR package.
See Also
Examples
1 2 3 4 5 6 7 8 | # expect an AUC near 0.5 with random test
labels <- c(rep(0, 500), rep(1, 500))
scores <- runif(1000)
calculateROC(labels, scores)
#With the real data, AUC should be around 0.85606
scoreResults <- calculateScore(tinyMetaObject$filterResults[[1]], tinyMetaObject$originalData[[1]])
rocRes <- calculateROC(predictions=scoreResults, labels=tinyMetaObject$originalData[[1]]$class)
print(rocRes$auc[[1]])
|
Example output
$roc
x y
1 0.000 0.000
2 0.000 0.002
3 0.000 0.004
4 0.000 0.006
5 0.000 0.008
6 0.002 0.008
7 0.002 0.010
8 0.004 0.010
9 0.004 0.012
10 0.004 0.014
11 0.004 0.016
12 0.004 0.018
13 0.004 0.020
14 0.004 0.022
15 0.006 0.022
16 0.008 0.022
17 0.010 0.022
18 0.012 0.022
19 0.012 0.024
20 0.012 0.026
21 0.012 0.028
22 0.014 0.028
23 0.014 0.030
24 0.016 0.030
25 0.018 0.030
26 0.020 0.030
27 0.020 0.032
28 0.020 0.034
29 0.020 0.036
30 0.022 0.036
31 0.022 0.038
32 0.024 0.038
33 0.026 0.038
34 0.026 0.040
35 0.028 0.040
36 0.028 0.042
37 0.028 0.044
38 0.028 0.046
39 0.028 0.048
40 0.030 0.048
41 0.032 0.048
42 0.032 0.050
43 0.034 0.050
44 0.036 0.050
45 0.036 0.052
46 0.036 0.054
47 0.038 0.054
48 0.038 0.056
49 0.038 0.058
50 0.038 0.060
51 0.040 0.060
52 0.040 0.062
53 0.040 0.064
54 0.040 0.066
55 0.040 0.068
56 0.040 0.070
57 0.040 0.072
58 0.040 0.074
59 0.042 0.074
60 0.042 0.076
61 0.042 0.078
62 0.042 0.080
63 0.042 0.082
64 0.042 0.084
65 0.044 0.084
66 0.046 0.084
67 0.048 0.084
68 0.050 0.084
69 0.050 0.086
70 0.050 0.088
71 0.050 0.090
72 0.050 0.092
73 0.052 0.092
74 0.054 0.092
75 0.056 0.092
76 0.058 0.092
77 0.058 0.094
78 0.060 0.094
79 0.060 0.096
80 0.060 0.098
81 0.062 0.098
82 0.062 0.100
83 0.062 0.102
84 0.062 0.104
85 0.062 0.106
86 0.062 0.108
87 0.062 0.110
88 0.064 0.110
89 0.066 0.110
90 0.068 0.110
91 0.070 0.110
92 0.070 0.112
93 0.070 0.114
94 0.070 0.116
95 0.072 0.116
96 0.074 0.116
97 0.074 0.118
98 0.074 0.120
99 0.076 0.120
100 0.076 0.122
101 0.076 0.124
102 0.076 0.126
103 0.078 0.126
104 0.080 0.126
105 0.080 0.128
106 0.080 0.130
107 0.080 0.132
108 0.082 0.132
109 0.082 0.134
110 0.082 0.136
111 0.084 0.136
112 0.084 0.138
113 0.086 0.138
114 0.088 0.138
115 0.090 0.138
116 0.092 0.138
117 0.092 0.140
118 0.092 0.142
119 0.094 0.142
120 0.094 0.144
121 0.094 0.146
122 0.096 0.146
123 0.098 0.146
124 0.098 0.148
125 0.098 0.150
126 0.098 0.152
127 0.100 0.152
128 0.100 0.154
129 0.102 0.154
130 0.102 0.156
131 0.102 0.158
132 0.102 0.160
133 0.104 0.160
134 0.106 0.160
135 0.106 0.162
136 0.108 0.162
137 0.110 0.162
138 0.112 0.162
139 0.112 0.164
140 0.114 0.164
141 0.114 0.166
142 0.116 0.166
143 0.118 0.166
144 0.118 0.168
145 0.118 0.170
146 0.120 0.170
147 0.120 0.172
148 0.122 0.172
149 0.124 0.172
150 0.126 0.172
151 0.128 0.172
152 0.128 0.174
153 0.128 0.176
154 0.128 0.178
155 0.128 0.180
156 0.128 0.182
157 0.128 0.184
158 0.130 0.184
159 0.132 0.184
160 0.134 0.184
161 0.136 0.184
162 0.138 0.184
163 0.138 0.186
164 0.138 0.188
165 0.138 0.190
166 0.138 0.192
167 0.140 0.192
168 0.142 0.192
169 0.144 0.192
170 0.144 0.194
171 0.144 0.196
172 0.144 0.198
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174 0.148 0.198
175 0.150 0.198
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177 0.152 0.200
178 0.154 0.200
179 0.156 0.200
180 0.158 0.200
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183 0.160 0.204
184 0.160 0.206
185 0.160 0.208
186 0.160 0.210
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188 0.160 0.214
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193 0.166 0.218
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198 0.172 0.222
199 0.174 0.222
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202 0.178 0.224
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205 0.184 0.224
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214 0.190 0.236
215 0.192 0.236
216 0.192 0.238
217 0.192 0.240
218 0.194 0.240
219 0.196 0.240
220 0.196 0.242
221 0.196 0.244
222 0.196 0.246
223 0.198 0.246
224 0.200 0.246
225 0.202 0.246
226 0.204 0.246
227 0.206 0.246
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230 0.210 0.248
231 0.212 0.248
232 0.214 0.248
233 0.216 0.248
234 0.216 0.250
235 0.216 0.252
236 0.218 0.252
237 0.218 0.254
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240 0.220 0.258
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250 0.232 0.266
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253 0.236 0.268
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256 0.240 0.270
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258 0.242 0.272
259 0.242 0.274
260 0.244 0.274
261 0.244 0.276
262 0.246 0.276
263 0.248 0.276
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265 0.248 0.280
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267 0.250 0.282
268 0.250 0.284
269 0.250 0.286
270 0.252 0.286
271 0.252 0.288
272 0.254 0.288
273 0.256 0.288
274 0.256 0.290
275 0.256 0.292
276 0.258 0.292
277 0.258 0.294
278 0.260 0.294
279 0.260 0.296
280 0.260 0.298
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283 0.262 0.302
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285 0.266 0.302
286 0.268 0.302
287 0.270 0.302
288 0.272 0.302
289 0.274 0.302
290 0.276 0.302
291 0.278 0.302
292 0.280 0.302
293 0.280 0.304
294 0.282 0.304
295 0.284 0.304
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297 0.284 0.308
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299 0.288 0.308
300 0.288 0.310
301 0.290 0.310
302 0.290 0.312
303 0.290 0.314
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305 0.294 0.314
306 0.296 0.314
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308 0.300 0.314
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310 0.304 0.314
311 0.306 0.314
312 0.308 0.314
313 0.310 0.314
314 0.310 0.316
315 0.310 0.318
316 0.312 0.318
317 0.314 0.318
318 0.316 0.318
319 0.318 0.318
320 0.320 0.318
321 0.320 0.320
322 0.320 0.322
323 0.320 0.324
324 0.322 0.324
325 0.324 0.324
326 0.326 0.324
327 0.328 0.324
328 0.330 0.324
329 0.332 0.324
330 0.332 0.326
331 0.332 0.328
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333 0.336 0.328
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335 0.338 0.330
336 0.338 0.332
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340 0.344 0.334
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342 0.346 0.336
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345 0.348 0.340
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349 0.350 0.346
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355 0.356 0.352
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370 0.380 0.358
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372 0.382 0.360
373 0.384 0.360
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375 0.386 0.362
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377 0.390 0.362
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380 0.394 0.364
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382 0.394 0.368
383 0.396 0.368
384 0.398 0.368
385 0.400 0.368
386 0.400 0.370
387 0.400 0.372
388 0.400 0.374
389 0.400 0.376
390 0.402 0.376
391 0.402 0.378
392 0.402 0.380
393 0.402 0.382
394 0.404 0.382
395 0.406 0.382
396 0.406 0.384
397 0.406 0.386
398 0.408 0.386
399 0.410 0.386
400 0.410 0.388
401 0.412 0.388
402 0.414 0.388
403 0.416 0.388
404 0.416 0.390
405 0.418 0.390
406 0.418 0.392
407 0.418 0.394
408 0.418 0.396
409 0.420 0.396
410 0.422 0.396
411 0.424 0.396
412 0.426 0.396
413 0.426 0.398
414 0.426 0.400
415 0.426 0.402
416 0.426 0.404
417 0.428 0.404
418 0.430 0.404
419 0.432 0.404
420 0.432 0.406
421 0.434 0.406
422 0.436 0.406
423 0.438 0.406
424 0.440 0.406
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426 0.442 0.408
427 0.442 0.410
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429 0.442 0.414
430 0.442 0.416
431 0.442 0.418
432 0.442 0.420
433 0.442 0.422
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435 0.442 0.426
436 0.444 0.426
437 0.444 0.428
438 0.444 0.430
439 0.444 0.432
440 0.446 0.432
441 0.446 0.434
442 0.448 0.434
443 0.450 0.434
444 0.452 0.434
445 0.452 0.436
446 0.452 0.438
447 0.452 0.440
448 0.454 0.440
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450 0.456 0.442
451 0.456 0.444
452 0.456 0.446
453 0.456 0.448
454 0.458 0.448
455 0.458 0.450
456 0.458 0.452
457 0.460 0.452
458 0.460 0.454
459 0.460 0.456
460 0.460 0.458
461 0.460 0.460
462 0.460 0.462
463 0.462 0.462
464 0.462 0.464
465 0.462 0.466
466 0.464 0.466
467 0.464 0.468
468 0.464 0.470
469 0.464 0.472
470 0.466 0.472
471 0.466 0.474
472 0.468 0.474
473 0.468 0.476
474 0.470 0.476
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527 0.518 0.534
528 0.518 0.536
529 0.520 0.536
530 0.522 0.536
531 0.522 0.538
532 0.524 0.538
533 0.524 0.540
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535 0.526 0.542
536 0.528 0.542
537 0.530 0.542
538 0.530 0.544
539 0.530 0.546
540 0.532 0.546
541 0.532 0.548
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561 0.554 0.566
562 0.556 0.566
563 0.556 0.568
564 0.558 0.568
565 0.560 0.568
566 0.562 0.568
567 0.564 0.568
568 0.566 0.568
569 0.568 0.568
570 0.568 0.570
571 0.568 0.572
572 0.568 0.574
573 0.568 0.576
574 0.568 0.578
575 0.570 0.578
576 0.572 0.578
577 0.572 0.580
578 0.574 0.580
579 0.576 0.580
580 0.578 0.580
581 0.580 0.580
582 0.580 0.582
583 0.580 0.584
584 0.580 0.586
585 0.580 0.588
586 0.582 0.588
587 0.582 0.590
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590 0.588 0.590
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592 0.588 0.594
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595 0.594 0.594
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598 0.594 0.600
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600 0.598 0.600
601 0.600 0.600
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605 0.602 0.606
606 0.602 0.608
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610 0.606 0.612
611 0.608 0.612
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613 0.608 0.616
614 0.608 0.618
615 0.610 0.618
616 0.612 0.618
617 0.614 0.618
618 0.614 0.620
619 0.616 0.620
620 0.616 0.622
621 0.616 0.624
622 0.618 0.624
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624 0.622 0.624
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627 0.624 0.628
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629 0.626 0.630
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997 0.998 0.994
998 0.998 0.996
999 0.998 0.998
1000 1.000 0.998
1001 1.000 1.000
$auc
[,1]
[1,] 0.5049
$auc.CI
[1] 0.4690997 0.5407003
Used 12 of 12 pos genes, and 12 of 12 neg genes
[1] 0.8560606
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