#Due to sensitivity of national mortality data, we cannot share the individual records. We uploaded csv files containing top piars and weight to replicate our network results.
#For the sake of completeness, we first simulated a data set with 27 conditions with prevalence similar to our empirical data set. The codes applied to our data is presented in the next section.
set.seed(100)
x1<-as.factor(rbinom(n=10000,size=1,prob=0.334))
x2<-as.factor(rbinom(n=10000,size=1,prob=0.332))
x3<-as.factor(rbinom(n=10000,size=1,prob=0.267))
x4<-as.factor(rbinom(n=10000,size=1,prob=0.206))
x5<-as.factor(rbinom(n=10000,size=1,prob=0.171))
x6<-as.factor(rbinom(n=10000,size=1,prob=0.164))
x7<-as.factor(rbinom(n=10000,size=1,prob=0.151))
x8<-as.factor(rbinom(n=10000,size=1,prob=0.145))
x9<-as.factor(rbinom(n=10000,size=1,prob=0.125))
x10<-as.factor(rbinom(n=10000,size=1,prob=0.111))
x11<-as.factor(rbinom(n=10000,size=1,prob=0.110))
x12<-as.factor(rbinom(n=10000,size=1,prob=0.104))
x13<-as.factor(rbinom(n=10000,size=1,prob=0.079))
x14<-as.factor(rbinom(n=10000,size=1,prob=0.067))
x15<-as.factor(rbinom(n=10000,size=1,prob=0.059))
x16<-as.factor(rbinom(n=10000,size=1,prob=0.051))
x17<-as.factor(rbinom(n=10000,size=1,prob=0.046))
x18<-as.factor(rbinom(n=10000,size=1,prob=0.044))
x19<-as.factor(rbinom(n=10000,size=1,prob=0.033))
x20<-as.factor(rbinom(n=10000,size=1,prob=0.031))
x21<-as.factor(rbinom(n=10000,size=1,prob=0.030))
x22<-as.factor(rbinom(n=10000,size=1,prob=0.030))
x23<-as.factor(rbinom(n=10000,size=1,prob=0.022))
x24<-as.factor(rbinom(n=10000,size=1,prob=0.022))
x25<-as.factor(rbinom(n=10000,size=1,prob=0.020))
x26<-as.factor(rbinom(n=10000,size=1,prob=0.015))
x27<-as.factor(rbinom(n=10000,size=1,prob=0.015))#Creating a data frame, named dataset
dataset=data.frame(x1, x2, x3, x4, x5, x6, x7, x8, x9, x10,
x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,
x21,x22,x23,x24,x25,x26,x27)
num_sub=dim(dataset)[1] # number of subjects ()
num_sub## [1] 10000num_cond=dim(dataset)[2] # number of conditions
num_cond## [1] 27#Extracting all possible pairs
k= combn(1:num_cond, 2) # generates list of all unique pairs (2 means combinations of two or pairs)
n= dim(k)[2] # number of unique pairs
n## [1] 351library(caret)## Loading required package: ggplot2## Loading required package: latticeconfusion1<-matrix(0,n,6) #create a matrix to be populated with condition 1, condition 2 and A (number of subjects #with both conditions), B (number of subjects who only suffer condition 2), C (number of subjects who only suffer #condition 1), and D (number of subjects with neither of conditions)
for (r in 1:n){
confusion<-confusionMatrix(data=dataset[,k[1,r]], reference = dataset[,k[2,r]])$table
confusion1[r,3:6]<-c(confusion[2,2],confusion[1,2],confusion[2,1],confusion[1,1]) #ordering as A,B,C,D in a row
confusion1[r,1:2]<-c(k[1,r],k[2,r])
}
colnames(confusion1)<-c("condition1","condition2","A","B","C","D")
#make the conditions factors instead of numerics
confusion2<-as.data.frame(confusion1)
confusion2$condition1<-as.factor(confusion2$condition1)
confusion2$condition2<-as.factor(confusion2$condition2)
attach(confusion2)
confusion2## condition1 condition2 A B C D
## 1 1 2 1113 2178 2217 4492
## 2 1 3 871 1751 2459 4919
## 3 1 4 699 1367 2631 5303
## 4 1 5 564 1111 2766 5559
## 5 1 6 577 1154 2753 5516
## 6 1 7 498 932 2832 5738
## 7 1 8 499 1019 2831 5651
## 8 1 9 439 880 2891 5790
## 9 1 10 401 722 2929 5948
## 10 1 11 347 769 2983 5901
## 11 1 12 349 703 2981 5967
## 12 1 13 232 488 3098 6182
## 13 1 14 228 446 3102 6224
## 14 1 15 200 405 3130 6265
## 15 1 16 169 367 3161 6303
## 16 1 17 153 313 3177 6357
## 17 1 18 169 293 3161 6377
## 18 1 19 108 218 3222 6452
## 19 1 20 106 217 3224 6453
## 20 1 21 107 204 3223 6466
## 21 1 22 93 184 3237 6486
## 22 1 23 71 150 3259 6520
## 23 1 24 62 140 3268 6530
## 24 1 25 63 128 3267 6542
## 25 1 26 66 91 3264 6579
## 26 1 27 38 106 3292 6564
## 27 2 3 827 1795 2464 4914
## 28 2 4 670 1396 2621 5313
## 29 2 5 555 1120 2736 5589
## 30 2 6 552 1179 2739 5530
## 31 2 7 473 957 2818 5752
## 32 2 8 504 1014 2787 5695
## 33 2 9 418 901 2873 5808
## 34 2 10 357 766 2934 5943
## 35 2 11 351 765 2940 5944
## 36 2 12 339 713 2952 5996
## 37 2 13 235 485 3056 6224
## 38 2 14 219 455 3072 6254
## 39 2 15 196 409 3095 6300
## 40 2 16 180 356 3111 6353
## 41 2 17 158 308 3133 6401
## 42 2 18 139 323 3152 6386
## 43 2 19 125 201 3166 6508
## 44 2 20 95 228 3196 6481
## 45 2 21 109 202 3182 6507
## 46 2 22 85 192 3206 6517
## 47 2 23 67 154 3224 6555
## 48 2 24 63 139 3228 6570
## 49 2 25 62 129 3229 6580
## 50 2 26 51 106 3240 6603
## 51 2 27 51 93 3240 6616
## 52 3 4 567 1499 2055 5879
## 53 3 5 449 1226 2173 6152
## 54 3 6 445 1286 2177 6092
## 55 3 7 420 1010 2202 6368
## 56 3 8 394 1124 2228 6254
## 57 3 9 314 1005 2308 6373
## 58 3 10 296 827 2326 6551
## 59 3 11 285 831 2337 6547
## 60 3 12 275 777 2347 6601
## 61 3 13 194 526 2428 6852
## 62 3 14 179 495 2443 6883
## 63 3 15 168 437 2454 6941
## 64 3 16 143 393 2479 6985
## 65 3 17 123 343 2499 7035
## 66 3 18 119 343 2503 7035
## 67 3 19 78 248 2544 7130
## 68 3 20 87 236 2535 7142
## 69 3 21 77 234 2545 7144
## 70 3 22 76 201 2546 7177
## 71 3 23 75 146 2547 7232
## 72 3 24 44 158 2578 7220
## 73 3 25 45 146 2577 7232
## 74 3 26 40 117 2582 7261
## 75 3 27 39 105 2583 7273
## 76 4 5 343 1332 1723 6602
## 77 4 6 358 1373 1708 6561
## 78 4 7 288 1142 1778 6792
## 79 4 8 312 1206 1754 6728
## 80 4 9 277 1042 1789 6892
## 81 4 10 225 898 1841 7036
## 82 4 11 218 898 1848 7036
## 83 4 12 233 819 1833 7115
## 84 4 13 157 563 1909 7371
## 85 4 14 131 543 1935 7391
## 86 4 15 126 479 1940 7455
## 87 4 16 101 435 1965 7499
## 88 4 17 89 377 1977 7557
## 89 4 18 96 366 1970 7568
## 90 4 19 58 268 2008 7666
## 91 4 20 69 254 1997 7680
## 92 4 21 76 235 1990 7699
## 93 4 22 59 218 2007 7716
## 94 4 23 47 174 2019 7760
## 95 4 24 41 161 2025 7773
## 96 4 25 28 163 2038 7771
## 97 4 26 21 136 2045 7798
## 98 4 27 31 113 2035 7821
## 99 5 6 278 1453 1397 6872
## 100 5 7 252 1178 1423 7147
## 101 5 8 243 1275 1432 7050
## 102 5 9 201 1118 1474 7207
## 103 5 10 176 947 1499 7378
## 104 5 11 188 928 1487 7397
## 105 5 12 158 894 1517 7431
## 106 5 13 119 601 1556 7724
## 107 5 14 122 552 1553 7773
## 108 5 15 112 493 1563 7832
## 109 5 16 88 448 1587 7877
## 110 5 17 62 404 1613 7921
## 111 5 18 70 392 1605 7933
## 112 5 19 52 274 1623 8051
## 113 5 20 47 276 1628 8049
## 114 5 21 46 265 1629 8060
## 115 5 22 33 244 1642 8081
## 116 5 23 43 178 1632 8147
## 117 5 24 27 175 1648 8150
## 118 5 25 42 149 1633 8176
## 119 5 26 24 133 1651 8192
## 120 5 27 25 119 1650 8206
## 121 6 7 225 1205 1506 7064
## 122 6 8 238 1280 1493 6989
## 123 6 9 238 1081 1493 7188
## 124 6 10 170 953 1561 7316
## 125 6 11 197 919 1534 7350
## 126 6 12 175 877 1556 7392
## 127 6 13 127 593 1604 7676
## 128 6 14 124 550 1607 7719
## 129 6 15 101 504 1630 7765
## 130 6 16 94 442 1637 7827
## 131 6 17 79 387 1652 7882
## 132 6 18 85 377 1646 7892
## 133 6 19 62 264 1669 8005
## 134 6 20 49 274 1682 7995
## 135 6 21 53 258 1678 8011
## 136 6 22 48 229 1683 8040
## 137 6 23 39 182 1692 8087
## 138 6 24 37 165 1694 8104
## 139 6 25 30 161 1701 8108
## 140 6 26 37 120 1694 8149
## 141 6 27 25 119 1706 8150
## 142 7 8 207 1311 1223 7259
## 143 7 9 182 1137 1248 7433
## 144 7 10 186 937 1244 7633
## 145 7 11 166 950 1264 7620
## 146 7 12 161 891 1269 7679
## 147 7 13 113 607 1317 7963
## 148 7 14 88 586 1342 7984
## 149 7 15 87 518 1343 8052
## 150 7 16 65 471 1365 8099
## 151 7 17 56 410 1374 8160
## 152 7 18 64 398 1366 8172
## 153 7 19 56 270 1374 8300
## 154 7 20 40 283 1390 8287
## 155 7 21 40 271 1390 8299
## 156 7 22 40 237 1390 8333
## 157 7 23 34 187 1396 8383
## 158 7 24 21 181 1409 8389
## 159 7 25 27 164 1403 8406
## 160 7 26 15 142 1415 8428
## 161 7 27 25 119 1405 8451
## 162 8 9 184 1135 1334 7347
## 163 8 10 173 950 1345 7532
## 164 8 11 161 955 1357 7527
## 165 8 12 150 902 1368 7580
## 166 8 13 110 610 1408 7872
## 167 8 14 104 570 1414 7912
## 168 8 15 90 515 1428 7967
## 169 8 16 72 464 1446 8018
## 170 8 17 74 392 1444 8090
## 171 8 18 75 387 1443 8095
## 172 8 19 47 279 1471 8203
## 173 8 20 46 277 1472 8205
## 174 8 21 41 270 1477 8212
## 175 8 22 36 241 1482 8241
## 176 8 23 39 182 1479 8300
## 177 8 24 30 172 1488 8310
## 178 8 25 28 163 1490 8319
## 179 8 26 28 129 1490 8353
## 180 8 27 26 118 1492 8364
## 181 9 10 137 986 1182 7695
## 182 9 11 160 956 1159 7725
## 183 9 12 118 934 1201 7747
## 184 9 13 98 622 1221 8059
## 185 9 14 86 588 1233 8093
## 186 9 15 76 529 1243 8152
## 187 9 16 73 463 1246 8218
## 188 9 17 59 407 1260 8274
## 189 9 18 49 413 1270 8268
## 190 9 19 54 272 1265 8409
## 191 9 20 43 280 1276 8401
## 192 9 21 42 269 1277 8412
## 193 9 22 36 241 1283 8440
## 194 9 23 33 188 1286 8493
## 195 9 24 22 180 1297 8501
## 196 9 25 20 171 1299 8510
## 197 9 26 19 138 1300 8543
## 198 9 27 21 123 1298 8558
## 199 10 11 125 991 998 7886
## 200 10 12 104 948 1019 7929
## 201 10 13 78 642 1045 8235
## 202 10 14 71 603 1052 8274
## 203 10 15 73 532 1050 8345
## 204 10 16 50 486 1073 8391
## 205 10 17 51 415 1072 8462
## 206 10 18 48 414 1075 8463
## 207 10 19 42 284 1081 8593
## 208 10 20 41 282 1082 8595
## 209 10 21 36 275 1087 8602
## 210 10 22 29 248 1094 8629
## 211 10 23 30 191 1093 8686
## 212 10 24 25 177 1098 8700
## 213 10 25 17 174 1106 8703
## 214 10 26 20 137 1103 8740
## 215 10 27 14 130 1109 8747
## 216 11 12 121 931 995 7953
## 217 11 13 86 634 1030 8250
## 218 11 14 72 602 1044 8282
## 219 11 15 69 536 1047 8348
## 220 11 16 57 479 1059 8405
## 221 11 17 41 425 1075 8459
## 222 11 18 42 420 1074 8464
## 223 11 19 33 293 1083 8591
## 224 11 20 42 281 1074 8603
## 225 11 21 33 278 1083 8606
## 226 11 22 33 244 1083 8640
## 227 11 23 28 193 1088 8691
## 228 11 24 21 181 1095 8703
## 229 11 25 16 175 1100 8709
## 230 11 26 19 138 1097 8746
## 231 11 27 20 124 1096 8760
## 232 12 13 72 648 980 8300
## 233 12 14 73 601 979 8347
## 234 12 15 72 533 980 8415
## 235 12 16 55 481 997 8467
## 236 12 17 49 417 1003 8531
## 237 12 18 39 423 1013 8525
## 238 12 19 35 291 1017 8657
## 239 12 20 43 280 1009 8668
## 240 12 21 33 278 1019 8670
## 241 12 22 37 240 1015 8708
## 242 12 23 18 203 1034 8745
## 243 12 24 16 186 1036 8762
## 244 12 25 17 174 1035 8774
## 245 12 26 19 138 1033 8810
## 246 12 27 15 129 1037 8819
## 247 13 14 39 635 681 8645
## 248 13 15 42 563 678 8717
## 249 13 16 36 500 684 8780
## 250 13 17 31 435 689 8845
## 251 13 18 33 429 687 8851
## 252 13 19 18 308 702 8972
## 253 13 20 26 297 694 8983
## 254 13 21 28 283 692 8997
## 255 13 22 19 258 701 9022
## 256 13 23 18 203 702 9077
## 257 13 24 17 185 703 9095
## 258 13 25 16 175 704 9105
## 259 13 26 11 146 709 9134
## 260 13 27 9 135 711 9145
## 261 14 15 42 563 632 8763
## 262 14 16 39 497 635 8829
## 263 14 17 30 436 644 8890
## 264 14 18 31 431 643 8895
## 265 14 19 29 297 645 9029
## 266 14 20 23 300 651 9026
## 267 14 21 23 288 651 9038
## 268 14 22 19 258 655 9068
## 269 14 23 16 205 658 9121
## 270 14 24 9 193 665 9133
## 271 14 25 15 176 659 9150
## 272 14 26 6 151 668 9175
## 273 14 27 11 133 663 9193
## 274 15 16 38 498 567 8897
## 275 15 17 34 432 571 8963
## 276 15 18 23 439 582 8956
## 277 15 19 14 312 591 9083
## 278 15 20 15 308 590 9087
## 279 15 21 17 294 588 9101
## 280 15 22 19 258 586 9137
## 281 15 23 12 209 593 9186
## 282 15 24 14 188 591 9207
## 283 15 25 18 173 587 9222
## 284 15 26 10 147 595 9248
## 285 15 27 10 134 595 9261
## 286 16 17 28 438 508 9026
## 287 16 18 25 437 511 9027
## 288 16 19 19 307 517 9157
## 289 16 20 11 312 525 9152
## 290 16 21 19 292 517 9172
## 291 16 22 13 264 523 9200
## 292 16 23 11 210 525 9254
## 293 16 24 12 190 524 9274
## 294 16 25 11 180 525 9284
## 295 16 26 9 148 527 9316
## 296 16 27 8 136 528 9328
## 297 17 18 23 439 443 9095
## 298 17 19 7 319 459 9215
## 299 17 20 18 305 448 9229
## 300 17 21 13 298 453 9236
## 301 17 22 8 269 458 9265
## 302 17 23 11 210 455 9324
## 303 17 24 12 190 454 9344
## 304 17 25 10 181 456 9353
## 305 17 26 6 151 460 9383
## 306 17 27 3 141 463 9393
## 307 18 19 18 308 444 9230
## 308 18 20 10 313 452 9225
## 309 18 21 19 292 443 9246
## 310 18 22 13 264 449 9274
## 311 18 23 6 215 456 9323
## 312 18 24 4 198 458 9340
## 313 18 25 7 184 455 9354
## 314 18 26 8 149 454 9389
## 315 18 27 8 136 454 9402
## 316 19 20 5 318 321 9356
## 317 19 21 16 295 310 9379
## 318 19 22 14 263 312 9411
## 319 19 23 4 217 322 9457
## 320 19 24 4 198 322 9476
## 321 19 25 4 187 322 9487
## 322 19 26 6 151 320 9523
## 323 19 27 2 142 324 9532
## 324 20 21 8 303 315 9374
## 325 20 22 9 268 314 9409
## 326 20 23 10 211 313 9466
## 327 20 24 5 197 318 9480
## 328 20 25 7 184 316 9493
## 329 20 26 11 146 312 9531
## 330 20 27 2 142 321 9535
## 331 21 22 6 271 305 9418
## 332 21 23 5 216 306 9473
## 333 21 24 7 195 304 9494
## 334 21 25 5 186 306 9503
## 335 21 26 6 151 305 9538
## 336 21 27 5 139 306 9550
## 337 22 23 6 215 271 9508
## 338 22 24 4 198 273 9525
## 339 22 25 4 187 273 9536
## 340 22 26 3 154 274 9569
## 341 22 27 4 140 273 9583
## 342 23 24 2 200 219 9579
## 343 23 25 3 188 218 9591
## 344 23 26 2 155 219 9624
## 345 23 27 3 141 218 9638
## 346 24 25 6 185 196 9613
## 347 24 26 1 156 201 9642
## 348 24 27 2 142 200 9656
## 349 25 26 3 154 188 9655
## 350 25 27 1 143 190 9666
## 351 26 27 3 141 154 9702#Another method to compute values of A, B, C, and D
##ANOTHER METHOD TO EXTRACT PAIRS & A & B & C & D
disease_vars <- colnames(dataset)[1:27]
matrix <- data.frame(matrix(ncol = 6, nrow = 0))
colnames(matrix) <- c("Disease1", "Disease2", "Both", "Disease1_Only", "Disease2_Only", "Neither")
# Loop through the pairs of diseases
for (i in 1:(length(disease_vars) - 1)) {
for (j in (i + 1):length(disease_vars)) {
# Extract the pair of diseases
pair <- c(disease_vars[i], disease_vars[j])
# Count the number of subjects with both diseases
both <- sum(dataset[,pair[1]] == 1 & dataset[,pair[2]] == 1)
# Count the number of subjects with the first disease but not the second
disease1_only <- sum(dataset[,pair[1]] == 1 & dataset[,pair[2]] == 0)
# Count the number of subjects with the second disease but not the first
disease2_only <- sum(dataset[,pair[1]] == 0 & dataset[,pair[2]] == 1)
# Count the number of subjects with neither disease
neither <- sum(dataset[,pair[1]] == 0 & dataset[,pair[2]] == 0)
# Add the summary to the matrix
matrix <- rbind(matrix, data.frame(Disease1 = pair[1], Disease2 = pair[2], Both = both, Disease1_Only = disease1_only, Disease2_Only = disease2_only, Neither = neither))
}
}
print(matrix)## Disease1 Disease2 Both Disease1_Only Disease2_Only Neither
## 1 x1 x2 1113 2217 2178 4492
## 2 x1 x3 871 2459 1751 4919
## 3 x1 x4 699 2631 1367 5303
## 4 x1 x5 564 2766 1111 5559
## 5 x1 x6 577 2753 1154 5516
## 6 x1 x7 498 2832 932 5738
## 7 x1 x8 499 2831 1019 5651
## 8 x1 x9 439 2891 880 5790
## 9 x1 x10 401 2929 722 5948
## 10 x1 x11 347 2983 769 5901
## 11 x1 x12 349 2981 703 5967
## 12 x1 x13 232 3098 488 6182
## 13 x1 x14 228 3102 446 6224
## 14 x1 x15 200 3130 405 6265
## 15 x1 x16 169 3161 367 6303
## 16 x1 x17 153 3177 313 6357
## 17 x1 x18 169 3161 293 6377
## 18 x1 x19 108 3222 218 6452
## 19 x1 x20 106 3224 217 6453
## 20 x1 x21 107 3223 204 6466
## 21 x1 x22 93 3237 184 6486
## 22 x1 x23 71 3259 150 6520
## 23 x1 x24 62 3268 140 6530
## 24 x1 x25 63 3267 128 6542
## 25 x1 x26 66 3264 91 6579
## 26 x1 x27 38 3292 106 6564
## 27 x2 x3 827 2464 1795 4914
## 28 x2 x4 670 2621 1396 5313
## 29 x2 x5 555 2736 1120 5589
## 30 x2 x6 552 2739 1179 5530
## 31 x2 x7 473 2818 957 5752
## 32 x2 x8 504 2787 1014 5695
## 33 x2 x9 418 2873 901 5808
## 34 x2 x10 357 2934 766 5943
## 35 x2 x11 351 2940 765 5944
## 36 x2 x12 339 2952 713 5996
## 37 x2 x13 235 3056 485 6224
## 38 x2 x14 219 3072 455 6254
## 39 x2 x15 196 3095 409 6300
## 40 x2 x16 180 3111 356 6353
## 41 x2 x17 158 3133 308 6401
## 42 x2 x18 139 3152 323 6386
## 43 x2 x19 125 3166 201 6508
## 44 x2 x20 95 3196 228 6481
## 45 x2 x21 109 3182 202 6507
## 46 x2 x22 85 3206 192 6517
## 47 x2 x23 67 3224 154 6555
## 48 x2 x24 63 3228 139 6570
## 49 x2 x25 62 3229 129 6580
## 50 x2 x26 51 3240 106 6603
## 51 x2 x27 51 3240 93 6616
## 52 x3 x4 567 2055 1499 5879
## 53 x3 x5 449 2173 1226 6152
## 54 x3 x6 445 2177 1286 6092
## 55 x3 x7 420 2202 1010 6368
## 56 x3 x8 394 2228 1124 6254
## 57 x3 x9 314 2308 1005 6373
## 58 x3 x10 296 2326 827 6551
## 59 x3 x11 285 2337 831 6547
## 60 x3 x12 275 2347 777 6601
## 61 x3 x13 194 2428 526 6852
## 62 x3 x14 179 2443 495 6883
## 63 x3 x15 168 2454 437 6941
## 64 x3 x16 143 2479 393 6985
## 65 x3 x17 123 2499 343 7035
## 66 x3 x18 119 2503 343 7035
## 67 x3 x19 78 2544 248 7130
## 68 x3 x20 87 2535 236 7142
## 69 x3 x21 77 2545 234 7144
## 70 x3 x22 76 2546 201 7177
## 71 x3 x23 75 2547 146 7232
## 72 x3 x24 44 2578 158 7220
## 73 x3 x25 45 2577 146 7232
## 74 x3 x26 40 2582 117 7261
## 75 x3 x27 39 2583 105 7273
## 76 x4 x5 343 1723 1332 6602
## 77 x4 x6 358 1708 1373 6561
## 78 x4 x7 288 1778 1142 6792
## 79 x4 x8 312 1754 1206 6728
## 80 x4 x9 277 1789 1042 6892
## 81 x4 x10 225 1841 898 7036
## 82 x4 x11 218 1848 898 7036
## 83 x4 x12 233 1833 819 7115
## 84 x4 x13 157 1909 563 7371
## 85 x4 x14 131 1935 543 7391
## 86 x4 x15 126 1940 479 7455
## 87 x4 x16 101 1965 435 7499
## 88 x4 x17 89 1977 377 7557
## 89 x4 x18 96 1970 366 7568
## 90 x4 x19 58 2008 268 7666
## 91 x4 x20 69 1997 254 7680
## 92 x4 x21 76 1990 235 7699
## 93 x4 x22 59 2007 218 7716
## 94 x4 x23 47 2019 174 7760
## 95 x4 x24 41 2025 161 7773
## 96 x4 x25 28 2038 163 7771
## 97 x4 x26 21 2045 136 7798
## 98 x4 x27 31 2035 113 7821
## 99 x5 x6 278 1397 1453 6872
## 100 x5 x7 252 1423 1178 7147
## 101 x5 x8 243 1432 1275 7050
## 102 x5 x9 201 1474 1118 7207
## 103 x5 x10 176 1499 947 7378
## 104 x5 x11 188 1487 928 7397
## 105 x5 x12 158 1517 894 7431
## 106 x5 x13 119 1556 601 7724
## 107 x5 x14 122 1553 552 7773
## 108 x5 x15 112 1563 493 7832
## 109 x5 x16 88 1587 448 7877
## 110 x5 x17 62 1613 404 7921
## 111 x5 x18 70 1605 392 7933
## 112 x5 x19 52 1623 274 8051
## 113 x5 x20 47 1628 276 8049
## 114 x5 x21 46 1629 265 8060
## 115 x5 x22 33 1642 244 8081
## 116 x5 x23 43 1632 178 8147
## 117 x5 x24 27 1648 175 8150
## 118 x5 x25 42 1633 149 8176
## 119 x5 x26 24 1651 133 8192
## 120 x5 x27 25 1650 119 8206
## 121 x6 x7 225 1506 1205 7064
## 122 x6 x8 238 1493 1280 6989
## 123 x6 x9 238 1493 1081 7188
## 124 x6 x10 170 1561 953 7316
## 125 x6 x11 197 1534 919 7350
## 126 x6 x12 175 1556 877 7392
## 127 x6 x13 127 1604 593 7676
## 128 x6 x14 124 1607 550 7719
## 129 x6 x15 101 1630 504 7765
## 130 x6 x16 94 1637 442 7827
## 131 x6 x17 79 1652 387 7882
## 132 x6 x18 85 1646 377 7892
## 133 x6 x19 62 1669 264 8005
## 134 x6 x20 49 1682 274 7995
## 135 x6 x21 53 1678 258 8011
## 136 x6 x22 48 1683 229 8040
## 137 x6 x23 39 1692 182 8087
## 138 x6 x24 37 1694 165 8104
## 139 x6 x25 30 1701 161 8108
## 140 x6 x26 37 1694 120 8149
## 141 x6 x27 25 1706 119 8150
## 142 x7 x8 207 1223 1311 7259
## 143 x7 x9 182 1248 1137 7433
## 144 x7 x10 186 1244 937 7633
## 145 x7 x11 166 1264 950 7620
## 146 x7 x12 161 1269 891 7679
## 147 x7 x13 113 1317 607 7963
## 148 x7 x14 88 1342 586 7984
## 149 x7 x15 87 1343 518 8052
## 150 x7 x16 65 1365 471 8099
## 151 x7 x17 56 1374 410 8160
## 152 x7 x18 64 1366 398 8172
## 153 x7 x19 56 1374 270 8300
## 154 x7 x20 40 1390 283 8287
## 155 x7 x21 40 1390 271 8299
## 156 x7 x22 40 1390 237 8333
## 157 x7 x23 34 1396 187 8383
## 158 x7 x24 21 1409 181 8389
## 159 x7 x25 27 1403 164 8406
## 160 x7 x26 15 1415 142 8428
## 161 x7 x27 25 1405 119 8451
## 162 x8 x9 184 1334 1135 7347
## 163 x8 x10 173 1345 950 7532
## 164 x8 x11 161 1357 955 7527
## 165 x8 x12 150 1368 902 7580
## 166 x8 x13 110 1408 610 7872
## 167 x8 x14 104 1414 570 7912
## 168 x8 x15 90 1428 515 7967
## 169 x8 x16 72 1446 464 8018
## 170 x8 x17 74 1444 392 8090
## 171 x8 x18 75 1443 387 8095
## 172 x8 x19 47 1471 279 8203
## 173 x8 x20 46 1472 277 8205
## 174 x8 x21 41 1477 270 8212
## 175 x8 x22 36 1482 241 8241
## 176 x8 x23 39 1479 182 8300
## 177 x8 x24 30 1488 172 8310
## 178 x8 x25 28 1490 163 8319
## 179 x8 x26 28 1490 129 8353
## 180 x8 x27 26 1492 118 8364
## 181 x9 x10 137 1182 986 7695
## 182 x9 x11 160 1159 956 7725
## 183 x9 x12 118 1201 934 7747
## 184 x9 x13 98 1221 622 8059
## 185 x9 x14 86 1233 588 8093
## 186 x9 x15 76 1243 529 8152
## 187 x9 x16 73 1246 463 8218
## 188 x9 x17 59 1260 407 8274
## 189 x9 x18 49 1270 413 8268
## 190 x9 x19 54 1265 272 8409
## 191 x9 x20 43 1276 280 8401
## 192 x9 x21 42 1277 269 8412
## 193 x9 x22 36 1283 241 8440
## 194 x9 x23 33 1286 188 8493
## 195 x9 x24 22 1297 180 8501
## 196 x9 x25 20 1299 171 8510
## 197 x9 x26 19 1300 138 8543
## 198 x9 x27 21 1298 123 8558
## 199 x10 x11 125 998 991 7886
## 200 x10 x12 104 1019 948 7929
## 201 x10 x13 78 1045 642 8235
## 202 x10 x14 71 1052 603 8274
## 203 x10 x15 73 1050 532 8345
## 204 x10 x16 50 1073 486 8391
## 205 x10 x17 51 1072 415 8462
## 206 x10 x18 48 1075 414 8463
## 207 x10 x19 42 1081 284 8593
## 208 x10 x20 41 1082 282 8595
## 209 x10 x21 36 1087 275 8602
## 210 x10 x22 29 1094 248 8629
## 211 x10 x23 30 1093 191 8686
## 212 x10 x24 25 1098 177 8700
## 213 x10 x25 17 1106 174 8703
## 214 x10 x26 20 1103 137 8740
## 215 x10 x27 14 1109 130 8747
## 216 x11 x12 121 995 931 7953
## 217 x11 x13 86 1030 634 8250
## 218 x11 x14 72 1044 602 8282
## 219 x11 x15 69 1047 536 8348
## 220 x11 x16 57 1059 479 8405
## 221 x11 x17 41 1075 425 8459
## 222 x11 x18 42 1074 420 8464
## 223 x11 x19 33 1083 293 8591
## 224 x11 x20 42 1074 281 8603
## 225 x11 x21 33 1083 278 8606
## 226 x11 x22 33 1083 244 8640
## 227 x11 x23 28 1088 193 8691
## 228 x11 x24 21 1095 181 8703
## 229 x11 x25 16 1100 175 8709
## 230 x11 x26 19 1097 138 8746
## 231 x11 x27 20 1096 124 8760
## 232 x12 x13 72 980 648 8300
## 233 x12 x14 73 979 601 8347
## 234 x12 x15 72 980 533 8415
## 235 x12 x16 55 997 481 8467
## 236 x12 x17 49 1003 417 8531
## 237 x12 x18 39 1013 423 8525
## 238 x12 x19 35 1017 291 8657
## 239 x12 x20 43 1009 280 8668
## 240 x12 x21 33 1019 278 8670
## 241 x12 x22 37 1015 240 8708
## 242 x12 x23 18 1034 203 8745
## 243 x12 x24 16 1036 186 8762
## 244 x12 x25 17 1035 174 8774
## 245 x12 x26 19 1033 138 8810
## 246 x12 x27 15 1037 129 8819
## 247 x13 x14 39 681 635 8645
## 248 x13 x15 42 678 563 8717
## 249 x13 x16 36 684 500 8780
## 250 x13 x17 31 689 435 8845
## 251 x13 x18 33 687 429 8851
## 252 x13 x19 18 702 308 8972
## 253 x13 x20 26 694 297 8983
## 254 x13 x21 28 692 283 8997
## 255 x13 x22 19 701 258 9022
## 256 x13 x23 18 702 203 9077
## 257 x13 x24 17 703 185 9095
## 258 x13 x25 16 704 175 9105
## 259 x13 x26 11 709 146 9134
## 260 x13 x27 9 711 135 9145
## 261 x14 x15 42 632 563 8763
## 262 x14 x16 39 635 497 8829
## 263 x14 x17 30 644 436 8890
## 264 x14 x18 31 643 431 8895
## 265 x14 x19 29 645 297 9029
## 266 x14 x20 23 651 300 9026
## 267 x14 x21 23 651 288 9038
## 268 x14 x22 19 655 258 9068
## 269 x14 x23 16 658 205 9121
## 270 x14 x24 9 665 193 9133
## 271 x14 x25 15 659 176 9150
## 272 x14 x26 6 668 151 9175
## 273 x14 x27 11 663 133 9193
## 274 x15 x16 38 567 498 8897
## 275 x15 x17 34 571 432 8963
## 276 x15 x18 23 582 439 8956
## 277 x15 x19 14 591 312 9083
## 278 x15 x20 15 590 308 9087
## 279 x15 x21 17 588 294 9101
## 280 x15 x22 19 586 258 9137
## 281 x15 x23 12 593 209 9186
## 282 x15 x24 14 591 188 9207
## 283 x15 x25 18 587 173 9222
## 284 x15 x26 10 595 147 9248
## 285 x15 x27 10 595 134 9261
## 286 x16 x17 28 508 438 9026
## 287 x16 x18 25 511 437 9027
## 288 x16 x19 19 517 307 9157
## 289 x16 x20 11 525 312 9152
## 290 x16 x21 19 517 292 9172
## 291 x16 x22 13 523 264 9200
## 292 x16 x23 11 525 210 9254
## 293 x16 x24 12 524 190 9274
## 294 x16 x25 11 525 180 9284
## 295 x16 x26 9 527 148 9316
## 296 x16 x27 8 528 136 9328
## 297 x17 x18 23 443 439 9095
## 298 x17 x19 7 459 319 9215
## 299 x17 x20 18 448 305 9229
## 300 x17 x21 13 453 298 9236
## 301 x17 x22 8 458 269 9265
## 302 x17 x23 11 455 210 9324
## 303 x17 x24 12 454 190 9344
## 304 x17 x25 10 456 181 9353
## 305 x17 x26 6 460 151 9383
## 306 x17 x27 3 463 141 9393
## 307 x18 x19 18 444 308 9230
## 308 x18 x20 10 452 313 9225
## 309 x18 x21 19 443 292 9246
## 310 x18 x22 13 449 264 9274
## 311 x18 x23 6 456 215 9323
## 312 x18 x24 4 458 198 9340
## 313 x18 x25 7 455 184 9354
## 314 x18 x26 8 454 149 9389
## 315 x18 x27 8 454 136 9402
## 316 x19 x20 5 321 318 9356
## 317 x19 x21 16 310 295 9379
## 318 x19 x22 14 312 263 9411
## 319 x19 x23 4 322 217 9457
## 320 x19 x24 4 322 198 9476
## 321 x19 x25 4 322 187 9487
## 322 x19 x26 6 320 151 9523
## 323 x19 x27 2 324 142 9532
## 324 x20 x21 8 315 303 9374
## 325 x20 x22 9 314 268 9409
## 326 x20 x23 10 313 211 9466
## 327 x20 x24 5 318 197 9480
## 328 x20 x25 7 316 184 9493
## 329 x20 x26 11 312 146 9531
## 330 x20 x27 2 321 142 9535
## 331 x21 x22 6 305 271 9418
## 332 x21 x23 5 306 216 9473
## 333 x21 x24 7 304 195 9494
## 334 x21 x25 5 306 186 9503
## 335 x21 x26 6 305 151 9538
## 336 x21 x27 5 306 139 9550
## 337 x22 x23 6 271 215 9508
## 338 x22 x24 4 273 198 9525
## 339 x22 x25 4 273 187 9536
## 340 x22 x26 3 274 154 9569
## 341 x22 x27 4 273 140 9583
## 342 x23 x24 2 219 200 9579
## 343 x23 x25 3 218 188 9591
## 344 x23 x26 2 219 155 9624
## 345 x23 x27 3 218 141 9638
## 346 x24 x25 6 196 185 9613
## 347 x24 x26 1 201 156 9642
## 348 x24 x27 2 200 142 9656
## 349 x25 x26 3 188 154 9655
## 350 x25 x27 1 190 143 9666
## 351 x26 x27 3 154 141 9702#calculating measures of association (Odds Ratio, lift, Phi, and SCI)
confusion2$O<-A
confusion2$E<-(A+C)*(A+B)/num_sub
confusion2$OE<-confusion2$O/confusion2$E
confusion2$OR<-(A*D)/(B*C)
confusion2$lift<-A*num_sub/((A+B)*(A+C))
confusion2$phi<-sign(confusion2$OR-1)*sqrt(((B*C-A*D)^2)/((A+C)*(B+D)*(A+B)*(C+D)))
confusion2$sci<-sign(confusion2$OR-1)*A/sqrt((A+B)*(A+C))
confusion2## condition1 condition2 A B C D O E OE
## 1 1 2 1113 2178 2217 4492 1113 1095.9030 1.0156008
## 2 1 3 871 1751 2459 4919 871 873.1260 0.9975651
## 3 1 4 699 1367 2631 5303 699 687.9780 1.0160209
## 4 1 5 564 1111 2766 5559 564 557.7750 1.0111604
## 5 1 6 577 1154 2753 5516 577 576.4230 1.0010010
## 6 1 7 498 932 2832 5738 498 476.1900 1.0458010
## 7 1 8 499 1019 2831 5651 499 505.4940 0.9871532
## 8 1 9 439 880 2891 5790 439 439.2270 0.9994832
## 9 1 10 401 722 2929 5948 401 373.9590 1.0723101
## 10 1 11 347 769 2983 5901 347 371.6280 0.9337294
## 11 1 12 349 703 2981 5967 349 350.3160 0.9962434
## 12 1 13 232 488 3098 6182 232 239.7600 0.9676343
## 13 1 14 228 446 3102 6224 228 224.4420 1.0158526
## 14 1 15 200 405 3130 6265 200 201.4650 0.9927283
## 15 1 16 169 367 3161 6303 169 178.4880 0.9468424
## 16 1 17 153 313 3177 6357 153 155.1780 0.9859645
## 17 1 18 169 293 3161 6377 169 153.8460 1.0985011
## 18 1 19 108 218 3222 6452 108 108.5580 0.9948599
## 19 1 20 106 217 3224 6453 106 107.5590 0.9855056
## 20 1 21 107 204 3223 6466 107 103.5630 1.0331875
## 21 1 22 93 184 3237 6486 93 92.2410 1.0082284
## 22 1 23 71 150 3259 6520 71 73.5930 0.9647657
## 23 1 24 62 140 3268 6530 62 67.2660 0.9217138
## 24 1 25 63 128 3267 6542 63 63.6030 0.9905193
## 25 1 26 66 91 3264 6579 66 52.2810 1.2624089
## 26 1 27 38 106 3292 6564 38 47.9520 0.7924591
## 27 2 3 827 1795 2464 4914 827 862.9002 0.9583959
## 28 2 4 670 1396 2621 5313 670 679.9206 0.9854092
## 29 2 5 555 1120 2736 5589 555 551.2425 1.0068164
## 30 2 6 552 1179 2739 5530 552 569.6721 0.9689785
## 31 2 7 473 957 2818 5752 473 470.6130 1.0050721
## 32 2 8 504 1014 2787 5695 504 499.5738 1.0088600
## 33 2 9 418 901 2873 5808 418 434.0829 0.9629497
## 34 2 10 357 766 2934 5943 357 369.5793 0.9659632
## 35 2 11 351 765 2940 5944 351 367.2756 0.9556856
## 36 2 12 339 713 2952 5996 339 346.2132 0.9791654
## 37 2 13 235 485 3056 6224 235 236.9520 0.9917620
## 38 2 14 219 455 3072 6254 219 221.8134 0.9873164
## 39 2 15 196 409 3095 6300 196 199.1055 0.9844027
## 40 2 16 180 356 3111 6353 180 176.3976 1.0204220
## 41 2 17 158 308 3133 6401 158 153.3606 1.0302516
## 42 2 18 139 323 3152 6386 139 152.0442 0.9142078
## 43 2 19 125 201 3166 6508 125 107.2866 1.1651036
## 44 2 20 95 228 3196 6481 95 106.2993 0.8937030
## 45 2 21 109 202 3182 6507 109 102.3501 1.0649721
## 46 2 22 85 192 3206 6517 85 91.1607 0.9324193
## 47 2 23 67 154 3224 6555 67 72.7311 0.9212015
## 48 2 24 63 139 3228 6570 63 66.4782 0.9476791
## 49 2 25 62 129 3229 6580 62 62.8581 0.9863486
## 50 2 26 51 106 3240 6603 51 51.6687 0.9870579
## 51 2 27 51 93 3240 6616 51 47.3904 1.0761673
## 52 3 4 567 1499 2055 5879 567 541.7052 1.0466948
## 53 3 5 449 1226 2173 6152 449 439.1850 1.0223482
## 54 3 6 445 1286 2177 6092 445 453.8682 0.9804608
## 55 3 7 420 1010 2202 6368 420 374.9460 1.1201613
## 56 3 8 394 1124 2228 6254 394 398.0196 0.9899010
## 57 3 9 314 1005 2308 6373 314 345.8418 0.9079296
## 58 3 10 296 827 2326 6551 296 294.4506 1.0052620
## 59 3 11 285 831 2337 6547 285 292.6152 0.9739754
## 60 3 12 275 777 2347 6601 275 275.8344 0.9969750
## 61 3 13 194 526 2428 6852 194 188.7840 1.0276295
## 62 3 14 179 495 2443 6883 179 176.7228 1.0128857
## 63 3 15 168 437 2454 6941 168 158.6310 1.0590616
## 64 3 16 143 393 2479 6985 143 140.5392 1.0175097
## 65 3 17 123 343 2499 7035 123 122.1852 1.0066686
## 66 3 18 119 343 2503 7035 119 121.1364 0.9823637
## 67 3 19 78 248 2544 7130 78 85.4772 0.9125240
## 68 3 20 87 236 2535 7142 87 84.6906 1.0272687
## 69 3 21 77 234 2545 7144 77 81.5442 0.9442732
## 70 3 22 76 201 2546 7177 76 72.6294 1.0464082
## 71 3 23 75 146 2547 7232 75 57.9462 1.2943040
## 72 3 24 44 158 2578 7220 44 52.9644 0.8307467
## 73 3 25 45 146 2577 7232 45 50.0802 0.8985587
## 74 3 26 40 117 2582 7261 40 41.1654 0.9716898
## 75 3 27 39 105 2583 7273 39 37.7568 1.0329265
## 76 4 5 343 1332 1723 6602 343 346.0550 0.9911719
## 77 4 6 358 1373 1708 6561 358 357.6246 1.0010497
## 78 4 7 288 1142 1778 6792 288 295.4380 0.9748238
## 79 4 8 312 1206 1754 6728 312 313.6188 0.9948383
## 80 4 9 277 1042 1789 6892 277 272.5054 1.0164936
## 81 4 10 225 898 1841 7036 225 232.0118 0.9697783
## 82 4 11 218 898 1848 7036 218 230.5656 0.9455010
## 83 4 12 233 819 1833 7115 233 217.3432 1.0720372
## 84 4 13 157 563 1909 7371 157 148.7520 1.0554480
## 85 4 14 131 543 1935 7391 131 139.2484 0.9407648
## 86 4 15 126 479 1940 7455 126 124.9930 1.0080565
## 87 4 16 101 435 1965 7499 101 110.7376 0.9120660
## 88 4 17 89 377 1977 7557 89 96.2756 0.9244295
## 89 4 18 96 366 1970 7568 96 95.4492 1.0057706
## 90 4 19 58 268 2008 7666 58 67.3516 0.8611525
## 91 4 20 69 254 1997 7680 69 66.7318 1.0339898
## 92 4 21 76 235 1990 7699 76 64.2526 1.1828315
## 93 4 22 59 218 2007 7716 59 57.2282 1.0309603
## 94 4 23 47 174 2019 7760 47 45.6586 1.0293789
## 95 4 24 41 161 2025 7773 41 41.7332 0.9824313
## 96 4 25 28 163 2038 7771 28 39.4606 0.7095685
## 97 4 26 21 136 2045 7798 21 32.4362 0.6474248
## 98 4 27 31 113 2035 7821 31 29.7504 1.0420028
## 99 5 6 278 1453 1397 6872 278 289.9425 0.9588108
## 100 5 7 252 1178 1423 7147 252 239.5250 1.0520822
## 101 5 8 243 1275 1432 7050 243 254.2650 0.9556958
## 102 5 9 201 1118 1474 7207 201 220.9325 0.9097801
## 103 5 10 176 947 1499 7378 176 188.1025 0.9356601
## 104 5 11 188 928 1487 7397 188 186.9300 1.0057241
## 105 5 12 158 894 1517 7431 158 176.2100 0.8966574
## 106 5 13 119 601 1556 7724 119 120.6000 0.9867330
## 107 5 14 122 552 1553 7773 122 112.8950 1.0806502
## 108 5 15 112 493 1563 7832 112 101.3375 1.1052177
## 109 5 16 88 448 1587 7877 88 89.7800 0.9801738
## 110 5 17 62 404 1613 7921 62 78.0550 0.7943117
## 111 5 18 70 392 1605 7933 70 77.3850 0.9045681
## 112 5 19 52 274 1623 8051 52 54.6050 0.9522937
## 113 5 20 47 276 1628 8049 47 54.1025 0.8687214
## 114 5 21 46 265 1629 8060 46 52.0925 0.8830446
## 115 5 22 33 244 1642 8081 33 46.3975 0.7112452
## 116 5 23 43 178 1632 8147 43 37.0175 1.1616128
## 117 5 24 27 175 1648 8150 27 33.8350 0.7979902
## 118 5 25 42 149 1633 8176 42 31.9925 1.3128077
## 119 5 26 24 133 1651 8192 24 26.2975 0.9126343
## 120 5 27 25 119 1650 8206 25 24.1200 1.0364842
## 121 6 7 225 1205 1506 7064 225 247.5330 0.9089697
## 122 6 8 238 1280 1493 6989 238 262.7658 0.9057495
## 123 6 9 238 1081 1493 7188 238 228.3189 1.0424017
## 124 6 10 170 953 1561 7316 170 194.3913 0.8745247
## 125 6 11 197 919 1534 7350 197 193.1796 1.0197764
## 126 6 12 175 877 1556 7392 175 182.1012 0.9610041
## 127 6 13 127 593 1604 7676 127 124.6320 1.0189999
## 128 6 14 124 550 1607 7719 124 116.6694 1.0628322
## 129 6 15 101 504 1630 7765 101 104.7255 0.9644260
## 130 6 16 94 442 1637 7827 94 92.7816 1.0131319
## 131 6 17 79 387 1652 7882 79 80.6646 0.9793639
## 132 6 18 85 377 1646 7892 85 79.9722 1.0628693
## 133 6 19 62 264 1669 8005 62 56.4306 1.0986947
## 134 6 20 49 274 1682 7995 49 55.9113 0.8763881
## 135 6 21 53 258 1678 8011 53 53.8341 0.9845061
## 136 6 22 48 229 1683 8040 48 47.9487 1.0010699
## 137 6 23 39 182 1692 8087 39 38.2551 1.0194719
## 138 6 24 37 165 1694 8104 37 34.9662 1.0581647
## 139 6 25 30 161 1701 8108 30 33.0621 0.9073834
## 140 6 26 37 120 1694 8149 37 27.1767 1.3614604
## 141 6 27 25 119 1706 8150 25 24.9264 1.0029527
## 142 7 8 207 1311 1223 7259 207 217.0740 0.9535919
## 143 7 9 182 1137 1248 7433 182 188.6170 0.9649183
## 144 7 10 186 937 1244 7633 186 160.5890 1.1582362
## 145 7 11 166 950 1264 7620 166 159.5880 1.0401785
## 146 7 12 161 891 1269 7679 161 150.4360 1.0702226
## 147 7 13 113 607 1317 7963 113 102.9600 1.0975136
## 148 7 14 88 586 1342 7984 88 96.3820 0.9130336
## 149 7 15 87 518 1343 8052 87 86.5150 1.0056060
## 150 7 16 65 471 1365 8099 65 76.6480 0.8480326
## 151 7 17 56 410 1374 8160 56 66.6380 0.8403614
## 152 7 18 64 398 1366 8172 64 66.0660 0.9687282
## 153 7 19 56 270 1374 8300 56 46.6180 1.2012527
## 154 7 20 40 283 1390 8287 40 46.1890 0.8660071
## 155 7 21 40 271 1390 8299 40 44.4730 0.8994221
## 156 7 22 40 237 1390 8333 40 39.6110 1.0098205
## 157 7 23 34 187 1396 8383 34 31.6030 1.0758472
## 158 7 24 21 181 1409 8389 21 28.8860 0.7269958
## 159 7 25 27 164 1403 8406 27 27.3130 0.9885403
## 160 7 26 15 142 1415 8428 15 22.4510 0.6681217
## 161 7 27 25 119 1405 8451 25 20.5920 1.2140637
## 162 8 9 184 1135 1334 7347 184 200.2242 0.9189698
## 163 8 10 173 950 1345 7532 173 170.4714 1.0148330
## 164 8 11 161 955 1357 7527 161 169.4088 0.9503639
## 165 8 12 150 902 1368 7580 150 159.6936 0.9392988
## 166 8 13 110 610 1408 7872 110 109.2960 1.0064412
## 167 8 14 104 570 1414 7912 104 102.3132 1.0164866
## 168 8 15 90 515 1428 7967 90 91.8390 0.9799758
## 169 8 16 72 464 1446 8018 72 81.3648 0.8849035
## 170 8 17 74 392 1444 8090 74 70.7388 1.0461020
## 171 8 18 75 387 1443 8095 75 70.1316 1.0694181
## 172 8 19 47 279 1471 8203 47 49.4868 0.9497482
## 173 8 20 46 277 1472 8205 46 49.0314 0.9381743
## 174 8 21 41 270 1477 8212 41 47.2098 0.8684638
## 175 8 22 36 241 1482 8241 36 42.0486 0.8561522
## 176 8 23 39 182 1479 8300 39 33.5478 1.1625203
## 177 8 24 30 172 1488 8310 30 30.6636 0.9783587
## 178 8 25 28 163 1490 8319 28 28.9938 0.9657237
## 179 8 26 28 129 1490 8353 28 23.8326 1.1748613
## 180 8 27 26 118 1492 8364 26 21.8592 1.1894305
## 181 9 10 137 986 1182 7695 137 148.1237 0.9249026
## 182 9 11 160 956 1159 7725 160 147.2004 1.0869536
## 183 9 12 118 934 1201 7747 118 138.7588 0.8503965
## 184 9 13 98 622 1221 8059 98 94.9680 1.0319265
## 185 9 14 86 588 1233 8093 86 88.9006 0.9673725
## 186 9 15 76 529 1243 8152 76 79.7995 0.9523869
## 187 9 16 73 463 1246 8218 73 70.6984 1.0325552
## 188 9 17 59 407 1260 8274 59 61.4654 0.9598896
## 189 9 18 49 413 1270 8268 49 60.9378 0.8040986
## 190 9 19 54 272 1265 8409 54 42.9994 1.2558315
## 191 9 20 43 280 1276 8401 43 42.6037 1.0093020
## 192 9 21 42 269 1277 8412 42 41.0209 1.0238683
## 193 9 22 36 241 1283 8440 36 36.5363 0.9853214
## 194 9 23 33 188 1286 8493 33 29.1499 1.1320794
## 195 9 24 22 180 1297 8501 22 26.6438 0.8257080
## 196 9 25 20 171 1299 8510 20 25.1929 0.7938745
## 197 9 26 19 138 1300 8543 19 20.7083 0.9175065
## 198 9 27 21 123 1298 8558 21 18.9936 1.1056356
## 199 10 11 125 991 998 7886 125 125.3268 0.9973924
## 200 10 12 104 948 1019 7929 104 118.1396 0.8803145
## 201 10 13 78 642 1045 8235 78 80.8560 0.9646779
## 202 10 14 71 603 1052 8274 71 75.6902 0.9380343
## 203 10 15 73 532 1050 8345 73 67.9415 1.0744538
## 204 10 16 50 486 1073 8391 50 60.1928 0.8306641
## 205 10 17 51 415 1072 8462 51 52.3318 0.9745508
## 206 10 18 48 414 1075 8463 48 51.8826 0.9251657
## 207 10 19 42 284 1081 8593 42 36.6098 1.1472338
## 208 10 20 41 282 1082 8595 41 36.2729 1.1303204
## 209 10 21 36 275 1087 8602 36 34.9253 1.0307714
## 210 10 22 29 248 1094 8629 29 31.1071 0.9322631
## 211 10 23 30 191 1093 8686 30 24.8183 1.2087855
## 212 10 24 25 177 1098 8700 25 22.6846 1.1020692
## 213 10 25 17 174 1106 8703 17 21.4493 0.7925667
## 214 10 26 20 137 1103 8740 20 17.6311 1.1343592
## 215 10 27 14 130 1109 8747 14 16.1712 0.8657366
## 216 11 12 121 931 995 7953 121 117.4032 1.0306363
## 217 11 13 86 634 1030 8250 86 80.3520 1.0702907
## 218 11 14 72 602 1044 8282 72 75.2184 0.9572126
## 219 11 15 69 536 1047 8348 69 67.5180 1.0219497
## 220 11 16 57 479 1059 8405 57 59.8176 0.9528968
## 221 11 17 41 425 1075 8459 41 52.0056 0.7883766
## 222 11 18 42 420 1074 8464 42 51.5592 0.8145976
## 223 11 19 33 293 1083 8591 33 36.3816 0.9070519
## 224 11 20 42 281 1074 8603 42 36.0468 1.1651520
## 225 11 21 33 278 1083 8606 33 34.7076 0.9508004
## 226 11 22 33 244 1083 8640 33 30.9132 1.0675051
## 227 11 23 28 193 1088 8691 28 24.6636 1.1352763
## 228 11 24 21 181 1095 8703 21 22.5432 0.9315448
## 229 11 25 16 175 1100 8709 16 21.3156 0.7506240
## 230 11 26 19 138 1097 8746 19 17.5212 1.0844006
## 231 11 27 20 124 1096 8760 20 16.0704 1.2445241
## 232 12 13 72 648 980 8300 72 75.7440 0.9505703
## 233 12 14 73 601 979 8347 73 70.9048 1.0295495
## 234 12 15 72 533 980 8415 72 63.6460 1.1312573
## 235 12 16 55 481 997 8467 55 56.3872 0.9753987
## 236 12 17 49 417 1003 8531 49 49.0232 0.9995268
## 237 12 18 39 423 1013 8525 39 48.6024 0.8024295
## 238 12 19 35 291 1017 8657 35 34.2952 1.0205510
## 239 12 20 43 280 1009 8668 43 33.9796 1.2654652
## 240 12 21 33 278 1019 8670 33 32.7172 1.0086438
## 241 12 22 37 240 1015 8708 37 29.1404 1.2697149
## 242 12 23 18 203 1034 8745 18 23.2492 0.7742202
## 243 12 24 16 186 1036 8762 16 21.2504 0.7529270
## 244 12 25 17 174 1035 8774 17 20.0932 0.8460574
## 245 12 26 19 138 1033 8810 19 16.5164 1.1503718
## 246 12 27 15 129 1037 8819 15 15.1488 0.9901774
## 247 13 14 39 635 681 8645 39 48.5280 0.8036597
## 248 13 15 42 563 678 8717 42 43.5600 0.9641873
## 249 13 16 36 500 684 8780 36 38.5920 0.9328358
## 250 13 17 31 435 689 8845 31 33.5520 0.9239390
## 251 13 18 33 429 687 8851 33 33.2640 0.9920635
## 252 13 19 18 308 702 8972 18 23.4720 0.7668712
## 253 13 20 26 297 694 8983 26 23.2560 1.1179911
## 254 13 21 28 283 692 8997 28 22.3920 1.2504466
## 255 13 22 19 258 701 9022 19 19.9440 0.9526675
## 256 13 23 18 203 702 9077 18 15.9120 1.1312217
## 257 13 24 17 185 703 9095 17 14.5440 1.1688669
## 258 13 25 16 175 704 9105 16 13.7520 1.1634671
## 259 13 26 11 146 709 9134 11 11.3040 0.9731069
## 260 13 27 9 135 711 9145 9 10.3680 0.8680556
## 261 14 15 42 563 632 8763 42 40.7770 1.0299924
## 262 14 16 39 497 635 8829 39 36.1264 1.0795429
## 263 14 17 30 436 644 8890 30 31.4084 0.9551585
## 264 14 18 31 431 643 8895 31 31.1388 0.9955425
## 265 14 19 29 297 645 9029 29 21.9724 1.3198376
## 266 14 20 23 300 651 9026 23 21.7702 1.0564901
## 267 14 21 23 288 651 9038 23 20.9614 1.0972550
## 268 14 22 19 258 655 9068 19 18.6698 1.0176863
## 269 14 23 16 205 658 9121 16 14.8954 1.0741571
## 270 14 24 9 193 665 9133 9 13.6148 0.6610453
## 271 14 25 15 176 659 9150 15 12.8734 1.1651933
## 272 14 26 6 151 668 9175 6 10.5818 0.5670113
## 273 14 27 11 133 663 9193 11 9.7056 1.1333663
## 274 15 16 38 498 567 8897 38 32.4280 1.1718268
## 275 15 17 34 432 571 8963 34 28.1930 1.2059731
## 276 15 18 23 439 582 8956 23 27.9510 0.8228686
## 277 15 19 14 312 591 9083 14 19.7230 0.7098312
## 278 15 20 15 308 590 9087 15 19.5415 0.7675972
## 279 15 21 17 294 588 9101 17 18.8155 0.9035104
## 280 15 22 19 258 586 9137 19 16.7585 1.1337530
## 281 15 23 12 209 593 9186 12 13.3705 0.8974982
## 282 15 24 14 188 591 9207 14 12.2210 1.1455691
## 283 15 25 18 173 587 9222 18 11.5555 1.5576998
## 284 15 26 10 147 595 9248 10 9.4985 1.0527978
## 285 15 27 10 134 595 9261 10 8.7120 1.1478421
## 286 16 17 28 438 508 9026 28 24.9776 1.1210044
## 287 16 18 25 437 511 9027 25 24.7632 1.0095626
## 288 16 19 19 307 517 9157 19 17.4736 1.0873546
## 289 16 20 11 312 525 9152 11 17.3128 0.6353681
## 290 16 21 19 292 517 9172 19 16.6696 1.1397994
## 291 16 22 13 264 523 9200 13 14.8472 0.8755860
## 292 16 23 11 210 525 9254 11 11.8456 0.9286148
## 293 16 24 12 190 524 9274 12 10.8272 1.1083198
## 294 16 25 11 180 525 9284 11 10.2376 1.0744706
## 295 16 26 9 148 527 9316 9 8.4152 1.0694933
## 296 16 27 8 136 528 9328 8 7.7184 1.0364842
## 297 17 18 23 439 443 9095 23 21.5292 1.0683165
## 298 17 19 7 319 459 9215 7 15.1916 0.4607810
## 299 17 20 18 305 448 9229 18 15.0518 1.1958703
## 300 17 21 13 298 453 9236 13 14.4926 0.8970095
## 301 17 22 8 269 458 9265 8 12.9082 0.6197611
## 302 17 23 11 210 455 9324 11 10.2986 1.0681063
## 303 17 24 12 190 454 9344 12 9.4132 1.2748056
## 304 17 25 10 181 456 9353 10 8.9006 1.1235198
## 305 17 26 6 151 460 9383 6 7.3162 0.8200979
## 306 17 27 3 141 463 9393 3 6.7104 0.4470672
## 307 18 19 18 308 444 9230 18 15.0612 1.1951239
## 308 18 20 10 313 452 9225 10 14.9226 0.6701245
## 309 18 21 19 292 443 9246 19 14.3682 1.3223647
## 310 18 22 13 264 449 9274 13 12.7974 1.0158313
## 311 18 23 6 215 456 9323 6 10.2102 0.5876476
## 312 18 24 4 198 458 9340 4 9.3324 0.4286143
## 313 18 25 7 184 455 9354 7 8.8242 0.7932730
## 314 18 26 8 149 454 9389 8 7.2534 1.1029310
## 315 18 27 8 136 454 9402 8 6.6528 1.2025012
## 316 19 20 5 318 321 9356 5 10.5298 0.4748428
## 317 19 21 16 295 310 9379 16 10.1386 1.5781272
## 318 19 22 14 263 312 9411 14 9.0302 1.5503533
## 319 19 23 4 217 322 9457 4 7.2046 0.5552008
## 320 19 24 4 198 322 9476 4 6.5852 0.6074227
## 321 19 25 4 187 322 9487 4 6.2266 0.6424052
## 322 19 26 6 151 320 9523 6 5.1182 1.1722871
## 323 19 27 2 142 324 9532 2 4.6944 0.4260395
## 324 20 21 8 303 315 9374 8 10.0453 0.7963923
## 325 20 22 9 268 314 9409 9 8.9471 1.0059125
## 326 20 23 10 211 313 9466 10 7.1383 1.4008938
## 327 20 24 5 197 318 9480 5 6.5246 0.7663305
## 328 20 25 7 184 316 9493 7 6.1693 1.1346506
## 329 20 26 11 146 312 9531 11 5.0711 2.1691546
## 330 20 27 2 142 321 9535 2 4.6512 0.4299966
## 331 21 22 6 271 305 9418 6 8.6147 0.6964839
## 332 21 23 5 216 306 9473 5 6.8731 0.7274738
## 333 21 24 7 195 304 9494 7 6.2822 1.1142593
## 334 21 25 5 186 306 9503 5 5.9401 0.8417367
## 335 21 26 6 151 305 9538 6 4.8827 1.2288283
## 336 21 27 5 139 306 9550 5 4.4784 1.1164702
## 337 22 23 6 215 271 9508 6 6.1217 0.9801199
## 338 22 24 4 198 273 9525 4 5.5954 0.7148729
## 339 22 25 4 187 273 9536 4 5.2907 0.7560436
## 340 22 26 3 154 274 9569 3 4.3489 0.6898296
## 341 22 27 4 140 273 9583 4 3.9888 1.0028079
## 342 23 24 2 200 219 9579 2 4.4642 0.4480086
## 343 23 25 3 188 218 9591 3 4.2211 0.7107152
## 344 23 26 2 155 219 9624 2 3.4697 0.5764187
## 345 23 27 3 141 218 9638 3 3.1824 0.9426848
## 346 24 25 6 185 196 9613 6 3.8582 1.5551293
## 347 24 26 1 156 201 9642 1 3.1714 0.3153182
## 348 24 27 2 142 200 9656 2 2.9088 0.6875688
## 349 25 26 3 154 188 9655 3 2.9987 1.0004335
## 350 25 27 1 143 190 9666 1 2.7504 0.3635835
## 351 26 27 3 141 154 9702 3 2.2608 1.3269639
## OR lift phi sci
## 1 1.0354076 1.0156008 7.720440e-03 0.336208823
## 2 0.9950624 0.9975651 -1.025634e-03 -0.294767565
## 3 1.0306458 1.0160209 5.776491e-03 0.266495513
## 4 1.0202569 1.0111604 3.537161e-03 0.238808390
## 5 1.0018162 1.0010010 3.236054e-04 0.240328437
## 6 1.0826317 1.0458010 1.321941e-02 0.228212384
## 7 0.9774888 0.9871532 -3.840097e-03 -0.221943558
## 8 0.9991077 0.9994832 -1.423422e-04 -0.209469119
## 9 1.1278693 1.0723101 1.817250e-02 0.207363530
## 10 0.8926383 0.9337294 -1.659616e-02 -0.180001142
## 11 0.9937203 0.9962434 -9.101228e-04 -0.186464191
## 12 0.9486713 0.9676343 -6.369956e-03 -0.149830290
## 13 1.0257175 1.0158526 3.011225e-03 0.152188831
## 14 0.9884432 0.9927283 -1.303847e-03 -0.140906229
## 15 0.9182130 0.9468424 -8.938608e-03 -0.126497573
## 16 0.9780974 0.9859645 -2.192514e-03 -0.122822054
## 17 1.1636195 1.0985011 1.531767e-02 0.136252224
## 18 0.9920558 0.9948599 -6.667105e-04 -0.103655616
## 19 0.9777161 0.9855056 -1.871067e-03 -0.102207435
## 20 1.0522744 1.0331875 4.201214e-03 0.105143267
## 21 1.0127433 1.0082284 9.813337e-04 0.096832456
## 22 0.9469571 0.9647657 -3.742606e-03 -0.082763738
## 23 0.8849012 0.9217138 -7.942394e-03 -0.075595142
## 24 0.9855802 0.9905193 -9.347667e-04 -0.078995390
## 25 1.4618819 1.2624089 2.341660e-02 0.091279235
## 26 0.7148032 0.7924591 -1.772531e-02 -0.054875720
## 27 0.9188307 0.9583959 -1.737074e-02 -0.281530353
## 28 0.9728865 0.9854092 -5.214754e-03 -0.256948273
## 29 1.0122621 1.0068164 2.141444e-03 0.236385937
## 30 0.9452754 0.9689785 -9.940776e-03 -0.231273889
## 31 1.0088511 1.0050721 1.451113e-03 0.218036489
## 32 1.0156623 1.0088600 2.625144e-03 0.225491777
## 33 0.9378696 0.9629497 -1.011496e-02 -0.200627260
## 34 0.9440284 0.9659632 -8.478922e-03 -0.185701066
## 35 0.9276351 0.9556856 -1.100038e-02 -0.183151752
## 36 0.9657294 0.9791654 -5.003389e-03 -0.182191406
## 37 0.9868300 0.9917620 -1.607114e-03 -0.152664364
## 38 0.9798721 0.9873164 -2.388147e-03 -0.147044988
## 39 0.9754672 0.9844027 -2.772124e-03 -0.138903901
## 40 1.0325268 1.0204220 3.403920e-03 0.135527107
## 41 1.0480785 1.0302516 4.684233e-03 0.127585167
## 42 0.8718765 0.9142078 -1.322437e-02 -0.112727499
## 43 1.2783524 1.1651036 2.122742e-02 0.120680548
## 44 0.8449364 0.8937030 -1.360151e-02 -0.092142163
## 45 1.1034579 1.0649721 8.152721e-03 0.107741337
## 46 0.8999158 0.9324193 -7.989088e-03 -0.089025639
## 47 0.8845691 0.9212015 -8.296631e-03 -0.078562397
## 48 0.9224813 0.9476791 -5.261594e-03 -0.077268223
## 49 0.9793994 0.9863486 -1.334185e-03 -0.078200776
## 50 0.9805294 0.9870579 -1.144788e-03 -0.070950655
## 51 1.1197929 1.0761673 6.448143e-03 0.074084097
## 52 1.0821141 1.0466948 1.420477e-02 0.243613615
## 53 1.0368417 1.0223482 5.975925e-03 0.214250869
## 54 0.9683236 0.9804608 -5.329357e-03 -0.208879170
## 55 1.2025791 1.1201613 2.926101e-02 0.216902685
## 56 0.9839490 0.9899010 -2.546901e-03 -0.197489492
## 57 0.8627236 0.9079296 -2.139462e-02 -0.168846051
## 58 1.0080547 1.0052620 1.115719e-03 0.172498566
## 59 0.9607878 0.9739754 -5.498690e-03 -0.166608218
## 60 0.9954245 0.9969750 -6.183264e-04 -0.165580230
## 61 1.0408416 1.0276295 4.587876e-03 0.141194942
## 62 1.0188310 1.0128857 2.065085e-03 0.134650118
## 63 1.0873650 1.0590616 8.934731e-03 0.133387536
## 64 1.0252585 1.0175097 2.484110e-03 0.120624992
## 65 1.0095058 1.0066686 8.788906e-04 0.111274540
## 66 0.9751156 0.9823637 -2.313914e-03 -0.108120894
## 67 0.8814858 0.9125240 -9.572855e-03 -0.084366389
## 68 1.0386019 1.0272687 2.969900e-03 0.094536963
## 69 0.9236949 0.9442732 -5.951852e-03 -0.085269592
## 70 1.0658647 1.0464082 4.669620e-03 0.089177925
## 71 1.4586056 1.2943040 2.637498e-02 0.098525531
## 72 0.7799197 0.8307467 -1.448742e-02 -0.060458957
## 73 0.8649752 0.8985587 -8.438516e-03 -0.063588633
## 74 0.9614226 0.9716898 -2.131453e-03 -0.062343879
## 75 1.0458382 1.0329265 2.372596e-03 0.063469784
## 76 0.9866887 0.9911719 -2.020693e-03 -0.184383288
## 77 1.0016008 1.0010497 2.450801e-04 0.189308160
## 78 0.9633682 0.9748238 -5.247911e-03 -0.167555740
## 79 0.9923473 0.9948383 -1.114287e-03 -0.176178760
## 80 1.0241109 1.0164936 3.280745e-03 0.167800099
## 81 0.9575870 0.9697783 -5.485235e-03 -0.147715980
## 82 0.9242810 0.9455010 -9.856792e-03 -0.143568525
## 83 1.1042934 1.0720372 1.260436e-02 0.158045776
## 84 1.0767422 1.0554480 7.881287e-03 0.128726584
## 85 0.9214965 0.9407648 -8.126075e-03 -0.111013601
## 86 1.0108366 1.0080565 1.043259e-03 0.112700982
## 87 0.8860800 0.9120660 -1.067876e-02 -0.095978470
## 88 0.9023840 0.9244295 -8.525641e-03 -0.090705138
## 89 1.0076392 1.0057706 6.480866e-04 0.098261884
## 90 0.8262250 0.8611525 -1.300657e-02 -0.070673083
## 91 1.0447167 1.0339898 3.168826e-03 0.084466144
## 92 1.2512007 1.1828315 1.671517e-02 0.094813077
## 93 1.0404959 1.0309603 2.666634e-03 0.077991445
## 94 1.0381832 1.0293789 2.253739e-03 0.069556315
## 95 0.9775109 0.9824313 -1.287262e-03 -0.063466276
## 96 0.6550028 0.7095685 -2.068079e-02 -0.044573444
## 97 0.5888034 0.6474248 -2.272254e-02 -0.036872647
## 98 1.0543411 1.0420028 2.590766e-03 0.056834925
## 99 0.9411653 0.9588108 -8.453191e-03 -0.163263407
## 100 1.0744201 1.0520822 9.542949e-03 0.162826511
## 101 0.9383010 0.9556958 -8.407108e-03 -0.152392285
## 102 0.8790454 0.9097801 -1.577448e-02 -0.135227884
## 103 0.9147443 0.9356601 -1.026485e-02 -0.128326215
## 104 1.0077540 1.0057241 9.100136e-04 0.137504954
## 105 0.8657276 0.8966574 -1.589423e-02 -0.119025992
## 106 0.9828906 0.9867330 -1.657602e-03 -0.108361076
## 107 1.1062110 1.0806502 9.725295e-03 0.114821305
## 108 1.1383736 1.1052177 1.197660e-02 0.111258431
## 109 0.9749640 0.9801738 -2.116414e-03 -0.092873726
## 110 0.7536262 0.7943117 -2.039766e-02 -0.070176439
## 111 0.8826213 0.9045681 -9.421094e-03 -0.079573717
## 112 0.9414214 0.9522937 -3.928225e-03 -0.070369933
## 113 0.8419306 0.8687214 -1.075821e-02 -0.063898283
## 114 0.8588670 0.8830446 -9.398885e-03 -0.063733861
## 115 0.6656042 0.7112452 -2.186171e-02 -0.048446973
## 116 1.2059409 1.1616128 1.089782e-02 0.070674853
## 117 0.7630028 0.7979902 -1.301052e-02 -0.046417385
## 118 1.4112947 1.3128077 1.957929e-02 0.074254914
## 119 0.8953699 0.9126343 -4.949285e-03 -0.046800879
## 120 1.0448179 1.0364842 1.978115e-03 0.050903935
## 121 0.8758328 0.9089697 -1.701318e-02 -0.143009855
## 122 0.8704067 0.9057495 -1.824283e-02 -0.146822474
## 123 1.0599845 1.0424017 7.562100e-03 0.157509236
## 124 0.8360395 0.8745247 -2.041912e-02 -0.121929982
## 125 1.0270999 1.0197764 3.206989e-03 0.141737770
## 126 0.9479618 0.9610041 -6.117667e-03 -0.129682581
## 127 1.0248956 1.0189999 2.421399e-03 0.113759831
## 128 1.0829394 1.0628322 7.728351e-03 0.114800348
## 129 0.9546511 0.9644260 -4.130318e-03 -0.098695000
## 130 1.0168391 1.0131319 1.429865e-03 0.097588114
## 131 0.9739631 0.9793639 -2.087394e-03 -0.087960077
## 132 1.0810227 1.0628693 6.330723e-03 0.095049405
## 133 1.1264003 1.0986947 8.289370e-03 0.082534278
## 134 0.8500373 0.8763881 -1.033268e-02 -0.065530923
## 135 0.9807333 0.9845061 -1.270058e-03 -0.072234911
## 136 1.0013311 1.0010699 8.262326e-05 0.069319085
## 137 1.0241895 1.0194719 1.339305e-03 0.063055059
## 138 1.0727630 1.0581647 3.821104e-03 0.062571635
## 139 0.8881878 0.9073834 -5.913101e-03 -0.052174229
## 140 1.4832399 1.3614604 2.088665e-02 0.070974667
## 141 1.0036254 1.0029527 1.632944e-04 0.050073763
## 142 0.9371692 0.9535919 -8.019707e-03 -0.140496803
## 143 0.9533678 0.9649183 -5.585929e-03 -0.132519861
## 144 1.2180027 1.1582362 2.299007e-02 0.146776000
## 145 1.0533977 1.0401785 5.816995e-03 0.131403814
## 146 1.0934306 1.0702226 9.835560e-03 0.131265316
## 147 1.1255912 1.0975136 1.109520e-02 0.111363835
## 148 0.8934146 0.9130336 -9.550180e-03 -0.089636462
## 149 1.0069717 1.0056060 5.811085e-04 0.093534870
## 150 0.8188252 0.8480326 -1.477315e-02 -0.074244270
## 151 0.8111620 0.8403614 -1.441687e-02 -0.068600463
## 152 0.9619989 0.9687282 -2.811397e-03 -0.078739194
## 153 1.2528977 1.2012527 1.509125e-02 0.082018384
## 154 0.8426672 0.8660071 -9.999779e-03 -0.058855996
## 155 0.8812551 0.8994221 -7.360728e-03 -0.059980734
## 156 1.0118083 1.0098205 6.770974e-04 0.063555346
## 157 1.0918208 1.0758472 4.657644e-03 0.060480415
## 158 0.6907803 0.7269958 -1.601231e-02 -0.039072895
## 159 0.9863967 0.9885403 -6.532161e-04 -0.051662933
## 160 0.6291743 0.6681217 -1.712153e-02 -0.031657267
## 161 1.2636442 1.2140637 1.056943e-02 0.055092280
## 162 0.8928452 0.9189698 -1.336199e-02 -0.130034784
## 163 1.0197895 1.0148330 2.231887e-03 0.132501361
## 164 0.9351140 0.9503639 -7.442394e-03 -0.123696637
## 165 0.9214416 0.9392988 -8.804997e-03 -0.118699121
## 166 1.0081967 1.0064412 7.590098e-04 0.105218123
## 167 1.0209286 1.0164866 1.874998e-03 0.102817610
## 168 0.9749939 0.9799758 -2.149664e-03 -0.093913697
## 169 0.8604235 0.8849035 -1.158761e-02 -0.079820458
## 170 1.0576135 1.0461020 4.311833e-03 0.087983833
## 171 1.0871784 1.0694181 6.463259e-03 0.089558001
## 172 0.9394068 0.9497482 -3.902510e-03 -0.066811800
## 173 0.9256543 0.9381743 -4.778445e-03 -0.065693240
## 174 0.8442840 0.8684638 -9.969490e-03 -0.059671613
## 175 0.8306483 0.8561522 -1.027140e-02 -0.055517095
## 176 1.2025500 1.1625203 1.033578e-02 0.067333716
## 177 0.9740716 0.9783587 -1.314550e-03 -0.054176343
## 178 0.9590810 0.9657237 -2.023415e-03 -0.052000254
## 179 1.2168149 1.1748613 9.342575e-03 0.057355137
## 180 1.2351979 1.1894305 9.686517e-03 0.055610425
## 181 0.9045546 0.9249026 -1.041164e-02 -0.112566274
## 182 1.1155194 1.0869536 1.201304e-02 0.131875916
## 183 0.8149401 0.8503965 -1.999512e-02 -0.100173244
## 184 1.0399230 1.0319265 3.466424e-03 0.100562817
## 185 0.9599919 0.9673725 -3.419030e-03 -0.091210766
## 186 0.9422171 0.9523869 -4.709698e-03 -0.085077262
## 187 1.0398961 1.0325552 3.019971e-03 0.086819657
## 188 0.9519247 0.9598896 -3.456604e-03 -0.075255224
## 189 0.7724009 0.8040986 -1.680612e-02 -0.062770082
## 190 1.3197105 1.2558315 1.830616e-02 0.082349803
## 191 1.0110921 1.0093020 6.624379e-04 0.065878666
## 192 1.0285026 1.0238683 1.666864e-03 0.065576268
## 193 0.9826554 0.9853214 -9.657406e-04 -0.059558016
## 194 1.1592477 1.1320794 7.739651e-03 0.061121697
## 195 0.8010880 0.8257080 -9.754878e-03 -0.042621095
## 196 0.7662214 0.7938745 -1.121176e-02 -0.039846567
## 197 0.9047770 0.9175065 -4.061096e-03 -0.041752394
## 198 1.1256718 1.1056356 4.977126e-03 0.048185420
## 199 0.9966957 0.9973924 -3.287174e-04 -0.111657535
## 200 0.8536291 0.8803145 -1.459632e-02 -0.095683178
## 201 0.9574297 0.9646779 -3.499411e-03 -0.086743807
## 202 0.9260636 0.9380343 -5.925032e-03 -0.081609088
## 203 1.0905567 1.0744538 6.720053e-03 0.088563607
## 204 0.8045402 0.8306641 -1.433345e-02 -0.064446262
## 205 0.9700638 0.9745508 -2.001177e-03 -0.070499711
## 206 0.9127604 0.9251657 -5.858009e-03 -0.066639292
## 207 1.1755743 1.1472338 9.613246e-03 0.069414566
## 208 1.1549239 1.1303204 8.468377e-03 0.068075794
## 209 1.0359522 1.0307714 1.960851e-03 0.060916147
## 210 0.9223366 0.9322631 -4.066513e-03 -0.051995796
## 211 1.2482097 1.2087855 1.116364e-02 0.060219236
## 212 1.1191380 1.1020692 5.212651e-03 0.052489743
## 213 0.7688003 0.7925667 -1.029532e-02 -0.036706448
## 214 1.1567656 1.1343592 6.035457e-03 0.047631065
## 215 0.8494000 0.8657366 -5.772252e-03 -0.034814239
## 216 1.0388279 1.0306363 3.723137e-03 0.111672285
## 217 1.0864905 1.0702907 6.939339e-03 0.095940087
## 218 0.9487914 0.9572126 -4.076862e-03 -0.083017653
## 219 1.0264081 1.0219497 1.974175e-03 0.083972930
## 220 0.9444546 0.9528968 -3.973042e-03 -0.073698791
## 221 0.7591114 0.7883766 -1.658238e-02 -0.056853709
## 222 0.7880819 0.8145976 -1.446224e-02 -0.058491964
## 223 0.8934322 0.9070519 -6.047474e-03 -0.054710797
## 224 1.1972604 1.1651520 1.069406e-02 0.069954544
## 225 0.9432830 0.9508004 -3.124137e-03 -0.056014653
## 226 1.0789701 1.0675051 4.038354e-03 0.059352902
## 227 1.1588883 1.1352763 7.207728e-03 0.056380613
## 228 0.9221373 0.9315448 -3.483706e-03 -0.044229447
## 229 0.7238649 0.7506240 -1.233352e-02 -0.034655423
## 230 1.0976841 1.0844006 3.777979e-03 0.045391201
## 231 1.2891453 1.2445241 1.047564e-02 0.049890362
## 232 0.9410431 0.9505703 -4.720899e-03 -0.082729115
## 233 1.0356097 1.0295495 2.723811e-03 0.086693202
## 234 1.1599341 1.1312573 1.142083e-02 0.090249944
## 235 0.9710733 0.9753987 -2.007467e-03 -0.073244063
## 236 0.9994453 0.9995268 -3.587456e-05 -0.069983434
## 237 0.7759061 0.8024295 -1.490937e-02 -0.055941712
## 238 1.0238151 1.0205510 1.293550e-03 0.059765612
## 239 1.3192836 1.2654652 1.662966e-02 0.073766525
## 240 1.0099830 1.0086438 5.309931e-04 0.057693366
## 241 1.3226437 1.2697149 1.560952e-02 0.068541558
## 242 0.7499214 0.7742202 -1.163801e-02 -0.037330903
## 243 0.7275294 0.7529270 -1.216402e-02 -0.034708547
## 244 0.8282414 0.8460574 -7.365599e-03 -0.037924893
## 245 1.1742217 1.1503718 6.511743e-03 0.046751538
## 246 0.9888767 0.9901774 -4.070994e-04 -0.038539151
## 247 0.7796663 0.8036597 -1.470225e-02 -0.055984578
## 248 0.9591317 0.9641873 -2.531385e-03 -0.063636364
## 249 0.9242105 0.9328358 -4.452203e-03 -0.057950056
## 250 0.9148524 0.9239390 -4.683925e-03 -0.053518322
## 251 0.9910424 0.9920635 -4.865350e-04 -0.057217214
## 252 0.7469197 0.7668712 -1.192049e-02 -0.037153305
## 253 1.1331276 1.1179911 6.004436e-03 0.053914532
## 254 1.2863621 1.2504466 1.249822e-02 0.059171365
## 255 0.9478044 0.9526675 -2.225316e-03 -0.042544896
## 256 1.1465201 1.1312217 5.494737e-03 0.045124263
## 257 1.1888432 1.1688669 6.753730e-03 0.044576605
## 258 1.1824675 1.1634671 6.353705e-03 0.043145653
## 259 0.9706320 0.9731069 -9.460624e-04 -0.032717236
## 260 0.8574777 0.8680556 -4.442365e-03 -0.027950850
## 261 1.0343717 1.0299924 2.046081e-03 0.065772092
## 262 1.0910534 1.0795429 5.088958e-03 0.064886189
## 263 0.9498404 0.9551585 -2.665127e-03 -0.053530136
## 264 0.9949916 0.9955425 -2.637316e-04 -0.055553415
## 265 1.3668520 1.3198376 1.578402e-02 0.061867027
## 266 1.0629698 1.0564901 2.774503e-03 0.049294291
## 267 1.1087323 1.0972550 4.684192e-03 0.050236306
## 268 1.0195396 1.0176863 8.025265e-04 0.043972764
## 269 1.0818889 1.0741571 2.996981e-03 0.041456621
## 270 0.6404379 0.6610453 -1.308371e-02 -0.024391408
## 271 1.1833529 1.1651933 6.196966e-03 0.041806579
## 272 0.5457628 0.5670113 -1.470093e-02 -0.018444695
## 273 1.1467923 1.1333663 4.333704e-03 0.035308681
## 274 1.1973325 1.1718268 1.037684e-02 0.066730367
## 275 1.2354138 1.2059731 1.155567e-02 0.064033652
## 276 0.8062216 0.8228686 -9.892754e-03 -0.043503997
## 277 0.6896286 0.7098312 -1.351717e-02 -0.031524017
## 278 0.7500825 0.7675972 -1.077461e-02 -0.033932223
## 279 0.8949801 0.9035104 -4.386827e-03 -0.039191424
## 280 1.1482591 1.1337530 5.728918e-03 0.046412614
## 281 0.8894196 0.8974982 -3.910296e-03 -0.032817646
## 282 1.1601145 1.1455691 5.304024e-03 0.040047431
## 283 1.6346072 1.5576998 1.974850e-02 0.052951484
## 284 1.0573372 1.0527978 1.692121e-03 0.032446846
## 285 1.1615452 1.1478421 4.534801e-03 0.033879818
## 286 1.1358358 1.1210044 6.366513e-03 0.056025105
## 287 1.0106042 1.0095626 5.008553e-04 0.050238496
## 288 1.0961700 1.0873546 3.816249e-03 0.045452985
## 289 0.6146032 0.6353681 -1.585370e-02 -0.026436809
## 290 1.1543679 1.1397994 5.960615e-03 0.046536210
## 291 0.8662147 0.8755860 -4.997515e-03 -0.033738135
## 292 0.9233016 0.9286148 -2.553888e-03 -0.031960543
## 293 1.1177983 1.1083198 3.701347e-03 0.036468942
## 294 1.0806772 1.0744706 2.473057e-03 0.034379029
## 295 1.0749782 1.0694933 2.088690e-03 0.031024893
## 296 1.0392157 1.0364842 1.049497e-03 0.028795614
## 297 1.0756285 1.0683165 3.324095e-03 0.049569426
## 298 0.4405447 0.4607810 -2.188398e-02 -0.017959584
## 299 1.2157641 1.1958703 7.911426e-03 0.046395759
## 300 0.8894321 0.8970095 -4.079371e-03 -0.034148387
## 301 0.6016136 0.6197611 -1.418900e-02 -0.022266766
## 302 1.0734066 1.0681063 2.263559e-03 0.034277062
## 303 1.2998841 1.2748056 8.723443e-03 0.039112232
## 304 1.1332025 1.1235198 3.810624e-03 0.033518946
## 305 0.8105096 0.8200979 -5.023169e-03 -0.022182397
## 306 0.4316438 0.4470672 -1.477606e-02 -0.011581026
## 307 1.2149000 1.1951239 7.883309e-03 0.046381279
## 308 0.6520541 0.6701245 -1.326396e-02 -0.025886763
## 309 1.3580661 1.3223647 1.271102e-02 0.050124773
## 310 1.0170919 1.0158313 5.880981e-04 0.036339796
## 311 0.5705630 0.5876476 -1.364300e-02 -0.018777342
## 312 0.4119801 0.4286143 -1.805630e-02 -0.013093728
## 313 0.7821070 0.7932730 -6.348831e-03 -0.023564616
## 314 1.1103687 1.1029310 2.861046e-03 0.029704290
## 315 1.2181912 1.2025012 5.387048e-03 0.031016140
## 316 0.4582770 0.4748428 -1.761270e-02 -0.015408485
## 317 1.6409404 1.5781272 1.901384e-02 0.050249412
## 318 1.6056595 1.5503533 1.705246e-02 0.046588567
## 319 0.5413745 0.5552008 -1.227492e-02 -0.014902360
## 320 0.5945166 0.6074227 -1.034756e-02 -0.015587466
## 321 0.6302189 0.6424052 -9.160123e-03 -0.016030036
## 322 1.1824917 1.1722871 3.994337e-03 0.026521167
## 323 0.4143627 0.4260395 -1.273558e-02 -0.009230813
## 324 0.7857090 0.7963923 -6.664471e-03 -0.025241115
## 325 1.0062862 1.0059125 1.823241e-04 0.030088557
## 326 1.4333086 1.4008938 1.101055e-02 0.037428515
## 327 0.7566325 0.7663305 -6.129709e-03 -0.019574607
## 328 1.1428694 1.1346506 3.432761e-03 0.028182538
## 329 2.3015674 2.1691546 2.697671e-02 0.048847416
## 330 0.4183669 0.4299966 -1.258749e-02 -0.009273581
## 331 0.6836610 0.6964839 -9.178298e-03 -0.020442367
## 332 0.7166092 0.7274738 -7.340032e-03 -0.019071887
## 333 1.1210864 1.1142593 2.939269e-03 0.027928150
## 334 0.8348268 0.8417367 -3.956629e-03 -0.020515076
## 335 1.2426012 1.2288283 5.177696e-03 0.027153213
## 336 1.1226313 1.1164702 2.522239e-03 0.023626999
## 337 0.9791127 0.9801199 -5.044373e-04 -0.024250195
## 338 0.7048507 0.7148729 -6.910113e-03 -0.016910032
## 339 0.7471744 0.7560436 -5.745876e-03 -0.017390154
## 340 0.6803251 0.6898296 -6.611898e-03 -0.014385718
## 341 1.0029304 1.0028079 5.728572e-05 0.020028059
## 342 0.4373973 0.4480086 -1.191484e-02 -0.009465819
## 343 0.7020545 0.7107152 -6.068465e-03 -0.014601869
## 344 0.5670349 0.5764187 -8.042143e-03 -0.010737027
## 345 0.9406598 0.9426848 -1.041477e-03 -0.016816820
## 346 1.5906784 1.5551293 1.112258e-02 0.030546319
## 347 0.3075010 0.3153182 -1.241600e-02 -0.005615320
## 348 0.6800000 0.6875688 -5.422410e-03 -0.011726626
## 349 1.0004490 1.0004335 7.640130e-06 0.017324262
## 350 0.3557600 0.3635835 -1.073437e-02 -0.006029788
## 351 1.3404255 1.3269639 4.991336e-03 0.019952172#calculating the normalised joint frequencies
library(ipfr)
adjacency<-matrix(0,num_cond,num_cond)
for (x in 1:num_cond){for (y in 1:num_cond){
adjacency[x,y]<-sum(dataset[,x]==1 & dataset[,y]==1)
}
}
diag(adjacency) <-0
row_tars <- rep(1, 27)
column_tars <- rep(1, 27)
njf<- ipu_matrix(adjacency, row_tars, column_tars)
for (x in 1:n){
confusion2$njf[x]<-njf[k[1,x],k[2,x]]
}
confusion2## condition1 condition2 A B C D O E OE
## 1 1 2 1113 2178 2217 4492 1113 1095.9030 1.0156008
## 2 1 3 871 1751 2459 4919 871 873.1260 0.9975651
## 3 1 4 699 1367 2631 5303 699 687.9780 1.0160209
## 4 1 5 564 1111 2766 5559 564 557.7750 1.0111604
## 5 1 6 577 1154 2753 5516 577 576.4230 1.0010010
## 6 1 7 498 932 2832 5738 498 476.1900 1.0458010
## 7 1 8 499 1019 2831 5651 499 505.4940 0.9871532
## 8 1 9 439 880 2891 5790 439 439.2270 0.9994832
## 9 1 10 401 722 2929 5948 401 373.9590 1.0723101
## 10 1 11 347 769 2983 5901 347 371.6280 0.9337294
## 11 1 12 349 703 2981 5967 349 350.3160 0.9962434
## 12 1 13 232 488 3098 6182 232 239.7600 0.9676343
## 13 1 14 228 446 3102 6224 228 224.4420 1.0158526
## 14 1 15 200 405 3130 6265 200 201.4650 0.9927283
## 15 1 16 169 367 3161 6303 169 178.4880 0.9468424
## 16 1 17 153 313 3177 6357 153 155.1780 0.9859645
## 17 1 18 169 293 3161 6377 169 153.8460 1.0985011
## 18 1 19 108 218 3222 6452 108 108.5580 0.9948599
## 19 1 20 106 217 3224 6453 106 107.5590 0.9855056
## 20 1 21 107 204 3223 6466 107 103.5630 1.0331875
## 21 1 22 93 184 3237 6486 93 92.2410 1.0082284
## 22 1 23 71 150 3259 6520 71 73.5930 0.9647657
## 23 1 24 62 140 3268 6530 62 67.2660 0.9217138
## 24 1 25 63 128 3267 6542 63 63.6030 0.9905193
## 25 1 26 66 91 3264 6579 66 52.2810 1.2624089
## 26 1 27 38 106 3292 6564 38 47.9520 0.7924591
## 27 2 3 827 1795 2464 4914 827 862.9002 0.9583959
## 28 2 4 670 1396 2621 5313 670 679.9206 0.9854092
## 29 2 5 555 1120 2736 5589 555 551.2425 1.0068164
## 30 2 6 552 1179 2739 5530 552 569.6721 0.9689785
## 31 2 7 473 957 2818 5752 473 470.6130 1.0050721
## 32 2 8 504 1014 2787 5695 504 499.5738 1.0088600
## 33 2 9 418 901 2873 5808 418 434.0829 0.9629497
## 34 2 10 357 766 2934 5943 357 369.5793 0.9659632
## 35 2 11 351 765 2940 5944 351 367.2756 0.9556856
## 36 2 12 339 713 2952 5996 339 346.2132 0.9791654
## 37 2 13 235 485 3056 6224 235 236.9520 0.9917620
## 38 2 14 219 455 3072 6254 219 221.8134 0.9873164
## 39 2 15 196 409 3095 6300 196 199.1055 0.9844027
## 40 2 16 180 356 3111 6353 180 176.3976 1.0204220
## 41 2 17 158 308 3133 6401 158 153.3606 1.0302516
## 42 2 18 139 323 3152 6386 139 152.0442 0.9142078
## 43 2 19 125 201 3166 6508 125 107.2866 1.1651036
## 44 2 20 95 228 3196 6481 95 106.2993 0.8937030
## 45 2 21 109 202 3182 6507 109 102.3501 1.0649721
## 46 2 22 85 192 3206 6517 85 91.1607 0.9324193
## 47 2 23 67 154 3224 6555 67 72.7311 0.9212015
## 48 2 24 63 139 3228 6570 63 66.4782 0.9476791
## 49 2 25 62 129 3229 6580 62 62.8581 0.9863486
## 50 2 26 51 106 3240 6603 51 51.6687 0.9870579
## 51 2 27 51 93 3240 6616 51 47.3904 1.0761673
## 52 3 4 567 1499 2055 5879 567 541.7052 1.0466948
## 53 3 5 449 1226 2173 6152 449 439.1850 1.0223482
## 54 3 6 445 1286 2177 6092 445 453.8682 0.9804608
## 55 3 7 420 1010 2202 6368 420 374.9460 1.1201613
## 56 3 8 394 1124 2228 6254 394 398.0196 0.9899010
## 57 3 9 314 1005 2308 6373 314 345.8418 0.9079296
## 58 3 10 296 827 2326 6551 296 294.4506 1.0052620
## 59 3 11 285 831 2337 6547 285 292.6152 0.9739754
## 60 3 12 275 777 2347 6601 275 275.8344 0.9969750
## 61 3 13 194 526 2428 6852 194 188.7840 1.0276295
## 62 3 14 179 495 2443 6883 179 176.7228 1.0128857
## 63 3 15 168 437 2454 6941 168 158.6310 1.0590616
## 64 3 16 143 393 2479 6985 143 140.5392 1.0175097
## 65 3 17 123 343 2499 7035 123 122.1852 1.0066686
## 66 3 18 119 343 2503 7035 119 121.1364 0.9823637
## 67 3 19 78 248 2544 7130 78 85.4772 0.9125240
## 68 3 20 87 236 2535 7142 87 84.6906 1.0272687
## 69 3 21 77 234 2545 7144 77 81.5442 0.9442732
## 70 3 22 76 201 2546 7177 76 72.6294 1.0464082
## 71 3 23 75 146 2547 7232 75 57.9462 1.2943040
## 72 3 24 44 158 2578 7220 44 52.9644 0.8307467
## 73 3 25 45 146 2577 7232 45 50.0802 0.8985587
## 74 3 26 40 117 2582 7261 40 41.1654 0.9716898
## 75 3 27 39 105 2583 7273 39 37.7568 1.0329265
## 76 4 5 343 1332 1723 6602 343 346.0550 0.9911719
## 77 4 6 358 1373 1708 6561 358 357.6246 1.0010497
## 78 4 7 288 1142 1778 6792 288 295.4380 0.9748238
## 79 4 8 312 1206 1754 6728 312 313.6188 0.9948383
## 80 4 9 277 1042 1789 6892 277 272.5054 1.0164936
## 81 4 10 225 898 1841 7036 225 232.0118 0.9697783
## 82 4 11 218 898 1848 7036 218 230.5656 0.9455010
## 83 4 12 233 819 1833 7115 233 217.3432 1.0720372
## 84 4 13 157 563 1909 7371 157 148.7520 1.0554480
## 85 4 14 131 543 1935 7391 131 139.2484 0.9407648
## 86 4 15 126 479 1940 7455 126 124.9930 1.0080565
## 87 4 16 101 435 1965 7499 101 110.7376 0.9120660
## 88 4 17 89 377 1977 7557 89 96.2756 0.9244295
## 89 4 18 96 366 1970 7568 96 95.4492 1.0057706
## 90 4 19 58 268 2008 7666 58 67.3516 0.8611525
## 91 4 20 69 254 1997 7680 69 66.7318 1.0339898
## 92 4 21 76 235 1990 7699 76 64.2526 1.1828315
## 93 4 22 59 218 2007 7716 59 57.2282 1.0309603
## 94 4 23 47 174 2019 7760 47 45.6586 1.0293789
## 95 4 24 41 161 2025 7773 41 41.7332 0.9824313
## 96 4 25 28 163 2038 7771 28 39.4606 0.7095685
## 97 4 26 21 136 2045 7798 21 32.4362 0.6474248
## 98 4 27 31 113 2035 7821 31 29.7504 1.0420028
## 99 5 6 278 1453 1397 6872 278 289.9425 0.9588108
## 100 5 7 252 1178 1423 7147 252 239.5250 1.0520822
## 101 5 8 243 1275 1432 7050 243 254.2650 0.9556958
## 102 5 9 201 1118 1474 7207 201 220.9325 0.9097801
## 103 5 10 176 947 1499 7378 176 188.1025 0.9356601
## 104 5 11 188 928 1487 7397 188 186.9300 1.0057241
## 105 5 12 158 894 1517 7431 158 176.2100 0.8966574
## 106 5 13 119 601 1556 7724 119 120.6000 0.9867330
## 107 5 14 122 552 1553 7773 122 112.8950 1.0806502
## 108 5 15 112 493 1563 7832 112 101.3375 1.1052177
## 109 5 16 88 448 1587 7877 88 89.7800 0.9801738
## 110 5 17 62 404 1613 7921 62 78.0550 0.7943117
## 111 5 18 70 392 1605 7933 70 77.3850 0.9045681
## 112 5 19 52 274 1623 8051 52 54.6050 0.9522937
## 113 5 20 47 276 1628 8049 47 54.1025 0.8687214
## 114 5 21 46 265 1629 8060 46 52.0925 0.8830446
## 115 5 22 33 244 1642 8081 33 46.3975 0.7112452
## 116 5 23 43 178 1632 8147 43 37.0175 1.1616128
## 117 5 24 27 175 1648 8150 27 33.8350 0.7979902
## 118 5 25 42 149 1633 8176 42 31.9925 1.3128077
## 119 5 26 24 133 1651 8192 24 26.2975 0.9126343
## 120 5 27 25 119 1650 8206 25 24.1200 1.0364842
## 121 6 7 225 1205 1506 7064 225 247.5330 0.9089697
## 122 6 8 238 1280 1493 6989 238 262.7658 0.9057495
## 123 6 9 238 1081 1493 7188 238 228.3189 1.0424017
## 124 6 10 170 953 1561 7316 170 194.3913 0.8745247
## 125 6 11 197 919 1534 7350 197 193.1796 1.0197764
## 126 6 12 175 877 1556 7392 175 182.1012 0.9610041
## 127 6 13 127 593 1604 7676 127 124.6320 1.0189999
## 128 6 14 124 550 1607 7719 124 116.6694 1.0628322
## 129 6 15 101 504 1630 7765 101 104.7255 0.9644260
## 130 6 16 94 442 1637 7827 94 92.7816 1.0131319
## 131 6 17 79 387 1652 7882 79 80.6646 0.9793639
## 132 6 18 85 377 1646 7892 85 79.9722 1.0628693
## 133 6 19 62 264 1669 8005 62 56.4306 1.0986947
## 134 6 20 49 274 1682 7995 49 55.9113 0.8763881
## 135 6 21 53 258 1678 8011 53 53.8341 0.9845061
## 136 6 22 48 229 1683 8040 48 47.9487 1.0010699
## 137 6 23 39 182 1692 8087 39 38.2551 1.0194719
## 138 6 24 37 165 1694 8104 37 34.9662 1.0581647
## 139 6 25 30 161 1701 8108 30 33.0621 0.9073834
## 140 6 26 37 120 1694 8149 37 27.1767 1.3614604
## 141 6 27 25 119 1706 8150 25 24.9264 1.0029527
## 142 7 8 207 1311 1223 7259 207 217.0740 0.9535919
## 143 7 9 182 1137 1248 7433 182 188.6170 0.9649183
## 144 7 10 186 937 1244 7633 186 160.5890 1.1582362
## 145 7 11 166 950 1264 7620 166 159.5880 1.0401785
## 146 7 12 161 891 1269 7679 161 150.4360 1.0702226
## 147 7 13 113 607 1317 7963 113 102.9600 1.0975136
## 148 7 14 88 586 1342 7984 88 96.3820 0.9130336
## 149 7 15 87 518 1343 8052 87 86.5150 1.0056060
## 150 7 16 65 471 1365 8099 65 76.6480 0.8480326
## 151 7 17 56 410 1374 8160 56 66.6380 0.8403614
## 152 7 18 64 398 1366 8172 64 66.0660 0.9687282
## 153 7 19 56 270 1374 8300 56 46.6180 1.2012527
## 154 7 20 40 283 1390 8287 40 46.1890 0.8660071
## 155 7 21 40 271 1390 8299 40 44.4730 0.8994221
## 156 7 22 40 237 1390 8333 40 39.6110 1.0098205
## 157 7 23 34 187 1396 8383 34 31.6030 1.0758472
## 158 7 24 21 181 1409 8389 21 28.8860 0.7269958
## 159 7 25 27 164 1403 8406 27 27.3130 0.9885403
## 160 7 26 15 142 1415 8428 15 22.4510 0.6681217
## 161 7 27 25 119 1405 8451 25 20.5920 1.2140637
## 162 8 9 184 1135 1334 7347 184 200.2242 0.9189698
## 163 8 10 173 950 1345 7532 173 170.4714 1.0148330
## 164 8 11 161 955 1357 7527 161 169.4088 0.9503639
## 165 8 12 150 902 1368 7580 150 159.6936 0.9392988
## 166 8 13 110 610 1408 7872 110 109.2960 1.0064412
## 167 8 14 104 570 1414 7912 104 102.3132 1.0164866
## 168 8 15 90 515 1428 7967 90 91.8390 0.9799758
## 169 8 16 72 464 1446 8018 72 81.3648 0.8849035
## 170 8 17 74 392 1444 8090 74 70.7388 1.0461020
## 171 8 18 75 387 1443 8095 75 70.1316 1.0694181
## 172 8 19 47 279 1471 8203 47 49.4868 0.9497482
## 173 8 20 46 277 1472 8205 46 49.0314 0.9381743
## 174 8 21 41 270 1477 8212 41 47.2098 0.8684638
## 175 8 22 36 241 1482 8241 36 42.0486 0.8561522
## 176 8 23 39 182 1479 8300 39 33.5478 1.1625203
## 177 8 24 30 172 1488 8310 30 30.6636 0.9783587
## 178 8 25 28 163 1490 8319 28 28.9938 0.9657237
## 179 8 26 28 129 1490 8353 28 23.8326 1.1748613
## 180 8 27 26 118 1492 8364 26 21.8592 1.1894305
## 181 9 10 137 986 1182 7695 137 148.1237 0.9249026
## 182 9 11 160 956 1159 7725 160 147.2004 1.0869536
## 183 9 12 118 934 1201 7747 118 138.7588 0.8503965
## 184 9 13 98 622 1221 8059 98 94.9680 1.0319265
## 185 9 14 86 588 1233 8093 86 88.9006 0.9673725
## 186 9 15 76 529 1243 8152 76 79.7995 0.9523869
## 187 9 16 73 463 1246 8218 73 70.6984 1.0325552
## 188 9 17 59 407 1260 8274 59 61.4654 0.9598896
## 189 9 18 49 413 1270 8268 49 60.9378 0.8040986
## 190 9 19 54 272 1265 8409 54 42.9994 1.2558315
## 191 9 20 43 280 1276 8401 43 42.6037 1.0093020
## 192 9 21 42 269 1277 8412 42 41.0209 1.0238683
## 193 9 22 36 241 1283 8440 36 36.5363 0.9853214
## 194 9 23 33 188 1286 8493 33 29.1499 1.1320794
## 195 9 24 22 180 1297 8501 22 26.6438 0.8257080
## 196 9 25 20 171 1299 8510 20 25.1929 0.7938745
## 197 9 26 19 138 1300 8543 19 20.7083 0.9175065
## 198 9 27 21 123 1298 8558 21 18.9936 1.1056356
## 199 10 11 125 991 998 7886 125 125.3268 0.9973924
## 200 10 12 104 948 1019 7929 104 118.1396 0.8803145
## 201 10 13 78 642 1045 8235 78 80.8560 0.9646779
## 202 10 14 71 603 1052 8274 71 75.6902 0.9380343
## 203 10 15 73 532 1050 8345 73 67.9415 1.0744538
## 204 10 16 50 486 1073 8391 50 60.1928 0.8306641
## 205 10 17 51 415 1072 8462 51 52.3318 0.9745508
## 206 10 18 48 414 1075 8463 48 51.8826 0.9251657
## 207 10 19 42 284 1081 8593 42 36.6098 1.1472338
## 208 10 20 41 282 1082 8595 41 36.2729 1.1303204
## 209 10 21 36 275 1087 8602 36 34.9253 1.0307714
## 210 10 22 29 248 1094 8629 29 31.1071 0.9322631
## 211 10 23 30 191 1093 8686 30 24.8183 1.2087855
## 212 10 24 25 177 1098 8700 25 22.6846 1.1020692
## 213 10 25 17 174 1106 8703 17 21.4493 0.7925667
## 214 10 26 20 137 1103 8740 20 17.6311 1.1343592
## 215 10 27 14 130 1109 8747 14 16.1712 0.8657366
## 216 11 12 121 931 995 7953 121 117.4032 1.0306363
## 217 11 13 86 634 1030 8250 86 80.3520 1.0702907
## 218 11 14 72 602 1044 8282 72 75.2184 0.9572126
## 219 11 15 69 536 1047 8348 69 67.5180 1.0219497
## 220 11 16 57 479 1059 8405 57 59.8176 0.9528968
## 221 11 17 41 425 1075 8459 41 52.0056 0.7883766
## 222 11 18 42 420 1074 8464 42 51.5592 0.8145976
## 223 11 19 33 293 1083 8591 33 36.3816 0.9070519
## 224 11 20 42 281 1074 8603 42 36.0468 1.1651520
## 225 11 21 33 278 1083 8606 33 34.7076 0.9508004
## 226 11 22 33 244 1083 8640 33 30.9132 1.0675051
## 227 11 23 28 193 1088 8691 28 24.6636 1.1352763
## 228 11 24 21 181 1095 8703 21 22.5432 0.9315448
## 229 11 25 16 175 1100 8709 16 21.3156 0.7506240
## 230 11 26 19 138 1097 8746 19 17.5212 1.0844006
## 231 11 27 20 124 1096 8760 20 16.0704 1.2445241
## 232 12 13 72 648 980 8300 72 75.7440 0.9505703
## 233 12 14 73 601 979 8347 73 70.9048 1.0295495
## 234 12 15 72 533 980 8415 72 63.6460 1.1312573
## 235 12 16 55 481 997 8467 55 56.3872 0.9753987
## 236 12 17 49 417 1003 8531 49 49.0232 0.9995268
## 237 12 18 39 423 1013 8525 39 48.6024 0.8024295
## 238 12 19 35 291 1017 8657 35 34.2952 1.0205510
## 239 12 20 43 280 1009 8668 43 33.9796 1.2654652
## 240 12 21 33 278 1019 8670 33 32.7172 1.0086438
## 241 12 22 37 240 1015 8708 37 29.1404 1.2697149
## 242 12 23 18 203 1034 8745 18 23.2492 0.7742202
## 243 12 24 16 186 1036 8762 16 21.2504 0.7529270
## 244 12 25 17 174 1035 8774 17 20.0932 0.8460574
## 245 12 26 19 138 1033 8810 19 16.5164 1.1503718
## 246 12 27 15 129 1037 8819 15 15.1488 0.9901774
## 247 13 14 39 635 681 8645 39 48.5280 0.8036597
## 248 13 15 42 563 678 8717 42 43.5600 0.9641873
## 249 13 16 36 500 684 8780 36 38.5920 0.9328358
## 250 13 17 31 435 689 8845 31 33.5520 0.9239390
## 251 13 18 33 429 687 8851 33 33.2640 0.9920635
## 252 13 19 18 308 702 8972 18 23.4720 0.7668712
## 253 13 20 26 297 694 8983 26 23.2560 1.1179911
## 254 13 21 28 283 692 8997 28 22.3920 1.2504466
## 255 13 22 19 258 701 9022 19 19.9440 0.9526675
## 256 13 23 18 203 702 9077 18 15.9120 1.1312217
## 257 13 24 17 185 703 9095 17 14.5440 1.1688669
## 258 13 25 16 175 704 9105 16 13.7520 1.1634671
## 259 13 26 11 146 709 9134 11 11.3040 0.9731069
## 260 13 27 9 135 711 9145 9 10.3680 0.8680556
## 261 14 15 42 563 632 8763 42 40.7770 1.0299924
## 262 14 16 39 497 635 8829 39 36.1264 1.0795429
## 263 14 17 30 436 644 8890 30 31.4084 0.9551585
## 264 14 18 31 431 643 8895 31 31.1388 0.9955425
## 265 14 19 29 297 645 9029 29 21.9724 1.3198376
## 266 14 20 23 300 651 9026 23 21.7702 1.0564901
## 267 14 21 23 288 651 9038 23 20.9614 1.0972550
## 268 14 22 19 258 655 9068 19 18.6698 1.0176863
## 269 14 23 16 205 658 9121 16 14.8954 1.0741571
## 270 14 24 9 193 665 9133 9 13.6148 0.6610453
## 271 14 25 15 176 659 9150 15 12.8734 1.1651933
## 272 14 26 6 151 668 9175 6 10.5818 0.5670113
## 273 14 27 11 133 663 9193 11 9.7056 1.1333663
## 274 15 16 38 498 567 8897 38 32.4280 1.1718268
## 275 15 17 34 432 571 8963 34 28.1930 1.2059731
## 276 15 18 23 439 582 8956 23 27.9510 0.8228686
## 277 15 19 14 312 591 9083 14 19.7230 0.7098312
## 278 15 20 15 308 590 9087 15 19.5415 0.7675972
## 279 15 21 17 294 588 9101 17 18.8155 0.9035104
## 280 15 22 19 258 586 9137 19 16.7585 1.1337530
## 281 15 23 12 209 593 9186 12 13.3705 0.8974982
## 282 15 24 14 188 591 9207 14 12.2210 1.1455691
## 283 15 25 18 173 587 9222 18 11.5555 1.5576998
## 284 15 26 10 147 595 9248 10 9.4985 1.0527978
## 285 15 27 10 134 595 9261 10 8.7120 1.1478421
## 286 16 17 28 438 508 9026 28 24.9776 1.1210044
## 287 16 18 25 437 511 9027 25 24.7632 1.0095626
## 288 16 19 19 307 517 9157 19 17.4736 1.0873546
## 289 16 20 11 312 525 9152 11 17.3128 0.6353681
## 290 16 21 19 292 517 9172 19 16.6696 1.1397994
## 291 16 22 13 264 523 9200 13 14.8472 0.8755860
## 292 16 23 11 210 525 9254 11 11.8456 0.9286148
## 293 16 24 12 190 524 9274 12 10.8272 1.1083198
## 294 16 25 11 180 525 9284 11 10.2376 1.0744706
## 295 16 26 9 148 527 9316 9 8.4152 1.0694933
## 296 16 27 8 136 528 9328 8 7.7184 1.0364842
## 297 17 18 23 439 443 9095 23 21.5292 1.0683165
## 298 17 19 7 319 459 9215 7 15.1916 0.4607810
## 299 17 20 18 305 448 9229 18 15.0518 1.1958703
## 300 17 21 13 298 453 9236 13 14.4926 0.8970095
## 301 17 22 8 269 458 9265 8 12.9082 0.6197611
## 302 17 23 11 210 455 9324 11 10.2986 1.0681063
## 303 17 24 12 190 454 9344 12 9.4132 1.2748056
## 304 17 25 10 181 456 9353 10 8.9006 1.1235198
## 305 17 26 6 151 460 9383 6 7.3162 0.8200979
## 306 17 27 3 141 463 9393 3 6.7104 0.4470672
## 307 18 19 18 308 444 9230 18 15.0612 1.1951239
## 308 18 20 10 313 452 9225 10 14.9226 0.6701245
## 309 18 21 19 292 443 9246 19 14.3682 1.3223647
## 310 18 22 13 264 449 9274 13 12.7974 1.0158313
## 311 18 23 6 215 456 9323 6 10.2102 0.5876476
## 312 18 24 4 198 458 9340 4 9.3324 0.4286143
## 313 18 25 7 184 455 9354 7 8.8242 0.7932730
## 314 18 26 8 149 454 9389 8 7.2534 1.1029310
## 315 18 27 8 136 454 9402 8 6.6528 1.2025012
## 316 19 20 5 318 321 9356 5 10.5298 0.4748428
## 317 19 21 16 295 310 9379 16 10.1386 1.5781272
## 318 19 22 14 263 312 9411 14 9.0302 1.5503533
## 319 19 23 4 217 322 9457 4 7.2046 0.5552008
## 320 19 24 4 198 322 9476 4 6.5852 0.6074227
## 321 19 25 4 187 322 9487 4 6.2266 0.6424052
## 322 19 26 6 151 320 9523 6 5.1182 1.1722871
## 323 19 27 2 142 324 9532 2 4.6944 0.4260395
## 324 20 21 8 303 315 9374 8 10.0453 0.7963923
## 325 20 22 9 268 314 9409 9 8.9471 1.0059125
## 326 20 23 10 211 313 9466 10 7.1383 1.4008938
## 327 20 24 5 197 318 9480 5 6.5246 0.7663305
## 328 20 25 7 184 316 9493 7 6.1693 1.1346506
## 329 20 26 11 146 312 9531 11 5.0711 2.1691546
## 330 20 27 2 142 321 9535 2 4.6512 0.4299966
## 331 21 22 6 271 305 9418 6 8.6147 0.6964839
## 332 21 23 5 216 306 9473 5 6.8731 0.7274738
## 333 21 24 7 195 304 9494 7 6.2822 1.1142593
## 334 21 25 5 186 306 9503 5 5.9401 0.8417367
## 335 21 26 6 151 305 9538 6 4.8827 1.2288283
## 336 21 27 5 139 306 9550 5 4.4784 1.1164702
## 337 22 23 6 215 271 9508 6 6.1217 0.9801199
## 338 22 24 4 198 273 9525 4 5.5954 0.7148729
## 339 22 25 4 187 273 9536 4 5.2907 0.7560436
## 340 22 26 3 154 274 9569 3 4.3489 0.6898296
## 341 22 27 4 140 273 9583 4 3.9888 1.0028079
## 342 23 24 2 200 219 9579 2 4.4642 0.4480086
## 343 23 25 3 188 218 9591 3 4.2211 0.7107152
## 344 23 26 2 155 219 9624 2 3.4697 0.5764187
## 345 23 27 3 141 218 9638 3 3.1824 0.9426848
## 346 24 25 6 185 196 9613 6 3.8582 1.5551293
## 347 24 26 1 156 201 9642 1 3.1714 0.3153182
## 348 24 27 2 142 200 9656 2 2.9088 0.6875688
## 349 25 26 3 154 188 9655 3 2.9987 1.0004335
## 350 25 27 1 143 190 9666 1 2.7504 0.3635835
## 351 26 27 3 141 154 9702 3 2.2608 1.3269639
## OR lift phi sci njf
## 1 1.0354076 1.0156008 7.720440e-03 0.336208823 0.03854613
## 2 0.9950624 0.9975651 -1.025634e-03 -0.294767565 0.03735287
## 3 1.0306458 1.0160209 5.776491e-03 0.266495513 0.03910222
## 4 1.0202569 1.0111604 3.537161e-03 0.238808390 0.03917583
## 5 1.0018162 1.0010010 3.236054e-04 0.240328437 0.03749660
## 6 1.0826317 1.0458010 1.321941e-02 0.228212384 0.03986867
## 7 0.9774888 0.9871532 -3.840097e-03 -0.221943558 0.03745463
## 8 0.9991077 0.9994832 -1.423422e-04 -0.209469119 0.03847238
## 9 1.1278693 1.0723101 1.817250e-02 0.207363530 0.04047219
## 10 0.8926383 0.9337294 -1.659616e-02 -0.180001142 0.03536613
## 11 0.9937203 0.9962434 -9.101228e-04 -0.186464191 0.03800005
## 12 0.9486713 0.9676343 -6.369956e-03 -0.149830290 0.03603426
## 13 1.0257175 1.0158526 3.011225e-03 0.152188831 0.03831645
## 14 0.9884432 0.9927283 -1.303847e-03 -0.140906229 0.03605918
## 15 0.9182130 0.9468424 -8.938608e-03 -0.126497573 0.03596047
## 16 0.9780974 0.9859645 -2.192514e-03 -0.122822054 0.03932914
## 17 1.1636195 1.0985011 1.531767e-02 0.136252224 0.04387163
## 18 0.9920558 0.9948599 -6.667105e-04 -0.103655616 0.03902267
## 19 0.9777161 0.9855056 -1.871067e-03 -0.102207435 0.03755071
## 20 1.0522744 1.0331875 4.201214e-03 0.105143267 0.03786023
## 21 1.0127433 1.0082284 9.813337e-04 0.096832456 0.03967156
## 22 0.9469571 0.9647657 -3.742606e-03 -0.082763738 0.03798229
## 23 0.8849012 0.9217138 -7.942394e-03 -0.075595142 0.03962021
## 24 0.9855802 0.9905193 -9.347667e-04 -0.078995390 0.03898698
## 25 1.4618819 1.2624089 2.341660e-02 0.091279235 0.04695735
## 26 0.7148032 0.7924591 -1.772531e-02 -0.054875720 0.03145916
## 27 0.9188307 0.9583959 -1.737074e-02 -0.281530353 0.03632609
## 28 0.9728865 0.9854092 -5.214754e-03 -0.256948273 0.03838896
## 29 1.0122621 1.0068164 2.141444e-03 0.236385937 0.03948566
## 30 0.9452754 0.9689785 -9.940776e-03 -0.231273889 0.03674197
## 31 1.0088511 1.0050721 1.451113e-03 0.218036489 0.03878563
## 32 1.0156623 1.0088600 2.625144e-03 0.225491777 0.03874743
## 33 0.9378696 0.9629497 -1.011496e-02 -0.200627260 0.03752045
## 34 0.9440284 0.9659632 -8.478922e-03 -0.185701066 0.03690522
## 35 0.9276351 0.9556856 -1.100038e-02 -0.183151752 0.03664144
## 36 0.9657294 0.9791654 -5.003389e-03 -0.182191406 0.03780644
## 37 0.9868300 0.9917620 -1.607114e-03 -0.152664364 0.03738546
## 38 0.9798721 0.9873164 -2.388147e-03 -0.147044988 0.03769657
## 39 0.9754672 0.9844027 -2.772124e-03 -0.138903901 0.03619505
## 40 1.0325268 1.0204220 3.403920e-03 0.135527107 0.03923001
## 41 1.0480785 1.0302516 4.684233e-03 0.127585167 0.04159943
## 42 0.8718765 0.9142078 -1.322437e-02 -0.112727499 0.03695891
## 43 1.2783524 1.1651036 2.122742e-02 0.120680548 0.04626052
## 44 0.8449364 0.8937030 -1.360151e-02 -0.092142163 0.03447015
## 45 1.1034579 1.0649721 8.152721e-03 0.107741337 0.03950329
## 46 0.8999158 0.9324193 -7.989088e-03 -0.089025639 0.03713834
## 47 0.8845691 0.9212015 -8.296631e-03 -0.078562397 0.03671173
## 48 0.9224813 0.9476791 -5.261594e-03 -0.077268223 0.04123566
## 49 0.9793994 0.9863486 -1.334185e-03 -0.078200776 0.03929869
## 50 0.9805294 0.9870579 -1.144788e-03 -0.070950655 0.03716526
## 51 1.1197929 1.0761673 6.448143e-03 0.074084097 0.04324551
## 52 1.0821141 1.0466948 1.420477e-02 0.243613615 0.04022859
## 53 1.0368417 1.0223482 5.975925e-03 0.214250869 0.03955606
## 54 0.9683236 0.9804608 -5.329357e-03 -0.208879170 0.03667783
## 55 1.2025791 1.1201613 2.926101e-02 0.216902685 0.04264609
## 56 0.9839490 0.9899010 -2.546901e-03 -0.197489492 0.03750842
## 57 0.8627236 0.9079296 -2.139462e-02 -0.168846051 0.03490131
## 58 1.0080547 1.0052620 1.115719e-03 0.172498566 0.03789061
## 59 0.9607878 0.9739754 -5.498690e-03 -0.166608218 0.03684092
## 60 0.9954245 0.9969750 -6.183264e-04 -0.165580230 0.03797686
## 61 1.0408416 1.0276295 4.587876e-03 0.141194942 0.03821703
## 62 1.0188310 1.0128857 2.065085e-03 0.134650118 0.03815321
## 63 1.0873650 1.0590616 8.934731e-03 0.133387536 0.03841693
## 64 1.0252585 1.0175097 2.484110e-03 0.120624992 0.03859244
## 65 1.0095058 1.0066686 8.788906e-04 0.111274540 0.04010104
## 66 0.9751156 0.9823637 -2.313914e-03 -0.108120894 0.03918065
## 67 0.8814858 0.9125240 -9.572855e-03 -0.084366389 0.03574501
## 68 1.0386019 1.0272687 2.969900e-03 0.094536963 0.03908941
## 69 0.9236949 0.9442732 -5.951852e-03 -0.085269592 0.03455554
## 70 1.0658647 1.0464082 4.669620e-03 0.089177925 0.04111852
## 71 1.4586056 1.2943040 2.637498e-02 0.098525531 0.05088756
## 72 0.7799197 0.8307467 -1.448742e-02 -0.060458957 0.03566197
## 73 0.8649752 0.8985587 -8.438516e-03 -0.063588633 0.03531987
## 74 0.9614226 0.9716898 -2.131453e-03 -0.062343879 0.03609501
## 75 1.0458382 1.0329265 2.372596e-03 0.063469784 0.04095017
## 76 0.9866887 0.9911719 -2.020693e-03 -0.184383288 0.03941659
## 77 1.0016008 1.0010497 2.450801e-04 0.189308160 0.03848974
## 78 0.9633682 0.9748238 -5.247911e-03 -0.167555740 0.03814528
## 79 0.9923473 0.9948383 -1.114287e-03 -0.176178760 0.03874409
## 80 1.0241109 1.0164936 3.280745e-03 0.167800099 0.04016152
## 81 0.9575870 0.9697783 -5.485235e-03 -0.147715980 0.03756996
## 82 0.9242810 0.9455010 -9.856792e-03 -0.143568525 0.03675873
## 83 1.1042934 1.0720372 1.260436e-02 0.158045776 0.04197209
## 84 1.0767422 1.0554480 7.881287e-03 0.128726584 0.04034346
## 85 0.9214965 0.9407648 -8.126075e-03 -0.111013601 0.03642232
## 86 1.0108366 1.0080565 1.043259e-03 0.112700982 0.03758394
## 87 0.8860800 0.9120660 -1.067876e-02 -0.095978470 0.03555543
## 88 0.9023840 0.9244295 -8.525641e-03 -0.090705138 0.03784939
## 89 1.0076392 1.0057706 6.480866e-04 0.098261884 0.04123008
## 90 0.8262250 0.8611525 -1.300657e-02 -0.070673083 0.03467106
## 91 1.0447167 1.0339898 3.168826e-03 0.084466144 0.04043964
## 92 1.2512007 1.1828315 1.671517e-02 0.094813077 0.04448964
## 93 1.0404959 1.0309603 2.666634e-03 0.077991445 0.04163842
## 94 1.0381832 1.0293789 2.253739e-03 0.069556315 0.04159743
## 95 0.9775109 0.9824313 -1.287262e-03 -0.063466276 0.04334658
## 96 0.6550028 0.7095685 -2.068079e-02 -0.044573444 0.02866705
## 97 0.5888034 0.6474248 -2.272254e-02 -0.036872647 0.02471866
## 98 1.0543411 1.0420028 2.590766e-03 0.056834925 0.04245913
## 99 0.9411653 0.9588108 -8.453191e-03 -0.163263407 0.03711262
## 100 1.0744201 1.0520822 9.542949e-03 0.162826511 0.04144420
## 101 0.9383010 0.9556958 -8.407108e-03 -0.152392285 0.03746900
## 102 0.8790454 0.9097801 -1.577448e-02 -0.135227884 0.03618606
## 103 0.9147443 0.9356601 -1.026485e-02 -0.128326215 0.03649100
## 104 1.0077540 1.0057241 9.100136e-04 0.137504954 0.03936196
## 105 0.8657276 0.8966574 -1.589423e-02 -0.119025992 0.03534082
## 106 0.9828906 0.9867330 -1.657602e-03 -0.108361076 0.03796954
## 107 1.1062110 1.0806502 9.725295e-03 0.114821305 0.04211832
## 108 1.1383736 1.1052177 1.197660e-02 0.111258431 0.04148247
## 109 0.9749640 0.9801738 -2.116414e-03 -0.092873726 0.03846645
## 110 0.7536262 0.7943117 -2.039766e-02 -0.070176439 0.03273976
## 111 0.8826213 0.9045681 -9.421094e-03 -0.079573717 0.03732982
## 112 0.9414214 0.9522937 -3.928225e-03 -0.070369933 0.03859734
## 113 0.8419306 0.8687214 -1.075821e-02 -0.063898283 0.03420353
## 114 0.8588670 0.8830446 -9.398885e-03 -0.063733861 0.03343629
## 115 0.6656042 0.7112452 -2.186171e-02 -0.048446973 0.02891818
## 116 1.2059409 1.1616128 1.089782e-02 0.070674853 0.04725546
## 117 0.7630028 0.7979902 -1.301052e-02 -0.046417385 0.03544456
## 118 1.4112947 1.3128077 1.957929e-02 0.074254914 0.05339359
## 119 0.8953699 0.9126343 -4.949285e-03 -0.046800879 0.03507775
## 120 1.0448179 1.0364842 1.978115e-03 0.050903935 0.04251717
## 121 0.8758328 0.9089697 -1.701318e-02 -0.143009855 0.03461965
## 122 0.8704067 0.9057495 -1.824283e-02 -0.146822474 0.03433363
## 123 1.0599845 1.0424017 7.562100e-03 0.157509236 0.04008660
## 124 0.8360395 0.8745247 -2.041912e-02 -0.121929982 0.03297608
## 125 1.0270999 1.0197764 3.206989e-03 0.141737770 0.03858887
## 126 0.9479618 0.9610041 -6.117667e-03 -0.129682581 0.03662137
## 127 1.0248956 1.0189999 2.421399e-03 0.113759831 0.03791133
## 128 1.0829394 1.0628322 7.728351e-03 0.114800348 0.04005068
## 129 0.9546511 0.9644260 -4.130318e-03 -0.098695000 0.03499814
## 130 1.0168391 1.0131319 1.429865e-03 0.097588114 0.03844185
## 131 0.9739631 0.9793639 -2.087394e-03 -0.087960077 0.03902904
## 132 1.0810227 1.0628693 6.330723e-03 0.095049405 0.04240858
## 133 1.1264003 1.0986947 8.289370e-03 0.082534278 0.04305491
## 134 0.8500373 0.8763881 -1.033268e-02 -0.065530923 0.03336154
## 135 0.9807333 0.9845061 -1.270058e-03 -0.072234911 0.03604235
## 136 1.0013311 1.0010699 8.262326e-05 0.069319085 0.03935277
## 137 1.0241895 1.0194719 1.339305e-03 0.063055059 0.04009822
## 138 1.0727630 1.0581647 3.821104e-03 0.062571635 0.04544275
## 139 0.8881878 0.9073834 -5.913101e-03 -0.052174229 0.03568109
## 140 1.4832399 1.3614604 2.088665e-02 0.070974667 0.05059401
## 141 1.0036254 1.0029527 1.632944e-04 0.050073763 0.03977785
## 142 0.9371692 0.9535919 -8.019707e-03 -0.140496803 0.03678743
## 143 0.9533678 0.9649183 -5.585929e-03 -0.132519861 0.03776417
## 144 1.2180027 1.1582362 2.299007e-02 0.146776000 0.04444771
## 145 1.0533977 1.0401785 5.816995e-03 0.131403814 0.04005809
## 146 1.0934306 1.0702226 9.835560e-03 0.131265316 0.04150580
## 147 1.1255912 1.0975136 1.109520e-02 0.111363835 0.04155566
## 148 0.8934146 0.9130336 -9.550180e-03 -0.089636462 0.03501525
## 149 1.0069717 1.0056060 5.811085e-04 0.093534870 0.03713891
## 150 0.8188252 0.8480326 -1.477315e-02 -0.074244270 0.03274735
## 151 0.8111620 0.8403614 -1.441687e-02 -0.068600463 0.03408279
## 152 0.9619989 0.9687282 -2.811397e-03 -0.078739194 0.03933699
## 153 1.2528977 1.2012527 1.509125e-02 0.082018384 0.04790771
## 154 0.8426672 0.8660071 -9.999779e-03 -0.058855996 0.03355030
## 155 0.8812551 0.8994221 -7.360728e-03 -0.059980734 0.03351071
## 156 1.0118083 1.0098205 6.770974e-04 0.063555346 0.04039991
## 157 1.0918208 1.0758472 4.657644e-03 0.060480415 0.04306514
## 158 0.6907803 0.7269958 -1.601231e-02 -0.039072895 0.03177375
## 159 0.9863967 0.9885403 -6.532161e-04 -0.051662933 0.03956097
## 160 0.6291743 0.6681217 -1.712153e-02 -0.031657267 0.02526824
## 161 1.2636442 1.2140637 1.056943e-02 0.055092280 0.04900356
## 162 0.8928452 0.9189698 -1.336199e-02 -0.130034784 0.03579555
## 163 1.0197895 1.0148330 2.231887e-03 0.132501361 0.03876012
## 164 0.9351140 0.9503639 -7.442394e-03 -0.123696637 0.03642593
## 165 0.9214416 0.9392988 -8.804997e-03 -0.118699121 0.03625574
## 166 1.0081967 1.0064412 7.590098e-04 0.105218123 0.03792687
## 167 1.0209286 1.0164866 1.874998e-03 0.102817610 0.03879811
## 168 0.9749939 0.9799758 -2.149664e-03 -0.093913697 0.03602094
## 169 0.8604235 0.8849035 -1.158761e-02 -0.079820458 0.03400931
## 170 1.0576135 1.0461020 4.311833e-03 0.087983833 0.04222615
## 171 1.0871784 1.0694181 6.463259e-03 0.089558001 0.04322003
## 172 0.9394068 0.9497482 -3.902510e-03 -0.066811800 0.03769796
## 173 0.9256543 0.9381743 -4.778445e-03 -0.065693240 0.03617403
## 174 0.8442840 0.8684638 -9.969490e-03 -0.059671613 0.03220402
## 175 0.8306483 0.8561522 -1.027140e-02 -0.055517095 0.03408988
## 176 1.2025500 1.1625203 1.033578e-02 0.067333716 0.04631420
## 177 0.9740716 0.9783587 -1.314550e-03 -0.054176343 0.04255720
## 178 0.9590810 0.9657237 -2.023415e-03 -0.052000254 0.03846483
## 179 1.2168149 1.1748613 9.342575e-03 0.057355137 0.04422262
## 180 1.2351979 1.1894305 9.686517e-03 0.055610425 0.04778192
## 181 0.9045546 0.9249026 -1.041164e-02 -0.112566274 0.03583761
## 182 1.1155194 1.0869536 1.201304e-02 0.131875916 0.04226533
## 183 0.8149401 0.8503965 -1.999512e-02 -0.100173244 0.03330021
## 184 1.0399230 1.0319265 3.466424e-03 0.100562817 0.03945117
## 185 0.9599919 0.9673725 -3.419030e-03 -0.091210766 0.03745891
## 186 0.9422171 0.9523869 -4.709698e-03 -0.085077262 0.03551449
## 187 1.0398961 1.0325552 3.019971e-03 0.086819657 0.04025943
## 188 0.9519247 0.9598896 -3.456604e-03 -0.075255224 0.03930802
## 189 0.7724009 0.8040986 -1.680612e-02 -0.062770082 0.03296851
## 190 1.3197105 1.2558315 1.830616e-02 0.082349803 0.05057002
## 191 1.0110921 1.0093020 6.624379e-04 0.065878666 0.03948089
## 192 1.0285026 1.0238683 1.666864e-03 0.065576268 0.03851722
## 193 0.9826554 0.9853214 -9.657406e-04 -0.059558016 0.03980201
## 194 1.1592477 1.1320794 7.739651e-03 0.061121697 0.04575546
## 195 0.8010880 0.8257080 -9.754878e-03 -0.042621095 0.03643795
## 196 0.7662214 0.7938745 -1.121176e-02 -0.039846567 0.03207859
## 197 0.9047770 0.9175065 -4.061096e-03 -0.041752394 0.03503640
## 198 1.1256718 1.1056356 4.977126e-03 0.048185420 0.04505977
## 199 0.9966957 0.9973924 -3.287174e-04 -0.111657535 0.03802788
## 200 0.8536291 0.8803145 -1.459632e-02 -0.095683178 0.03380074
## 201 0.9574297 0.9646779 -3.499411e-03 -0.086743807 0.03616232
## 202 0.9260636 0.9380343 -5.925032e-03 -0.081609088 0.03561581
## 203 1.0905567 1.0744538 6.720053e-03 0.088563607 0.03928644
## 204 0.8045402 0.8306641 -1.433345e-02 -0.064446262 0.03175723
## 205 0.9700638 0.9745508 -2.001177e-03 -0.070499711 0.03913157
## 206 0.9127604 0.9251657 -5.858009e-03 -0.066639292 0.03719396
## 207 1.1755743 1.1472338 9.613246e-03 0.069414566 0.04529774
## 208 1.1549239 1.1303204 8.468377e-03 0.068075794 0.04335410
## 209 1.0359522 1.0307714 1.960851e-03 0.060916147 0.03802209
## 210 0.9223366 0.9322631 -4.066513e-03 -0.051995796 0.03692567
## 211 1.2482097 1.2087855 1.116364e-02 0.060219236 0.04790470
## 212 1.1191380 1.1020692 5.212651e-03 0.052489743 0.04768691
## 213 0.7688003 0.7925667 -1.029532e-02 -0.036706448 0.03140234
## 214 1.1567656 1.1343592 6.035457e-03 0.047631065 0.04247405
## 215 0.8494000 0.8657366 -5.772252e-03 -0.034814239 0.03459598
## 216 1.0388279 1.0306363 3.723137e-03 0.111672285 0.03971220
## 217 1.0864905 1.0702907 6.939339e-03 0.095940087 0.04026297
## 218 0.9487914 0.9572126 -4.076862e-03 -0.083017653 0.03647226
## 219 1.0264081 1.0219497 1.974175e-03 0.083972930 0.03749857
## 220 0.9444546 0.9528968 -3.973042e-03 -0.073698791 0.03655891
## 221 0.7591114 0.7883766 -1.658238e-02 -0.056853709 0.03176776
## 222 0.7880819 0.8145976 -1.446224e-02 -0.058491964 0.03286443
## 223 0.8934322 0.9070519 -6.047474e-03 -0.054710797 0.03594073
## 224 1.1972604 1.1651520 1.069406e-02 0.069954544 0.04484782
## 225 0.9432830 0.9508004 -3.124137e-03 -0.056014653 0.03519599
## 226 1.0789701 1.0675051 4.038354e-03 0.059352902 0.04243166
## 227 1.1588883 1.1352763 7.207728e-03 0.056380613 0.04515030
## 228 0.9221373 0.9315448 -3.483706e-03 -0.044229447 0.04045053
## 229 0.7238649 0.7506240 -1.233352e-02 -0.034655423 0.02984550
## 230 1.0976841 1.0844006 3.777979e-03 0.045391201 0.04074676
## 231 1.2891453 1.2445241 1.047564e-02 0.049890362 0.04990836
## 232 0.9410431 0.9505703 -4.720899e-03 -0.082729115 0.03601145
## 233 1.0356097 1.0295495 2.723811e-03 0.086693202 0.03950516
## 234 1.1599341 1.1312573 1.142083e-02 0.090249944 0.04180217
## 235 0.9710733 0.9753987 -2.007467e-03 -0.073244063 0.03768615
## 236 0.9994453 0.9995268 -3.587456e-05 -0.069983434 0.04056015
## 237 0.7759061 0.8024295 -1.490937e-02 -0.055941712 0.03260184
## 238 1.0238151 1.0205510 1.293550e-03 0.059765612 0.04072319
## 239 1.3192836 1.2654652 1.662966e-02 0.073766525 0.04905251
## 240 1.0099830 1.0086438 5.309931e-04 0.057693366 0.03760052
## 241 1.3226437 1.2697149 1.560952e-02 0.068541558 0.05082513
## 242 0.7499214 0.7742202 -1.163801e-02 -0.037330903 0.03100814
## 243 0.7275294 0.7529270 -1.216402e-02 -0.034708547 0.03292498
## 244 0.8282414 0.8460574 -7.365599e-03 -0.037924893 0.03387728
## 245 1.1742217 1.1503718 6.511743e-03 0.046751538 0.04353051
## 246 0.9888767 0.9901774 -4.070994e-04 -0.038539151 0.03998851
## 247 0.7796663 0.8036597 -1.470225e-02 -0.055984578 0.03010678
## 248 0.9591317 0.9641873 -2.531385e-03 -0.063636364 0.03478439
## 249 0.9242105 0.9328358 -4.452203e-03 -0.057950056 0.03518766
## 250 0.9148524 0.9239390 -4.683925e-03 -0.053518322 0.03660446
## 251 0.9910424 0.9920635 -4.865350e-04 -0.057217214 0.03935141
## 252 0.7469197 0.7668712 -1.192049e-02 -0.037153305 0.02987549
## 253 1.1331276 1.1179911 6.004436e-03 0.053914532 0.04230921
## 254 1.2863621 1.2504466 1.249822e-02 0.059171365 0.04551000
## 255 0.9478044 0.9526675 -2.225316e-03 -0.042544896 0.03723053
## 256 1.1465201 1.1312217 5.494737e-03 0.045124263 0.04423282
## 257 1.1888432 1.1688669 6.753730e-03 0.044576605 0.04990262
## 258 1.1824675 1.1634671 6.353705e-03 0.043145653 0.04548292
## 259 0.9706320 0.9731069 -9.460624e-04 -0.032717236 0.03595023
## 260 0.8574777 0.8680556 -4.442365e-03 -0.027950850 0.03422594
## 261 1.0343717 1.0299924 2.046081e-03 0.065772092 0.03763633
## 262 1.0910534 1.0795429 5.088958e-03 0.064886189 0.04124539
## 263 0.9498404 0.9551585 -2.665127e-03 -0.053530136 0.03832802
## 264 0.9949916 0.9955425 -2.637316e-04 -0.055553415 0.03999732
## 265 1.3668520 1.3198376 1.578402e-02 0.061867027 0.05207909
## 266 1.0629698 1.0564901 2.774503e-03 0.049294291 0.04049601
## 267 1.1087323 1.0972550 4.684192e-03 0.050236306 0.04044822
## 268 1.0195396 1.0176863 8.025265e-04 0.043972764 0.04028302
## 269 1.0818889 1.0741571 2.996981e-03 0.041456621 0.04254171
## 270 0.6404379 0.6610453 -1.308371e-02 -0.024391408 0.02858510
## 271 1.1833529 1.1651933 6.196966e-03 0.041806579 0.04613627
## 272 0.5457628 0.5670113 -1.470093e-02 -0.018444695 0.02121696
## 273 1.1467923 1.1333663 4.333704e-03 0.035308681 0.04526144
## 274 1.1973325 1.1718268 1.037684e-02 0.066730367 0.04311514
## 275 1.2354138 1.2059731 1.155567e-02 0.064033652 0.04660252
## 276 0.8062216 0.8228686 -9.892754e-03 -0.043503997 0.03183702
## 277 0.6896286 0.7098312 -1.351717e-02 -0.031524017 0.02697297
## 278 0.7500825 0.7675972 -1.077461e-02 -0.033932223 0.02833421
## 279 0.8949801 0.9035104 -4.386827e-03 -0.039191424 0.03207421
## 280 1.1482591 1.1337530 5.728918e-03 0.046412614 0.04321728
## 281 0.8894196 0.8974982 -3.910296e-03 -0.032817646 0.03423037
## 282 1.1601145 1.1455691 5.304024e-03 0.040047431 0.04770464
## 283 1.6346072 1.5576998 1.974850e-02 0.052951484 0.05939626
## 284 1.0573372 1.0527978 1.692121e-03 0.032446846 0.03793737
## 285 1.1615452 1.1478421 4.534801e-03 0.033879818 0.04414394
## 286 1.1358358 1.1210044 6.366513e-03 0.056025105 0.04529407
## 287 1.0106042 1.0095626 5.008553e-04 0.050238496 0.04084110
## 288 1.0961700 1.0873546 3.816249e-03 0.045452985 0.04320233
## 289 0.6146032 0.6353681 -1.585370e-02 -0.026436809 0.02452253
## 290 1.1543679 1.1397994 5.960615e-03 0.046536210 0.04230711
## 291 0.8662147 0.8755860 -4.997515e-03 -0.033738135 0.03489796
## 292 0.9233016 0.9286148 -2.553888e-03 -0.031960543 0.03703189
## 293 1.1177983 1.1083198 3.701347e-03 0.036468942 0.04825771
## 294 1.0806772 1.0744706 2.473057e-03 0.034379029 0.04283829
## 295 1.0749782 1.0694933 2.088690e-03 0.031024893 0.04029605
## 296 1.0392157 1.0364842 1.049497e-03 0.028795614 0.04167867
## 297 1.0756285 1.0683165 3.324095e-03 0.049569426 0.04539099
## 298 0.4405447 0.4607810 -2.188398e-02 -0.017959584 0.01922808
## 299 1.2157641 1.1958703 7.911426e-03 0.046395759 0.04847630
## 300 0.8894321 0.8970095 -4.079371e-03 -0.034148387 0.03496935
## 301 0.6016136 0.6197611 -1.418900e-02 -0.022266766 0.02594364
## 302 1.0734066 1.0681063 2.263559e-03 0.034277062 0.04473631
## 303 1.2998841 1.2748056 8.723443e-03 0.039112232 0.05829765
## 304 1.1332025 1.1235198 3.810624e-03 0.033518946 0.04704612
## 305 0.8105096 0.8200979 -5.023169e-03 -0.022182397 0.03245306
## 306 0.4316438 0.4470672 -1.477606e-02 -0.011581026 0.01888119
## 307 1.2149000 1.1951239 7.883309e-03 0.046381279 0.04993264
## 308 0.6520541 0.6701245 -1.326396e-02 -0.025886763 0.02719763
## 309 1.3580661 1.3223647 1.271102e-02 0.050124773 0.05161451
## 310 1.0170919 1.0158313 5.880981e-04 0.036339796 0.04257537
## 311 0.5705630 0.5876476 -1.364300e-02 -0.018777342 0.02464296
## 312 0.4119801 0.4286143 -1.805630e-02 -0.013093728 0.01962474
## 313 0.7821070 0.7932730 -6.348831e-03 -0.023564616 0.03325798
## 314 1.1103687 1.1029310 2.861046e-03 0.029704290 0.04369869
## 315 1.2181912 1.2025012 5.387048e-03 0.031016140 0.05084781
## 316 0.4582770 0.4748428 -1.761270e-02 -0.015408485 0.01892767
## 317 1.6409404 1.5781272 1.901384e-02 0.050249412 0.06049707
## 318 1.6056595 1.5503533 1.705246e-02 0.046588567 0.06381742
## 319 0.5413745 0.5552008 -1.227492e-02 -0.014902360 0.02286639
## 320 0.5945166 0.6074227 -1.034756e-02 -0.015587466 0.02731492
## 321 0.6302189 0.6424052 -9.160123e-03 -0.016030036 0.02645172
## 322 1.1824917 1.1722871 3.994337e-03 0.026521167 0.04561690
## 323 0.4143627 0.4260395 -1.273558e-02 -0.009230813 0.01769328
## 324 0.7857090 0.7963923 -6.664471e-03 -0.025241115 0.02965674
## 325 1.0062862 1.0059125 1.823241e-04 0.030088557 0.04022285
## 326 1.4333086 1.4008938 1.101055e-02 0.037428515 0.05604757
## 327 0.7566325 0.7663305 -6.129709e-03 -0.019574607 0.03347566
## 328 1.1428694 1.1346506 3.432761e-03 0.028182538 0.04538487
## 329 2.3015674 2.1691546 2.697671e-02 0.048847416 0.08199481
## 330 0.4183669 0.4299966 -1.258749e-02 -0.009273581 0.01734713
## 331 0.6836610 0.6964839 -9.178298e-03 -0.020442367 0.02678359
## 332 0.7166092 0.7274738 -7.340032e-03 -0.019071887 0.02799071
## 333 1.1210864 1.1142593 2.939269e-03 0.027928150 0.04681061
## 334 0.8348268 0.8417367 -3.956629e-03 -0.020515076 0.03237951
## 335 1.2426012 1.2288283 5.177696e-03 0.027153213 0.04467166
## 336 1.1226313 1.1164702 2.522239e-03 0.023626999 0.04331663
## 337 0.9791127 0.9801199 -5.044373e-04 -0.024250195 0.04049413
## 338 0.7048507 0.7148729 -6.910113e-03 -0.016910032 0.03224802
## 339 0.7471744 0.7560436 -5.745876e-03 -0.017390154 0.03122893
## 340 0.6803251 0.6898296 -6.611898e-03 -0.014385718 0.02692768
## 341 1.0029304 1.0028079 5.728572e-05 0.020028059 0.04177741
## 342 0.4373973 0.4480086 -1.191484e-02 -0.009465819 0.02022086
## 343 0.7020545 0.7107152 -6.068465e-03 -0.014601869 0.02937276
## 344 0.5670349 0.5764187 -8.042143e-03 -0.010737027 0.02251304
## 345 0.9406598 0.9426848 -1.041477e-03 -0.016816820 0.03929427
## 346 1.5906784 1.5551293 1.112258e-02 0.030546319 0.07017414
## 347 0.3075010 0.3153182 -1.241600e-02 -0.005615320 0.01344641
## 348 0.6800000 0.6875688 -5.422410e-03 -0.011726626 0.03129250
## 349 1.0004490 1.0004335 7.640130e-06 0.017324262 0.03906445
## 350 0.3557600 0.3635835 -1.073437e-02 -0.006029788 0.01515180
## 351 1.3404255 1.3269639 4.991336e-03 0.019952172 0.05225961#Scatter plot between Phi and SCI weights
library(ggplot2)
p1=ggplot(confusion2,aes(y=phi, x=sci))
p1=p1+geom_point()+geom_smooth(se=FALSE,size=2)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.p1=p1+theme_bw()+ theme(panel.grid.minor = element_blank()) + ylab("Phi")+xlab("SCI")
p1=p1+theme(text=element_text(size=10))
p1## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
#select of top pairs based on different measures of association (10% of pairs with highest weights)
OR_10<-confusion2[confusion2$OR > quantile(confusion2$OR,0.9),c(1,2,10)] #1, 2, and 10 refers to columns for conditions 1, condition 2, and OR values
lift_10<-confusion2[confusion2$lift > quantile(confusion2$lift,0.9),c(1,2,11)] #selection of top pairs (those with weight>90th percentile of the distribution)
phi_10<-confusion2[confusion2$phi > quantile(confusion2$phi,0.9),c(1,2,12)]
sci_10<-confusion2[confusion2$sci > quantile(confusion2$sci,0.9),c(1,2,13)]
njf_10<-confusion2[confusion2$njf > quantile(confusion2$njf,0.9),c(1,2,14)]#network analysis based on normalised joint frequencies as weights using top pairs. Similar codes can be applied to other weights
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionNL=1:num_cond #generate a node list
njf_graph<-graph_from_data_frame(d=njf_10,vertices=NL,directed=FALSE)
closeness(njf_graph,normalized=TRUE) #when true, it calculates the inverse average distance to all reachable vertices## 1 2 3 4 5 6 7 8
## 0.2808989 0.2840909 0.2777778 NaN 0.3246753 0.2808989 0.3571429 0.3571429
## 9 10 11 12 13 14 15 16
## 0.2840909 0.3623188 0.2873563 0.3571429 0.3012048 0.2840909 0.3333333 0.3012048
## 17 18 19 20 21 22 23 24
## 0.3906250 0.4032258 0.3906250 0.4237288 0.4166667 0.3333333 0.3787879 0.4237288
## 25 26 27
## 0.3521127 0.3846154 0.3968254closeness(njf_graph,normalized=FALSE) #when false, it calculates the inverse of sum of distance to all reachable vertices## 1 2 3 4 5 6 7
## 0.01123596 0.01136364 0.01111111 NaN 0.01298701 0.01123596 0.01428571
## 8 9 10 11 12 13 14
## 0.01428571 0.01136364 0.01449275 0.01149425 0.01428571 0.01204819 0.01136364
## 15 16 17 18 19 20 21
## 0.01333333 0.01204819 0.01562500 0.01612903 0.01562500 0.01694915 0.01666667
## 22 23 24 25 26 27
## 0.01333333 0.01515152 0.01694915 0.01408451 0.01538462 0.01587302betweenness(njf_graph,normalized=TRUE)## 1 2 3 4 5 6 7
## 0.00000000 0.00000000 0.00000000 0.00000000 0.01435897 0.00000000 0.04717949
## 8 9 10 11 12 13 14
## 0.05743590 0.00000000 0.04307692 0.00000000 0.04871795 0.00000000 0.00000000
## 15 16 17 18 19 20 21
## 0.00000000 0.00000000 0.09128205 0.08923077 0.27589744 0.20000000 0.18358974
## 22 23 24 25 26 27
## 0.04000000 0.16461538 0.28205128 0.03538462 0.17589744 0.21128205comm=cluster_leiden(njf_graph,objective_function = "modularity",n_iterations = 3,resolution_parameter = 1)
plot(comm,njf_graph,vertex,layout=layout_with_fr,vertex.size=25,edge.arrow.size=5)## Warning in symbols(x = coords[, 1], y = coords[, 2], bg = vertex.color, :
## supplied color is neither numeric nor character
#extract all unique triads and their OE ratio
k<-combn(1:num_cond,3) #generate list of all unique triads of conditions (3 refers to the number of conditions)
n<-dim(k)[2] #number of unique triads
triads_OE<-matrix(0,n,6)
triads_OE[,1:3]<-t(k)
colnames(triads_OE)<-c("condition1","condition2","condition3","O","E","OE")
triads_OE<-as.data.frame(triads_OE)
for (r in 1:n){
x<-triads_OE[r,1]
y<-triads_OE[r,2]
z<-triads_OE[r,3]
triads_OE[r,"O"]<-sum(dataset[,x]==1 & dataset[,y]==1 & dataset[,z]==1)
nx<-sum(dataset[,x]==1)/num_sub
ny<-sum(dataset[,y]==1)/num_sub
nz<-sum(dataset[,z]==1)/num_sub
triads_OE[r,"E"]<-nx*ny*nz*num_sub
}
triads_OE$OE<-triads_OE$O/triads_OE$E
triads_OE## condition1 condition2 condition3 O E OE
## 1 1 2 3 268 287.34576660 0.9326743
## 2 1 2 4 231 226.41355980 1.0202569
## 3 1 2 5 188 183.56375250 1.0241673
## 4 1 2 6 180 189.70080930 0.9488626
## 5 1 2 7 179 156.71412900 1.1422072
## 6 1 2 8 173 166.35807540 1.0399255
## 7 1 2 9 148 144.54960570 1.0238700
## 8 1 2 10 132 123.06990690 1.0725611
## 9 1 2 11 110 122.30277480 0.8994072
## 10 1 2 12 115 115.28899560 0.9974933
## 11 1 2 13 70 78.90501600 0.8871426
## 12 1 2 14 77 73.86386220 1.0424584
## 13 1 2 15 76 66.30213150 1.1462678
## 14 1 2 16 56 58.74040080 0.9533473
## 15 1 2 17 60 51.06907980 1.1748792
## 16 1 2 18 52 50.63071860 1.0270445
## 17 1 2 19 37 35.72643780 1.0356476
## 18 1 2 20 27 35.39766690 0.7627621
## 19 1 2 21 43 34.08258330 1.2616415
## 20 1 2 22 26 30.35651310 0.8564884
## 21 1 2 23 21 24.21945630 0.8670715
## 22 1 2 24 20 22.13724060 0.9034550
## 23 1 2 25 21 20.93174730 1.0032607
## 24 1 2 26 20 17.20567710 1.1624070
## 25 1 2 27 12 15.78100320 0.7604079
## 26 1 3 4 194 180.38783160 1.0754606
## 27 1 3 5 161 146.24860500 1.1008652
## 28 1 3 6 138 151.13811060 0.9130722
## 29 1 3 7 148 124.85701800 1.1853559
## 30 1 3 8 133 132.54052680 1.0034667
## 31 1 3 9 109 115.16531940 0.9464655
## 32 1 3 10 92 98.05204980 0.9382772
## 33 1 3 11 91 97.44086160 0.9338998
## 34 1 3 12 99 91.85285520 1.0778108
## 35 1 3 13 68 62.86507200 1.0816817
## 36 1 3 14 54 58.84869240 0.9176075
## 37 1 3 15 47 52.82412300 0.8897450
## 38 1 3 16 47 46.79955360 1.0042831
## 39 1 3 17 36 40.68767160 0.8847889
## 40 1 3 18 32 40.33842120 0.7932884
## 41 1 3 19 29 28.46390760 1.0188341
## 42 1 3 20 23 28.20196980 0.8155459
## 43 1 3 21 26 27.15421860 0.9574939
## 44 1 3 22 25 24.18559020 1.0336733
## 45 1 3 23 19 19.29608460 0.9846557
## 46 1 3 24 15 17.63714520 0.8504778
## 47 1 3 25 12 16.67670660 0.7195665
## 48 1 3 26 21 13.70807820 1.5319434
## 49 1 3 27 8 12.57301440 0.6362834
## 50 1 4 5 120 115.23631500 1.0413384
## 51 1 4 6 117 119.08899180 0.9824586
## 52 1 4 7 89 98.38085400 0.9046476
## 53 1 4 8 106 104.43506040 1.0149848
## 54 1 4 9 85 90.74429820 0.9366980
## 55 1 4 10 80 77.25992940 1.0354656
## 56 1 4 11 80 76.77834480 1.0419605
## 57 1 4 12 66 72.37528560 0.9119135
## 58 1 4 13 52 49.53441600 1.0497752
## 59 1 4 14 45 46.36971720 0.9704610
## 60 1 4 15 38 41.62266900 0.9129640
## 61 1 4 16 29 36.87562080 0.7864274
## 62 1 4 17 24 32.05977480 0.7486016
## 63 1 4 18 38 31.78458360 1.1955481
## 64 1 4 19 18 22.42808280 0.8025653
## 65 1 4 20 21 22.22168940 0.9450227
## 66 1 4 21 40 21.39611580 1.8694982
## 67 1 4 22 15 19.05699060 0.7871127
## 68 1 4 23 13 15.20431380 0.8550205
## 69 1 4 24 14 13.89715560 1.0074004
## 70 1 4 25 8 13.14037980 0.6088104
## 71 1 4 26 8 10.80125460 0.7406547
## 72 1 4 27 8 9.90688320 0.8075194
## 73 1 5 6 104 96.55085250 1.0771526
## 74 1 5 7 89 79.76182500 1.1158220
## 75 1 5 8 85 84.67024500 1.0038946
## 76 1 5 9 65 73.57052250 0.8835060
## 77 1 5 10 52 62.63813250 0.8301652
## 78 1 5 11 55 62.24769000 0.8835669
## 79 1 5 12 44 58.67793000 0.7498560
## 80 1 5 13 38 40.15980000 0.9462199
## 81 1 5 14 35 37.59403500 0.9309988
## 82 1 5 15 44 33.74538750 1.3038819
## 83 1 5 16 25 29.89674000 0.8362116
## 84 1 5 17 19 25.99231500 0.7309853
## 85 1 5 18 25 25.76920500 0.9701502
## 86 1 5 19 18 18.18346500 0.9899103
## 87 1 5 20 10 18.01613250 0.5550581
## 88 1 5 21 19 17.34680250 1.0953027
## 89 1 5 22 12 15.45036750 0.7766806
## 90 1 5 23 14 12.32682750 1.1357342
## 91 1 5 24 8 11.26705500 0.7100347
## 92 1 5 25 13 10.65350250 1.2202560
## 93 1 5 26 12 8.75706750 1.3703217
## 94 1 5 27 6 8.03196000 0.7470157
## 95 1 6 7 71 82.42848900 0.8613527
## 96 1 6 8 69 87.50101140 0.7885623
## 97 1 6 9 76 76.03019370 0.9996029
## 98 1 6 10 61 64.73230290 0.9423425
## 99 1 6 11 64 64.32880680 0.9948887
## 100 1 6 12 56 60.63969960 0.9234874
## 101 1 6 13 37 41.50245600 0.8915135
## 102 1 6 14 44 38.85091020 1.1325346
## 103 1 6 15 32 34.87359150 0.9175998
## 104 1 6 16 24 30.89627280 0.7767927
## 105 1 6 17 24 26.86131180 0.8934783
## 106 1 6 18 34 26.63074260 1.2767199
## 107 1 6 19 22 18.79138980 1.1707490
## 108 1 6 20 16 18.61846290 0.8593620
## 109 1 6 21 16 17.92675530 0.8925207
## 110 1 6 22 15 15.96691710 0.9394425
## 111 1 6 23 14 12.73894830 1.0989918
## 112 1 6 24 10 11.64374460 0.8588302
## 113 1 6 25 11 11.00967930 0.9991208
## 114 1 6 26 20 9.04984110 2.2099836
## 115 1 6 27 5 8.30049120 0.6023740
## 116 1 7 8 64 72.28564200 0.8853764
## 117 1 7 9 56 62.80946100 0.8915854
## 118 1 7 10 68 53.47613700 1.2715952
## 119 1 7 11 58 53.14280400 1.0913989
## 120 1 7 12 51 50.09518800 1.0180619
## 121 1 7 13 49 34.28568000 1.4291681
## 122 1 7 14 33 32.09520600 1.0281909
## 123 1 7 15 32 28.80949500 1.1107449
## 124 1 7 16 19 25.52378400 0.7444037
## 125 1 7 17 21 22.19045400 0.9463529
## 126 1 7 18 30 21.99997800 1.3636377
## 127 1 7 19 16 15.52379400 1.0306759
## 128 1 7 20 16 15.38093700 1.0402487
## 129 1 7 21 9 14.80950900 0.6077176
## 130 1 7 22 13 13.19046300 0.9855606
## 131 1 7 23 14 10.52379900 1.3303181
## 132 1 7 24 6 9.61903800 0.6237630
## 133 1 7 25 13 9.09522900 1.4293208
## 134 1 7 26 7 7.47618300 0.9363067
## 135 1 7 27 3 6.85713600 0.4375004
## 136 1 8 9 66 66.67465860 0.9898813
## 137 1 8 10 65 56.76697620 1.1450319
## 138 1 8 11 54 56.41313040 0.9572240
## 139 1 8 12 47 53.17796880 0.8838247
## 140 1 8 13 34 36.39556800 0.9341797
## 141 1 8 14 45 34.07029560 1.3207986
## 142 1 8 15 22 30.58238700 0.7193683
## 143 1 8 16 28 27.09447840 1.0334209
## 144 1 8 17 29 23.55602040 1.2311078
## 145 1 8 18 28 23.35382280 1.1989472
## 146 1 8 19 18 16.47910440 1.0922924
## 147 1 8 20 14 16.32745620 0.8574514
## 148 1 8 21 16 15.72086340 1.0177558
## 149 1 8 22 11 14.00218380 0.7855917
## 150 1 8 23 16 11.17141740 1.4322265
## 151 1 8 24 7 10.21097880 0.6855366
## 152 1 8 25 15 9.65493540 1.5536096
## 153 1 8 26 15 7.93625580 1.8900600
## 154 1 8 27 7 7.27911360 0.9616556
## 155 1 9 10 42 49.32519210 0.8514919
## 156 1 9 11 51 49.01773320 1.0404398
## 157 1 9 12 38 46.20668040 0.8223919
## 158 1 9 13 21 31.62434400 0.6640454
## 159 1 9 14 28 29.60389980 0.9458213
## 160 1 9 15 27 26.57323350 1.0160600
## 161 1 9 16 20 23.54256720 0.8495250
## 162 1 9 17 24 20.46797820 1.1725633
## 163 1 9 18 19 20.29228740 0.9363163
## 164 1 9 19 15 14.31880020 1.0475738
## 165 1 9 20 12 14.18703210 0.8458429
## 166 1 9 21 12 13.65995970 0.8784799
## 167 1 9 22 11 12.16658790 0.9041154
## 168 1 9 23 12 9.70691670 1.2362319
## 169 1 9 24 7 8.87238540 0.7889648
## 170 1 9 25 7 8.38923570 0.8344026
## 171 1 9 26 14 6.89586390 2.0302025
## 172 1 9 27 6 6.32486880 0.9486363
## 173 1 10 11 35 41.73382440 0.8386483
## 174 1 10 12 29 39.34048680 0.7371541
## 175 1 10 13 24 26.92504800 0.8913633
## 176 1 10 14 29 25.20483660 1.1505728
## 177 1 10 15 19 22.62451950 0.8397968
## 178 1 10 16 17 20.04420240 0.8481255
## 179 1 10 17 18 17.42648940 1.0329103
## 180 1 10 18 12 17.27690580 0.6945688
## 181 1 10 19 14 12.19106340 1.1483822
## 182 1 10 20 16 12.07887570 1.3246266
## 183 1 10 21 11 11.63012490 0.9458196
## 184 1 10 22 6 10.35866430 0.5792253
## 185 1 10 23 11 8.26449390 1.3309950
## 186 1 10 24 9 7.55397180 1.1914262
## 187 1 10 25 8 7.14261690 1.1200377
## 188 1 10 26 11 5.87115630 1.8735662
## 189 1 10 27 1 5.38500960 0.1857007
## 190 1 11 12 39 39.09526560 0.9975632
## 191 1 11 13 28 26.75721600 1.0464467
## 192 1 11 14 21 25.04772720 0.8383994
## 193 1 11 15 20 22.48349400 0.8895415
## 194 1 11 16 18 19.91926080 0.9036480
## 195 1 11 17 11 17.31786480 0.6351822
## 196 1 11 18 14 17.16921360 0.8154130
## 197 1 11 19 5 12.11507280 0.4127090
## 198 1 11 20 11 12.00358440 0.9163929
## 199 1 11 21 14 11.55763080 1.2113209
## 200 1 11 22 11 10.29409560 1.0685737
## 201 1 11 23 8 8.21297880 0.9740680
## 202 1 11 24 4 7.50688560 0.5328441
## 203 1 11 25 5 7.09809480 0.7044144
## 204 1 11 26 8 5.83455960 1.3711403
## 205 1 11 27 7 5.35144320 1.3080584
## 206 1 12 13 22 25.22275200 0.8722284
## 207 1 12 14 26 23.61129840 1.1011677
## 208 1 12 15 16 21.19411800 0.7549264
## 209 1 12 16 19 18.77693760 1.0118796
## 210 1 12 17 15 16.32472560 0.9188516
## 211 1 12 18 16 16.18459920 0.9885941
## 212 1 12 19 11 11.42030160 0.9631970
## 213 1 12 20 21 11.31520680 1.8559095
## 214 1 12 21 12 10.89482760 1.1014401
## 215 1 12 22 12 9.70375320 1.2366349
## 216 1 12 23 6 7.74198360 0.7749952
## 217 1 12 24 7 7.07638320 0.9892059
## 218 1 12 25 3 6.69103560 0.4483611
## 219 1 12 26 7 5.49996120 1.2727363
## 220 1 12 27 6 5.04455040 1.1894023
## 221 1 13 14 10 16.15982400 0.6188186
## 222 1 13 15 14 14.50548000 0.9651525
## 223 1 13 16 12 12.85113600 0.9337696
## 224 1 13 17 13 11.17281600 1.1635384
## 225 1 13 18 13 11.07691200 1.1736123
## 226 1 13 19 8 7.81617600 1.0235184
## 227 1 13 20 8 7.74424800 1.0330248
## 228 1 13 21 5 7.45653600 0.6705527
## 229 1 13 22 4 6.64135200 0.6022870
## 230 1 13 23 7 5.29869600 1.3210798
## 231 1 13 24 5 4.84315200 1.0323855
## 232 1 13 25 6 4.57941600 1.3102107
## 233 1 13 26 3 3.76423200 0.7969753
## 234 1 13 27 2 3.45254400 0.5792830
## 235 1 14 15 15 13.57874100 1.1046680
## 236 1 14 16 13 12.03009120 1.0806236
## 237 1 14 17 10 10.45899720 0.9561146
## 238 1 14 18 15 10.36922040 1.4465890
## 239 1 14 19 10 7.31680920 1.3667160
## 240 1 14 20 8 7.24947660 1.1035279
## 241 1 14 21 10 6.98014620 1.4326347
## 242 1 14 22 6 6.21704340 0.9650890
## 243 1 14 23 5 4.96016820 1.0080303
## 244 1 14 24 4 4.53372840 0.8822761
## 245 1 14 25 5 4.28684220 1.1663597
## 246 1 14 26 3 3.52373940 0.8513683
## 247 1 14 27 2 3.23196480 0.6188186
## 248 1 15 16 10 10.79852400 0.9260525
## 249 1 15 17 11 9.38826900 1.1716750
## 250 1 15 18 6 9.30768300 0.6446287
## 251 1 15 19 8 6.56775900 1.2180715
## 252 1 15 20 6 6.50731950 0.9220386
## 253 1 15 21 7 6.26556150 1.1172183
## 254 1 15 22 7 5.58058050 1.2543498
## 255 1 15 23 2 4.45237650 0.4491983
## 256 1 15 24 4 4.06959300 0.9828993
## 257 1 15 25 4 3.84798150 1.0395060
## 258 1 15 26 3 3.16300050 0.9484665
## 259 1 15 27 3 2.90109600 1.0340919
## 260 1 16 17 11 8.31754080 1.3225063
## 261 1 16 18 9 8.24614560 1.0914190
## 262 1 16 19 8 5.81870880 1.3748755
## 263 1 16 20 6 5.76516240 1.0407339
## 264 1 16 21 10 5.55097680 1.8014847
## 265 1 16 22 5 4.94411760 1.0113028
## 266 1 16 23 3 3.94458480 0.7605363
## 267 1 16 24 5 3.60545760 1.3867865
## 268 1 16 25 3 3.40912080 0.8799923
## 269 1 16 26 4 2.80226160 1.4274185
## 270 1 16 27 4 2.57022720 1.5562827
## 271 1 17 18 6 7.16922360 0.8369107
## 272 1 17 19 2 5.05880280 0.3953505
## 273 1 17 20 3 5.01224940 0.5985337
## 274 1 17 21 3 4.82603580 0.6216282
## 275 1 17 22 2 4.29843060 0.4652861
## 276 1 17 23 2 3.42943380 0.5831866
## 277 1 17 24 6 3.13459560 1.9141225
## 278 1 17 25 5 2.96389980 1.6869666
## 279 1 17 26 4 2.43629460 1.6418376
## 280 1 17 27 0 2.23456320 0.0000000
## 281 1 18 19 7 5.01537960 1.3957069
## 282 1 18 20 3 4.96922580 0.6037158
## 283 1 18 21 8 4.78461060 1.6720274
## 284 1 18 22 4 4.26153420 0.9386291
## 285 1 18 23 3 3.39999660 0.8823538
## 286 1 18 24 0 3.10768920 0.0000000
## 287 1 18 25 5 2.93845860 1.7015724
## 288 1 18 26 2 2.41538220 0.8280263
## 289 1 18 27 3 2.21538240 1.3541680
## 290 1 19 20 1 3.50642340 0.2851909
## 291 1 19 21 3 3.37615380 0.8885851
## 292 1 19 22 4 3.00705660 1.3302044
## 293 1 19 23 1 2.39913180 0.4168175
## 294 1 19 24 3 2.19287160 1.3680692
## 295 1 19 25 2 2.07345780 0.9645723
## 296 1 19 26 3 1.70436060 1.7601909
## 297 1 19 27 1 1.56323520 0.6396990
## 298 1 20 21 2 3.34508490 0.5978921
## 299 1 20 22 2 2.97938430 0.6712796
## 300 1 20 23 1 2.37705390 0.4206888
## 301 1 20 24 0 2.17269180 0.0000000
## 302 1 20 25 1 2.05437690 0.4867656
## 303 1 20 26 5 1.68867630 2.9608990
## 304 1 20 27 0 1.54884960 0.0000000
## 305 1 21 22 2 2.86869510 0.6971811
## 306 1 21 23 1 2.28874230 0.4369212
## 307 1 21 24 2 2.09197260 0.9560355
## 308 1 21 25 3 1.97805330 1.5166427
## 309 1 21 26 1 1.62593910 0.6150292
## 310 1 21 27 0 1.49130720 0.0000000
## 311 1 22 23 4 2.03852610 1.9622020
## 312 1 22 24 1 1.86326820 0.5366914
## 313 1 22 25 1 1.76180310 0.5676003
## 314 1 22 26 2 1.44818370 1.3810403
## 315 1 22 27 1 1.32827040 0.7528588
## 316 1 23 24 0 1.48657860 0.0000000
## 317 1 23 25 1 1.40562630 0.7114266
## 318 1 23 26 1 1.15541010 0.8654936
## 319 1 23 27 1 1.05973920 0.9436284
## 320 1 24 25 3 1.28478060 2.3350290
## 321 1 24 26 0 1.05607620 0.0000000
## 322 1 24 27 0 0.96863040 0.0000000
## 323 1 25 26 1 0.99856710 1.0014350
## 324 1 25 27 1 0.91588320 1.0918423
## 325 1 26 27 1 0.75284640 1.3282922
## 326 2 3 4 165 178.27518132 0.9255354
## 327 2 3 5 132 144.53578350 0.9132687
## 328 2 3 6 143 149.36802462 0.9573669
## 329 2 3 7 128 123.39472860 1.0373215
## 330 2 3 8 116 130.98825036 0.8855756
## 331 2 3 9 90 113.81653638 0.7907463
## 332 2 3 10 85 96.90369246 0.8771596
## 333 2 3 11 84 96.29966232 0.8722772
## 334 2 3 12 85 90.77710104 0.9363595
## 335 2 3 13 64 62.12881440 1.0301178
## 336 2 3 14 53 58.15947348 0.9112875
## 337 2 3 15 53 52.20546210 1.0152194
## 338 2 3 16 47 46.25145072 1.0161843
## 339 2 3 17 27 40.21114932 0.6714556
## 340 2 3 18 31 39.86598924 0.7776052
## 341 2 3 19 34 28.13054652 1.2086505
## 342 2 3 20 28 27.87167646 1.0046041
## 343 2 3 21 26 26.83619622 0.9688407
## 344 2 3 22 22 23.90233554 0.9204121
## 345 2 3 23 24 19.07009442 1.2585150
## 346 2 3 24 14 17.43058404 0.8031859
## 347 2 3 25 16 16.48139382 0.9707917
## 348 2 3 26 10 13.54753314 0.7381418
## 349 2 3 27 11 12.42576288 0.8852575
## 350 2 4 5 112 113.88670050 0.9834335
## 351 2 4 6 106 117.69425586 0.9006387
## 352 2 4 7 94 97.22864580 0.9667933
## 353 2 4 8 97 103.21194708 0.9398137
## 354 2 4 9 82 89.68152714 0.9143466
## 355 2 4 10 73 76.35508338 0.9560595
## 356 2 4 11 69 75.87913896 0.9093408
## 357 2 4 12 71 71.52764712 0.9926232
## 358 2 4 13 55 48.95428320 1.1234972
## 359 2 4 14 47 45.82664844 1.0256041
## 360 2 4 15 42 41.13519630 1.0210234
## 361 2 4 16 37 36.44374416 1.0152634
## 362 2 4 17 27 31.68429996 0.8521571
## 363 2 4 18 34 31.41233172 1.0823775
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## 1831 8 11 22 4 4.69262376 0.8524016
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## 1838 8 12 14 10 10.76334864 0.9290789
## 1839 8 12 15 14 9.66146280 1.4490559
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## 1841 8 12 17 7 7.44172176 0.9406425
## 1842 8 12 18 4 7.37784432 0.5421638
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## 1850 8 12 26 5 2.50718952 1.9942649
## 1851 8 12 27 3 2.29958784 1.3045816
## 1852 8 13 14 5 7.36655040 0.6787437
## 1853 8 13 15 3 6.61240800 0.4536925
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## 1858 8 13 20 4 3.53026080 1.1330608
## 1859 8 13 21 4 3.39910560 1.1767802
## 1860 8 13 22 5 3.02749920 1.6515281
## 1861 8 13 23 6 2.41544160 2.4840178
## 1862 8 13 24 1 2.20777920 0.4529438
## 1863 8 13 25 4 2.08755360 1.9161185
## 1864 8 13 26 1 1.71594720 0.5827685
## 1865 8 13 27 1 1.57386240 0.6353796
## 1866 8 14 15 3 6.18994860 0.4846567
## 1867 8 14 16 1 5.48398752 0.1823491
## 1868 8 14 17 5 4.76779512 1.0487028
## 1869 8 14 18 7 4.72686984 1.4808954
## 1870 8 14 19 5 3.33541032 1.4990659
## 1871 8 14 20 4 3.30471636 1.2103913
## 1872 8 14 21 3 3.18194052 0.9428209
## 1873 8 14 22 1 2.83407564 0.3528487
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## 1876 8 14 25 2 1.95418212 1.0234461
## 1877 8 14 26 2 1.60631724 1.2450841
## 1878 8 14 27 2 1.47331008 1.3574875
## 1879 8 15 16 6 4.92257040 1.2188754
## 1880 8 15 17 4 4.27969740 0.9346455
## 1881 8 15 18 1 4.24296180 0.2356844
## 1882 8 15 19 2 2.99395140 0.6680135
## 1883 8 15 20 2 2.96639970 0.6742180
## 1884 8 15 21 1 2.85619290 0.3501164
## 1885 8 15 22 5 2.54394030 1.9654549
## 1886 8 15 23 3 2.02964190 1.4780933
## 1887 8 15 24 2 1.85514780 1.0780812
## 1888 8 15 25 1 1.75412490 0.5700848
## 1889 8 15 26 2 1.44187230 1.3870854
## 1890 8 15 27 3 1.32248160 2.2684626
## 1891 8 16 17 4 3.79159968 1.0549637
## 1892 8 16 18 2 3.75905376 0.5320488
## 1893 8 16 19 1 2.65249248 0.3770039
## 1894 8 16 20 2 2.62808304 0.7610110
## 1895 8 16 21 4 2.53044528 1.5807495
## 1896 8 16 22 2 2.25380496 0.8873882
## 1897 8 16 23 2 1.79816208 1.1122468
## 1898 8 16 24 2 1.64356896 1.2168641
## 1899 8 16 25 1 1.55406768 0.6434726
## 1900 8 16 26 3 1.27742736 2.3484701
## 1901 8 16 27 1 1.17165312 0.8534949
## 1902 8 17 18 3 3.26813256 0.9179554
## 1903 8 17 19 1 2.30608488 0.4336354
## 1904 8 17 20 2 2.28486324 0.8753259
## 1905 8 17 21 0 2.19997668 0.0000000
## 1906 8 17 22 0 1.95946476 0.0000000
## 1907 8 17 23 2 1.56332748 1.2793225
## 1908 8 17 24 1 1.42892376 0.6998274
## 1909 8 17 25 3 1.35111108 2.2203948
## 1910 8 17 26 2 1.11059916 1.8008297
## 1911 8 17 27 2 1.01863872 1.9634047
## 1912 8 18 19 4 2.28629016 1.7495592
## 1913 8 18 20 2 2.26525068 0.8829045
## 1914 8 18 21 3 2.18109276 1.3754573
## 1915 8 18 22 1 1.94264532 0.5147620
## 1916 8 18 23 0 1.54990836 0.0000000
## 1917 8 18 24 1 1.41665832 0.7058865
## 1918 8 18 25 0 1.33951356 0.0000000
## 1919 8 18 26 1 1.10106612 0.9082107
## 1920 8 18 27 3 1.00989504 2.9706057
## 1921 8 19 20 1 1.59842364 0.6256164
## 1922 8 19 21 2 1.53903948 1.2995118
## 1923 8 19 22 1 1.37078436 0.7295093
## 1924 8 19 23 0 1.09365828 0.0000000
## 1925 8 19 24 0 0.99963336 0.0000000
## 1926 8 19 25 1 0.94519788 1.0579795
## 1927 8 19 26 2 0.77694276 2.5741922
## 1928 8 19 27 0 0.71260992 0.0000000
## 1929 8 20 21 2 1.52487654 1.3115816
## 1930 8 20 22 0 1.35816978 0.0000000
## 1931 8 20 23 2 1.08359394 1.8457098
## 1932 8 20 24 1 0.99043428 1.0096581
## 1933 8 20 25 1 0.93649974 1.0678060
## 1934 8 20 26 2 0.76979298 2.5981011
## 1935 8 20 27 1 0.70605216 1.4163260
## 1936 8 21 22 1 1.30771146 0.7646947
## 1937 8 21 23 0 1.04333658 0.0000000
## 1938 8 21 24 1 0.95363796 1.0486160
## 1939 8 21 25 0 0.90170718 0.0000000
## 1940 8 21 26 2 0.74119386 2.6983494
## 1941 8 21 27 1 0.67982112 1.4709752
## 1942 8 22 23 0 0.92927406 0.0000000
## 1943 8 22 24 0 0.84938172 0.0000000
## 1944 8 22 25 0 0.80312826 0.0000000
## 1945 8 22 26 1 0.66016302 1.5147774
## 1946 8 22 27 1 0.60549984 1.6515281
## 1947 8 23 24 1 0.67766556 1.4756542
## 1948 8 23 25 1 0.64076298 1.5606395
## 1949 8 23 26 1 0.52670046 1.8986124
## 1950 8 23 27 1 0.48308832 2.0700149
## 1951 8 24 25 0 0.58567476 0.0000000
## 1952 8 24 26 0 0.48141852 0.0000000
## 1953 8 24 27 2 0.44155584 4.5294385
## 1954 8 25 26 1 0.45520266 2.1968237
## 1955 8 25 27 0 0.41751072 0.0000000
## 1956 8 26 27 0 0.34318944 0.0000000
## 1957 9 10 11 16 16.53060492 0.9679017
## 1958 9 10 12 10 15.58261324 0.6417409
## 1959 9 10 13 7 10.66490640 0.6563583
## 1960 9 10 14 10 9.98353738 1.0016490
## 1961 9 10 15 5 8.96148385 0.5579433
## 1962 9 10 16 6 7.93943032 0.7557217
## 1963 9 10 17 7 6.90256442 1.0141159
## 1964 9 10 18 3 6.84331494 0.4383840
## 1965 9 10 19 7 4.82883262 1.4496257
## 1966 9 10 20 4 4.78439551 0.8360513
## 1967 9 10 21 4 4.60664707 0.8683105
## 1968 9 10 22 5 4.10302649 1.2186127
## 1969 9 10 23 5 3.27353377 1.5274014
## 1970 9 10 24 2 2.99209874 0.6684271
## 1971 9 10 25 2 2.82916267 0.7069229
## 1972 9 10 26 5 2.32554209 2.1500363
## 1973 9 10 27 1 2.13298128 0.4688274
## 1974 9 11 12 15 15.48548208 0.9686492
## 1975 9 11 13 14 10.59842880 1.3209505
## 1976 9 11 14 9 9.92130696 0.9071385
## 1977 9 11 15 10 8.90562420 1.1228859
## 1978 9 11 16 4 7.88994144 0.5069746
## 1979 9 11 17 7 6.85953864 1.0204768
## 1980 9 11 18 5 6.80065848 0.7352229
## 1981 9 11 19 6 4.79873304 1.2503300
## 1982 9 11 20 5 4.75457292 1.0516192
## 1983 9 11 21 2 4.57793244 0.4368784
## 1984 9 11 22 4 4.07745108 0.9810050
## 1985 9 11 23 4 3.25312884 1.2295855
## 1986 9 11 24 1 2.97344808 0.3363099
## 1987 9 11 25 2 2.81152764 0.7113570
## 1988 9 11 26 0 2.31104628 0.0000000
## 1989 9 11 27 6 2.11968576 2.8306083
## 1990 9 12 13 9 9.99063360 0.9008438
## 1991 9 12 14 6 9.35234312 0.6415505
## 1992 9 12 15 5 8.39490740 0.5955992
## 1993 9 12 16 6 7.43747168 0.8067258
## 1994 9 12 17 9 6.46616008 1.3918616
## 1995 9 12 18 5 6.41065656 0.7799513
## 1996 9 12 19 5 4.52353688 1.1053298
## 1997 9 12 20 6 4.48190924 1.3387152
## 1998 9 12 21 5 4.31539868 1.1586415
## 1999 9 12 22 6 3.84361876 1.5610289
## 2000 9 12 23 1 3.06656948 0.3260973
## 2001 9 12 24 3 2.80292776 1.0703094
## 2002 9 12 25 2 2.65029308 0.7546335
## 2003 9 12 26 0 2.17851316 0.0000000
## 2004 9 12 27 1 1.99812672 0.5004688
## 2005 9 13 14 11 6.40084320 1.7185236
## 2006 9 13 15 10 5.74556400 1.7404732
## 2007 9 13 16 5 5.09028480 0.9822633
## 2008 9 13 17 3 4.42550880 0.6778882
## 2009 9 13 18 4 4.38752160 0.9116764
## 2010 9 13 19 3 3.09595680 0.9690058
## 2011 9 13 20 3 3.06746640 0.9780058
## 2012 9 13 21 6 2.95350480 2.0314848
## 2013 9 13 22 2 2.63061360 0.7602789
## 2014 9 13 23 2 2.09879280 0.9529288
## 2015 9 13 24 2 1.91835360 1.0425607
## 2016 9 13 25 1 1.81388880 0.5513017
## 2017 9 13 26 2 1.49099760 1.3413838
## 2018 9 13 27 1 1.36753920 0.7312405
## 2019 9 14 15 4 5.37848630 0.7437037
## 2020 9 14 16 6 4.76507216 1.2591625
## 2021 9 14 17 4 4.14276796 0.9655380
## 2022 9 14 18 2 4.10720772 0.4869488
## 2023 9 14 19 5 2.89815956 1.7252328
## 2024 9 14 20 1 2.87148938 0.3482513
## 2025 9 14 21 3 2.76480866 1.0850660
## 2026 9 14 22 3 2.46254662 1.2182510
## 2027 9 14 23 5 1.96470326 2.5449136
## 2028 9 14 24 2 1.79579212 1.1137147
## 2029 9 14 25 1 1.69800146 0.5889276
## 2030 9 14 26 2 1.39573942 1.4329322
## 2031 9 14 27 1 1.28016864 0.7811471
## 2032 9 15 16 5 4.27725320 1.1689745
## 2033 9 15 17 2 3.71865670 0.5378286
## 2034 9 15 18 0 3.68673690 0.0000000
## 2035 9 15 19 0 2.60146370 0.0000000
## 2036 9 15 20 3 2.57752385 1.1639078
## 2037 9 15 21 2 2.48176445 0.8058783
## 2038 9 15 22 2 2.21044615 0.9047947
## 2039 9 15 23 2 1.76356895 1.1340640
## 2040 9 15 24 2 1.61194990 1.2407334
## 2041 9 15 25 3 1.52417045 1.9682838
## 2042 9 15 26 2 1.25285215 1.5963576
## 2043 9 15 27 0 1.14911280 0.0000000
## 2044 9 16 17 3 3.29454544 0.9105960
## 2045 9 16 18 0 3.26626608 0.0000000
## 2046 9 16 19 4 2.30476784 1.7355327
## 2047 9 16 20 1 2.28355832 0.4379131
## 2048 9 16 21 3 2.19872024 1.3644301
## 2049 9 16 22 2 1.95834568 1.0212702
## 2050 9 16 23 0 1.56243464 0.0000000
## 2051 9 16 24 1 1.42810768 0.7002273
## 2052 9 16 25 3 1.35033944 2.2216636
## 2053 9 16 26 0 1.10996488 0.0000000
## 2054 9 16 27 2 1.01805696 1.9645266
## 2055 9 17 18 4 2.83970148 1.4085988
## 2056 9 17 19 2 2.00377204 0.9981175
## 2057 9 17 20 3 1.98533242 1.5110820
## 2058 9 17 21 1 1.91157394 0.5231291
## 2059 9 17 22 1 1.70259158 0.5873399
## 2060 9 17 23 1 1.35838534 0.7361681
## 2061 9 17 24 4 1.24160108 3.2216467
## 2062 9 17 25 1 1.17398914 0.8517966
## 2063 9 17 26 1 0.96500678 1.0362621
## 2064 9 17 27 0 0.88510176 0.0000000
## 2065 9 18 19 3 1.98657228 1.5101389
## 2066 9 18 20 1 1.96829094 0.5080550
## 2067 9 18 21 6 1.89516558 3.1659503
## 2068 9 18 22 1 1.68797706 0.5924251
## 2069 9 18 23 0 1.34672538 0.0000000
## 2070 9 18 24 0 1.23094356 0.0000000
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## 2853 19 21 26 1 0.15917602 6.2823533
## 2854 19 21 27 0 0.14599584 0.0000000
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## 2872 20 21 24 0 0.20291506 0.0000000
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## 2893 21 22 25 0 0.16454077 0.0000000
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## 2900 21 24 25 0 0.11999002 0.0000000
## 2901 21 24 26 1 0.09863054 10.1388475
## 2902 21 24 27 0 0.09046368 0.0000000
## 2903 21 25 26 0 0.09325957 0.0000000
## 2904 21 25 27 0 0.08553744 0.0000000
## 2905 21 26 27 1 0.07031088 14.2225499
## 2906 22 23 24 0 0.12365834 0.0000000
## 2907 22 23 25 1 0.11692447 8.5525297
## 2908 22 23 26 0 0.09611069 0.0000000
## 2909 22 23 27 0 0.08815248 0.0000000
## 2910 22 24 25 0 0.10687214 0.0000000
## 2911 22 24 26 0 0.08784778 0.0000000
## 2912 22 24 27 0 0.08057376 0.0000000
## 2913 22 25 26 0 0.08306399 0.0000000
## 2914 22 25 27 0 0.07618608 0.0000000
## 2915 22 26 27 0 0.06262416 0.0000000
## 2916 23 24 25 0 0.08526622 0.0000000
## 2917 23 24 26 0 0.07008794 0.0000000
## 2918 23 24 27 0 0.06428448 0.0000000
## 2919 23 25 26 0 0.06627127 0.0000000
## 2920 23 25 27 0 0.06078384 0.0000000
## 2921 23 26 27 0 0.04996368 0.0000000
## 2922 24 25 26 0 0.06057374 0.0000000
## 2923 24 25 27 0 0.05555808 0.0000000
## 2924 24 26 27 0 0.04566816 0.0000000
## 2925 25 26 27 0 0.04318128 0.0000000#Bayesian Poisson Gamma model to estimate posterior OE ratios
#the computation of the likelihood function required calculation of factorial of large numbers. To avoid computational problems, we calculated the logarithm of factorials
for (m in 1:n){
O<-triads_OE$O[m]
if (O>0){
triads_OE$log_factorial_O[m]<-sum(log(1:O))
}
else {
triads_OE$log_factorial_O[m]<-0
}
}
#the likelihood function for Poisson-Gamma model
PG <- function (theta){
O <- O
E <- E
log_factorial_O<-log_factorial_O
a = theta
b = theta
LL <- a * log (b) +
O * log (E) +
lgamma ( O + a ) -
lgamma ( a ) -
log_factorial_O -
( O + a ) * log ( b + num_sub )
return(-sum ( LL ))
}
O<-triads_OE$O
E<-triads_OE$E
log_factorial_O<-triads_OE$log_factorial_O
# set the initial value for parameters
theta <- 1
#R function to estimate parameter; lower shows the minimum value for estimated parameter
par.optim <- optim( par= 60 , PG ,lower=quantile(triads_OE[triads_OE$E<100,"E"],0.9), upper=Inf,method="L-BFGS-B")
triads_OE$Posterior_OE <- ( par.optim$par [1] + O ) / ( par.optim$par [1] + E )
triads_OE## condition1 condition2 condition3 O E OE
## 1 1 2 3 268 287.34576660 0.9326743
## 2 1 2 4 231 226.41355980 1.0202569
## 3 1 2 5 188 183.56375250 1.0241673
## 4 1 2 6 180 189.70080930 0.9488626
## 5 1 2 7 179 156.71412900 1.1422072
## 6 1 2 8 173 166.35807540 1.0399255
## 7 1 2 9 148 144.54960570 1.0238700
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## 1742 7 18 20 3 2.13393180 1.4058556
## 1743 7 18 21 0 2.05465260 0.0000000
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## 1787 8 9 11 16 22.34502072 0.7160432
## 1788 8 9 12 20 21.06358584 0.9495059
## 1789 8 9 13 14 14.41614240 0.9711336
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## 1798 8 9 22 3 5.54621034 0.5409099
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## 1804 8 10 11 9 19.02460824 0.4730715
## 1805 8 10 12 11 17.93359128 0.6133741
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## 1807 8 10 14 12 11.48977236 1.0444071
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## 1809 8 10 16 8 9.13726704 0.8755353
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## 1817 8 10 24 4 3.44352228 1.1616013
## 1818 8 10 25 2 3.25600374 0.6142499
## 1819 8 10 26 2 2.67640098 0.7472722
## 1820 8 10 27 3 2.45478816 1.2221014
## 1821 8 11 12 15 17.82180576 0.8416656
## 1822 8 11 13 16 12.19743360 1.3117514
## 1823 8 11 14 15 11.41815312 1.3136976
## 1824 8 11 15 10 10.24923240 0.9756828
## 1825 8 11 16 8 9.08031168 0.8810270
## 1826 8 11 17 7 7.89445008 0.8866989
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## 1828 8 11 19 2 5.52272688 0.3621399
## 1829 8 11 20 9 5.47190424 1.6447656
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## 1831 8 11 22 4 4.69262376 0.8524016
## 1832 8 11 23 4 3.74393448 1.0683948
## 1833 8 11 24 4 3.42205776 1.1688873
## 1834 8 11 25 2 3.23570808 0.6181027
## 1835 8 11 26 2 2.65971816 0.7519594
## 1836 8 11 27 4 2.43948672 1.6396892
## 1837 8 12 13 14 11.49793920 1.2176095
## 1838 8 12 14 10 10.76334864 0.9290789
## 1839 8 12 15 14 9.66146280 1.4490559
## 1840 8 12 16 6 8.55957696 0.7009692
## 1841 8 12 17 7 7.44172176 0.9406425
## 1842 8 12 18 4 7.37784432 0.5421638
## 1843 8 12 19 3 5.20601136 0.5762569
## 1844 8 12 20 5 5.15810328 0.9693486
## 1845 8 12 21 4 4.96647096 0.8054009
## 1846 8 12 22 6 4.42351272 1.3563881
## 1847 8 12 23 4 3.52922856 1.1333922
## 1848 8 12 24 0 3.22581072 0.0000000
## 1849 8 12 25 1 3.05014776 0.3278530
## 1850 8 12 26 5 2.50718952 1.9942649
## 1851 8 12 27 3 2.29958784 1.3045816
## 1852 8 13 14 5 7.36655040 0.6787437
## 1853 8 13 15 3 6.61240800 0.4536925
## 1854 8 13 16 8 5.85826560 1.3655919
## 1855 8 13 17 7 5.09319360 1.3743833
## 1856 8 13 18 3 5.04947520 0.5941211
## 1857 8 13 19 2 3.56304960 0.5613169
## 1858 8 13 20 4 3.53026080 1.1330608
## 1859 8 13 21 4 3.39910560 1.1767802
## 1860 8 13 22 5 3.02749920 1.6515281
## 1861 8 13 23 6 2.41544160 2.4840178
## 1862 8 13 24 1 2.20777920 0.4529438
## 1863 8 13 25 4 2.08755360 1.9161185
## 1864 8 13 26 1 1.71594720 0.5827685
## 1865 8 13 27 1 1.57386240 0.6353796
## 1866 8 14 15 3 6.18994860 0.4846567
## 1867 8 14 16 1 5.48398752 0.1823491
## 1868 8 14 17 5 4.76779512 1.0487028
## 1869 8 14 18 7 4.72686984 1.4808954
## 1870 8 14 19 5 3.33541032 1.4990659
## 1871 8 14 20 4 3.30471636 1.2103913
## 1872 8 14 21 3 3.18194052 0.9428209
## 1873 8 14 22 1 2.83407564 0.3528487
## 1874 8 14 23 0 2.26112172 0.0000000
## 1875 8 14 24 3 2.06672664 1.4515708
## 1876 8 14 25 2 1.95418212 1.0234461
## 1877 8 14 26 2 1.60631724 1.2450841
## 1878 8 14 27 2 1.47331008 1.3574875
## 1879 8 15 16 6 4.92257040 1.2188754
## 1880 8 15 17 4 4.27969740 0.9346455
## 1881 8 15 18 1 4.24296180 0.2356844
## 1882 8 15 19 2 2.99395140 0.6680135
## 1883 8 15 20 2 2.96639970 0.6742180
## 1884 8 15 21 1 2.85619290 0.3501164
## 1885 8 15 22 5 2.54394030 1.9654549
## 1886 8 15 23 3 2.02964190 1.4780933
## 1887 8 15 24 2 1.85514780 1.0780812
## 1888 8 15 25 1 1.75412490 0.5700848
## 1889 8 15 26 2 1.44187230 1.3870854
## 1890 8 15 27 3 1.32248160 2.2684626
## 1891 8 16 17 4 3.79159968 1.0549637
## 1892 8 16 18 2 3.75905376 0.5320488
## 1893 8 16 19 1 2.65249248 0.3770039
## 1894 8 16 20 2 2.62808304 0.7610110
## 1895 8 16 21 4 2.53044528 1.5807495
## 1896 8 16 22 2 2.25380496 0.8873882
## 1897 8 16 23 2 1.79816208 1.1122468
## 1898 8 16 24 2 1.64356896 1.2168641
## 1899 8 16 25 1 1.55406768 0.6434726
## 1900 8 16 26 3 1.27742736 2.3484701
## 1901 8 16 27 1 1.17165312 0.8534949
## 1902 8 17 18 3 3.26813256 0.9179554
## 1903 8 17 19 1 2.30608488 0.4336354
## 1904 8 17 20 2 2.28486324 0.8753259
## 1905 8 17 21 0 2.19997668 0.0000000
## 1906 8 17 22 0 1.95946476 0.0000000
## 1907 8 17 23 2 1.56332748 1.2793225
## 1908 8 17 24 1 1.42892376 0.6998274
## 1909 8 17 25 3 1.35111108 2.2203948
## 1910 8 17 26 2 1.11059916 1.8008297
## 1911 8 17 27 2 1.01863872 1.9634047
## 1912 8 18 19 4 2.28629016 1.7495592
## 1913 8 18 20 2 2.26525068 0.8829045
## 1914 8 18 21 3 2.18109276 1.3754573
## 1915 8 18 22 1 1.94264532 0.5147620
## 1916 8 18 23 0 1.54990836 0.0000000
## 1917 8 18 24 1 1.41665832 0.7058865
## 1918 8 18 25 0 1.33951356 0.0000000
## 1919 8 18 26 1 1.10106612 0.9082107
## 1920 8 18 27 3 1.00989504 2.9706057
## 1921 8 19 20 1 1.59842364 0.6256164
## 1922 8 19 21 2 1.53903948 1.2995118
## 1923 8 19 22 1 1.37078436 0.7295093
## 1924 8 19 23 0 1.09365828 0.0000000
## 1925 8 19 24 0 0.99963336 0.0000000
## 1926 8 19 25 1 0.94519788 1.0579795
## 1927 8 19 26 2 0.77694276 2.5741922
## 1928 8 19 27 0 0.71260992 0.0000000
## 1929 8 20 21 2 1.52487654 1.3115816
## 1930 8 20 22 0 1.35816978 0.0000000
## 1931 8 20 23 2 1.08359394 1.8457098
## 1932 8 20 24 1 0.99043428 1.0096581
## 1933 8 20 25 1 0.93649974 1.0678060
## 1934 8 20 26 2 0.76979298 2.5981011
## 1935 8 20 27 1 0.70605216 1.4163260
## 1936 8 21 22 1 1.30771146 0.7646947
## 1937 8 21 23 0 1.04333658 0.0000000
## 1938 8 21 24 1 0.95363796 1.0486160
## 1939 8 21 25 0 0.90170718 0.0000000
## 1940 8 21 26 2 0.74119386 2.6983494
## 1941 8 21 27 1 0.67982112 1.4709752
## 1942 8 22 23 0 0.92927406 0.0000000
## 1943 8 22 24 0 0.84938172 0.0000000
## 1944 8 22 25 0 0.80312826 0.0000000
## 1945 8 22 26 1 0.66016302 1.5147774
## 1946 8 22 27 1 0.60549984 1.6515281
## 1947 8 23 24 1 0.67766556 1.4756542
## 1948 8 23 25 1 0.64076298 1.5606395
## 1949 8 23 26 1 0.52670046 1.8986124
## 1950 8 23 27 1 0.48308832 2.0700149
## 1951 8 24 25 0 0.58567476 0.0000000
## 1952 8 24 26 0 0.48141852 0.0000000
## 1953 8 24 27 2 0.44155584 4.5294385
## 1954 8 25 26 1 0.45520266 2.1968237
## 1955 8 25 27 0 0.41751072 0.0000000
## 1956 8 26 27 0 0.34318944 0.0000000
## 1957 9 10 11 16 16.53060492 0.9679017
## 1958 9 10 12 10 15.58261324 0.6417409
## 1959 9 10 13 7 10.66490640 0.6563583
## 1960 9 10 14 10 9.98353738 1.0016490
## 1961 9 10 15 5 8.96148385 0.5579433
## 1962 9 10 16 6 7.93943032 0.7557217
## 1963 9 10 17 7 6.90256442 1.0141159
## 1964 9 10 18 3 6.84331494 0.4383840
## 1965 9 10 19 7 4.82883262 1.4496257
## 1966 9 10 20 4 4.78439551 0.8360513
## 1967 9 10 21 4 4.60664707 0.8683105
## 1968 9 10 22 5 4.10302649 1.2186127
## 1969 9 10 23 5 3.27353377 1.5274014
## 1970 9 10 24 2 2.99209874 0.6684271
## 1971 9 10 25 2 2.82916267 0.7069229
## 1972 9 10 26 5 2.32554209 2.1500363
## 1973 9 10 27 1 2.13298128 0.4688274
## 1974 9 11 12 15 15.48548208 0.9686492
## 1975 9 11 13 14 10.59842880 1.3209505
## 1976 9 11 14 9 9.92130696 0.9071385
## 1977 9 11 15 10 8.90562420 1.1228859
## 1978 9 11 16 4 7.88994144 0.5069746
## 1979 9 11 17 7 6.85953864 1.0204768
## 1980 9 11 18 5 6.80065848 0.7352229
## 1981 9 11 19 6 4.79873304 1.2503300
## 1982 9 11 20 5 4.75457292 1.0516192
## 1983 9 11 21 2 4.57793244 0.4368784
## 1984 9 11 22 4 4.07745108 0.9810050
## 1985 9 11 23 4 3.25312884 1.2295855
## 1986 9 11 24 1 2.97344808 0.3363099
## 1987 9 11 25 2 2.81152764 0.7113570
## 1988 9 11 26 0 2.31104628 0.0000000
## 1989 9 11 27 6 2.11968576 2.8306083
## 1990 9 12 13 9 9.99063360 0.9008438
## 1991 9 12 14 6 9.35234312 0.6415505
## 1992 9 12 15 5 8.39490740 0.5955992
## 1993 9 12 16 6 7.43747168 0.8067258
## 1994 9 12 17 9 6.46616008 1.3918616
## 1995 9 12 18 5 6.41065656 0.7799513
## 1996 9 12 19 5 4.52353688 1.1053298
## 1997 9 12 20 6 4.48190924 1.3387152
## 1998 9 12 21 5 4.31539868 1.1586415
## 1999 9 12 22 6 3.84361876 1.5610289
## 2000 9 12 23 1 3.06656948 0.3260973
## 2001 9 12 24 3 2.80292776 1.0703094
## 2002 9 12 25 2 2.65029308 0.7546335
## 2003 9 12 26 0 2.17851316 0.0000000
## 2004 9 12 27 1 1.99812672 0.5004688
## 2005 9 13 14 11 6.40084320 1.7185236
## 2006 9 13 15 10 5.74556400 1.7404732
## 2007 9 13 16 5 5.09028480 0.9822633
## 2008 9 13 17 3 4.42550880 0.6778882
## 2009 9 13 18 4 4.38752160 0.9116764
## 2010 9 13 19 3 3.09595680 0.9690058
## 2011 9 13 20 3 3.06746640 0.9780058
## 2012 9 13 21 6 2.95350480 2.0314848
## 2013 9 13 22 2 2.63061360 0.7602789
## 2014 9 13 23 2 2.09879280 0.9529288
## 2015 9 13 24 2 1.91835360 1.0425607
## 2016 9 13 25 1 1.81388880 0.5513017
## 2017 9 13 26 2 1.49099760 1.3413838
## 2018 9 13 27 1 1.36753920 0.7312405
## 2019 9 14 15 4 5.37848630 0.7437037
## 2020 9 14 16 6 4.76507216 1.2591625
## 2021 9 14 17 4 4.14276796 0.9655380
## 2022 9 14 18 2 4.10720772 0.4869488
## 2023 9 14 19 5 2.89815956 1.7252328
## 2024 9 14 20 1 2.87148938 0.3482513
## 2025 9 14 21 3 2.76480866 1.0850660
## 2026 9 14 22 3 2.46254662 1.2182510
## 2027 9 14 23 5 1.96470326 2.5449136
## 2028 9 14 24 2 1.79579212 1.1137147
## 2029 9 14 25 1 1.69800146 0.5889276
## 2030 9 14 26 2 1.39573942 1.4329322
## 2031 9 14 27 1 1.28016864 0.7811471
## 2032 9 15 16 5 4.27725320 1.1689745
## 2033 9 15 17 2 3.71865670 0.5378286
## 2034 9 15 18 0 3.68673690 0.0000000
## 2035 9 15 19 0 2.60146370 0.0000000
## 2036 9 15 20 3 2.57752385 1.1639078
## 2037 9 15 21 2 2.48176445 0.8058783
## 2038 9 15 22 2 2.21044615 0.9047947
## 2039 9 15 23 2 1.76356895 1.1340640
## 2040 9 15 24 2 1.61194990 1.2407334
## 2041 9 15 25 3 1.52417045 1.9682838
## 2042 9 15 26 2 1.25285215 1.5963576
## 2043 9 15 27 0 1.14911280 0.0000000
## 2044 9 16 17 3 3.29454544 0.9105960
## 2045 9 16 18 0 3.26626608 0.0000000
## 2046 9 16 19 4 2.30476784 1.7355327
## 2047 9 16 20 1 2.28355832 0.4379131
## 2048 9 16 21 3 2.19872024 1.3644301
## 2049 9 16 22 2 1.95834568 1.0212702
## 2050 9 16 23 0 1.56243464 0.0000000
## 2051 9 16 24 1 1.42810768 0.7002273
## 2052 9 16 25 3 1.35033944 2.2216636
## 2053 9 16 26 0 1.10996488 0.0000000
## 2054 9 16 27 2 1.01805696 1.9645266
## 2055 9 17 18 4 2.83970148 1.4085988
## 2056 9 17 19 2 2.00377204 0.9981175
## 2057 9 17 20 3 1.98533242 1.5110820
## 2058 9 17 21 1 1.91157394 0.5231291
## 2059 9 17 22 1 1.70259158 0.5873399
## 2060 9 17 23 1 1.35838534 0.7361681
## 2061 9 17 24 4 1.24160108 3.2216467
## 2062 9 17 25 1 1.17398914 0.8517966
## 2063 9 17 26 1 0.96500678 1.0362621
## 2064 9 17 27 0 0.88510176 0.0000000
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## 2066 9 18 20 1 1.96829094 0.5080550
## 2067 9 18 21 6 1.89516558 3.1659503
## 2068 9 18 22 1 1.68797706 0.5924251
## 2069 9 18 23 0 1.34672538 0.0000000
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## 2075 9 19 21 4 1.33728134 2.9911432
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## 2079 9 19 25 0 0.82128854 0.0000000
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## 2086 9 20 25 0 0.81373067 0.0000000
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## 2089 9 21 22 0 1.13627893 0.0000000
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## 2091 9 21 24 1 0.82862218 1.2068226
## 2092 9 21 25 2 0.78349919 2.5526510
## 2093 9 21 26 1 0.64402813 1.5527272
## 2094 9 21 27 1 0.59070096 1.6929040
## 2095 9 22 23 0 0.80745223 0.0000000
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## 2110 10 11 12 17 13.18437936 1.2894046
## 2111 10 11 13 5 9.02352960 0.5541069
## 2112 10 11 14 9 8.44702632 1.0654637
## 2113 10 11 15 10 7.58227140 1.3188660
## 2114 10 11 16 7 6.71751648 1.0420518
## 2115 10 11 17 5 5.84022888 0.8561308
## 2116 10 11 18 4 5.79009816 0.6908346
## 2117 10 11 19 4 4.08565368 0.9790355
## 2118 10 11 20 5 4.04805564 1.2351609
## 2119 10 11 21 4 3.89766348 1.0262559
## 2120 10 11 22 2 3.47155236 0.5761111
## 2121 10 11 23 4 2.76972228 1.4441881
## 2122 10 11 24 2 2.53160136 0.7900138
## 2123 10 11 25 1 2.39374188 0.4177560
## 2124 10 11 26 2 1.96763076 1.0164509
## 2125 10 11 27 1 1.80470592 0.5541069
## 2126 10 12 13 9 8.50605120 1.0580703
## 2127 10 12 14 4 7.96260904 0.5023479
## 2128 10 12 15 12 7.14744580 1.6789214
## 2129 10 12 16 3 6.33228256 0.4737628
## 2130 10 12 17 4 5.50530536 0.7265719
## 2131 10 12 18 2 5.45804952 0.3664313
## 2132 10 12 19 6 3.85135096 1.5578949
## 2133 10 12 20 3 3.81590908 0.7861823
## 2134 10 12 21 6 3.67414156 1.6330345
## 2135 10 12 22 2 3.27246692 0.6111597
## 2136 10 12 23 5 2.61088516 1.9150593
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## 2139 10 12 26 1 1.85479172 0.5391441
## 2140 10 12 27 2 1.70121024 1.1756336
## 2141 10 13 14 6 5.44969440 1.1009792
## 2142 10 13 15 1 4.89178800 0.2044242
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## 2145 10 13 18 4 3.73554720 1.0707936
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## 2149 10 13 22 3 2.23971120 1.3394584
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## 2151 10 13 24 1 1.63329120 0.6122607
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## 2590 14 17 23 0 0.69412564 0.0000000
## 2591 14 17 24 0 0.63444968 0.0000000
## 2592 14 17 25 0 0.59990044 0.0000000
## 2593 14 17 26 0 0.49311188 0.0000000
## 2594 14 17 27 0 0.45228096 0.0000000
## 2595 14 18 19 2 1.01512488 1.9702009
## 2596 14 18 20 0 1.00578324 0.0000000
## 2597 14 18 21 0 0.96841668 0.0000000
## 2598 14 18 22 0 0.86254476 0.0000000
## 2599 14 18 23 0 0.68816748 0.0000000
## 2600 14 18 24 0 0.62900376 0.0000000
## 2601 14 18 25 1 0.59475108 1.6813757
## 2602 14 18 26 0 0.48887916 0.0000000
## 2603 14 18 27 1 0.44839872 2.2301580
## 2604 14 19 20 1 0.70970852 1.4090292
## 2605 14 19 21 0 0.68334164 0.0000000
## 2606 14 19 22 1 0.60863548 1.6430196
## 2607 14 19 23 0 0.48559004 0.0000000
## 2608 14 19 24 0 0.44384248 0.0000000
## 2609 14 19 25 1 0.41967284 2.3828085
## 2610 14 19 26 0 0.34496668 0.0000000
## 2611 14 19 27 0 0.31640256 0.0000000
## 2612 14 20 21 0 0.67705322 0.0000000
## 2613 14 20 22 0 0.60303454 0.0000000
## 2614 14 20 23 1 0.48112142 2.0784774
## 2615 14 20 24 0 0.43975804 0.0000000
## 2616 14 20 25 1 0.41581082 2.4049398
## 2617 14 20 26 0 0.34179214 0.0000000
## 2618 14 20 27 0 0.31349088 0.0000000
## 2619 14 21 22 1 0.58063078 1.7222649
## 2620 14 21 23 1 0.46324694 2.1586759
## 2621 14 21 24 1 0.42342028 2.3617197
## 2622 14 21 25 0 0.40036274 0.0000000
## 2623 14 21 26 1 0.32909398 3.0386457
## 2624 14 21 27 0 0.30184416 0.0000000
## 2625 14 22 23 0 0.41260258 0.0000000
## 2626 14 22 24 0 0.37712996 0.0000000
## 2627 14 22 25 0 0.35659318 0.0000000
## 2628 14 22 26 0 0.29311586 0.0000000
## 2629 14 22 27 1 0.26884512 3.7196137
## 2630 14 23 24 0 0.30088708 0.0000000
## 2631 14 23 25 0 0.28450214 0.0000000
## 2632 14 23 26 0 0.23385778 0.0000000
## 2633 14 23 27 0 0.21449376 0.0000000
## 2634 14 24 25 1 0.26004268 3.8455226
## 2635 14 24 26 0 0.21375236 0.0000000
## 2636 14 24 27 0 0.19605312 0.0000000
## 2637 14 25 26 0 0.20211238 0.0000000
## 2638 14 25 27 0 0.18537696 0.0000000
## 2639 14 26 27 0 0.15237792 0.0000000
## 2640 15 16 17 1 1.51114480 0.6617500
## 2641 15 16 18 2 1.49817360 1.3349588
## 2642 15 16 19 2 1.05715280 1.8918741
## 2643 15 16 20 2 1.04742440 1.9094457
## 2644 15 16 21 1 1.00851080 0.9915610
## 2645 15 16 22 0 0.89825560 0.0000000
## 2646 15 16 23 1 0.71665880 1.3953642
## 2647 15 16 24 1 0.65504560 1.5266113
## 2648 15 16 25 1 0.61937480 1.6145313
## 2649 15 16 26 1 0.50911960 1.9641750
## 2650 15 16 27 0 0.46696320 0.0000000
## 2651 15 17 18 2 1.30251660 1.5354891
## 2652 15 17 19 0 0.91909180 0.0000000
## 2653 15 17 20 1 0.91063390 1.0981361
## 2654 15 17 21 2 0.87680230 2.2810159
## 2655 15 17 22 1 0.78094610 1.2804981
## 2656 15 17 23 1 0.62306530 1.6049682
## 2657 15 17 24 1 0.56949860 1.7559306
## 2658 15 17 25 0 0.53848630 0.0000000
## 2659 15 17 26 0 0.44263010 0.0000000
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## 2661 15 18 19 1 0.91120260 1.0974508
## 2662 15 18 20 0 0.90281730 0.0000000
## 2663 15 18 21 1 0.86927610 1.1503825
## 2664 15 18 22 0 0.77424270 0.0000000
## 2665 15 18 23 0 0.61771710 0.0000000
## 2666 15 18 24 0 0.56461020 0.0000000
## 2667 15 18 25 0 0.53386410 0.0000000
## 2668 15 18 26 0 0.43883070 0.0000000
## 2669 15 18 27 0 0.40249440 0.0000000
## 2670 15 19 20 0 0.63705290 0.0000000
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## 2672 15 19 22 0 0.54632710 0.0000000
## 2673 15 19 23 0 0.43587830 0.0000000
## 2674 15 19 24 0 0.39840460 0.0000000
## 2675 15 19 25 0 0.37670930 0.0000000
## 2676 15 19 26 0 0.30965110 0.0000000
## 2677 15 19 27 0 0.28401120 0.0000000
## 2678 15 20 21 0 0.60774065 0.0000000
## 2679 15 20 22 0 0.54129955 0.0000000
## 2680 15 20 23 1 0.43186715 2.3155269
## 2681 15 20 24 1 0.39473830 2.5333240
## 2682 15 20 25 1 0.37324265 2.6792222
## 2683 15 20 26 0 0.30680155 0.0000000
## 2684 15 20 27 1 0.28139760 3.5536906
## 2685 15 21 22 0 0.52118935 0.0000000
## 2686 15 21 23 0 0.41582255 0.0000000
## 2687 15 21 24 0 0.38007310 0.0000000
## 2688 15 21 25 1 0.35937605 2.7826006
## 2689 15 21 26 0 0.29540335 0.0000000
## 2690 15 21 27 0 0.27094320 0.0000000
## 2691 15 22 23 0 0.37036285 0.0000000
## 2692 15 22 24 1 0.33852170 2.9540204
## 2693 15 22 25 0 0.32008735 0.0000000
## 2694 15 22 26 0 0.26310845 0.0000000
## 2695 15 22 27 0 0.24132240 0.0000000
## 2696 15 23 24 0 0.27008410 0.0000000
## 2697 15 23 25 0 0.25537655 0.0000000
## 2698 15 23 26 1 0.20991685 4.7637910
## 2699 15 23 27 1 0.19253520 5.1938555
## 2700 15 24 25 2 0.23342110 8.5682057
## 2701 15 24 26 0 0.19186970 0.0000000
## 2702 15 24 27 1 0.17598240 5.6823864
## 2703 15 25 26 1 0.18142135 5.5120304
## 2704 15 25 27 0 0.16639920 0.0000000
## 2705 15 26 27 0 0.13677840 0.0000000
## 2706 16 17 18 1 1.15396512 0.8665773
## 2707 16 17 19 1 0.81426976 1.2280942
## 2708 16 17 20 1 0.80677648 1.2395007
## 2709 16 17 21 1 0.77680336 1.2873271
## 2710 16 17 22 0 0.69187952 0.0000000
## 2711 16 17 23 1 0.55200496 1.8115779
## 2712 16 17 24 0 0.50454752 0.0000000
## 2713 16 17 25 3 0.47707216 6.2883569
## 2714 16 17 26 0 0.39214832 0.0000000
## 2715 16 17 27 0 0.35967744 0.0000000
## 2716 16 18 19 3 0.80728032 3.7161813
## 2717 16 18 20 0 0.79985136 0.0000000
## 2718 16 18 21 0 0.77013552 0.0000000
## 2719 16 18 22 1 0.68594064 1.4578521
## 2720 16 18 23 0 0.54726672 0.0000000
## 2721 16 18 24 0 0.50021664 0.0000000
## 2722 16 18 25 0 0.47297712 0.0000000
## 2723 16 18 26 2 0.38878224 5.1442679
## 2724 16 18 27 1 0.35659008 2.8043405
## 2725 16 19 20 0 0.56439728 0.0000000
## 2726 16 19 21 1 0.54342896 1.8401669
## 2727 16 19 22 0 0.48401872 0.0000000
## 2728 16 19 23 0 0.38616656 0.0000000
## 2729 16 19 24 0 0.35296672 0.0000000
## 2730 16 19 25 0 0.33374576 0.0000000
## 2731 16 19 26 1 0.27433552 3.6451714
## 2732 16 19 27 0 0.25161984 0.0000000
## 2733 16 20 21 0 0.53842808 0.0000000
## 2734 16 20 22 0 0.47956456 0.0000000
## 2735 16 20 23 0 0.38261288 0.0000000
## 2736 16 20 24 0 0.34971856 0.0000000
## 2737 16 20 25 0 0.33067448 0.0000000
## 2738 16 20 26 1 0.27181096 3.6790275
## 2739 16 20 27 0 0.24930432 0.0000000
## 2740 16 21 22 0 0.46174792 0.0000000
## 2741 16 21 23 0 0.36839816 0.0000000
## 2742 16 21 24 0 0.33672592 0.0000000
## 2743 16 21 25 1 0.31838936 3.1408085
## 2744 16 21 26 1 0.26171272 3.8209836
## 2745 16 21 27 0 0.24004224 0.0000000
## 2746 16 22 23 0 0.32812312 0.0000000
## 2747 16 22 24 0 0.29991344 0.0000000
## 2748 16 22 25 0 0.28358152 0.0000000
## 2749 16 22 26 0 0.23310104 0.0000000
## 2750 16 22 27 0 0.21379968 0.0000000
## 2751 16 23 24 0 0.23928112 0.0000000
## 2752 16 23 25 0 0.22625096 0.0000000
## 2753 16 23 26 0 0.18597592 0.0000000
## 2754 16 23 27 0 0.17057664 0.0000000
## 2755 16 24 25 1 0.20679952 4.8356012
## 2756 16 24 26 0 0.16998704 0.0000000
## 2757 16 24 27 0 0.15591168 0.0000000
## 2758 16 25 26 0 0.16073032 0.0000000
## 2759 16 25 27 0 0.14742144 0.0000000
## 2760 16 26 27 0 0.12117888 0.0000000
## 2761 17 18 19 0 0.70185192 0.0000000
## 2762 17 18 20 0 0.69539316 0.0000000
## 2763 17 18 21 1 0.66955812 1.4935223
## 2764 17 18 22 1 0.59635884 1.6768428
## 2765 17 18 23 0 0.47579532 0.0000000
## 2766 17 18 24 0 0.43488984 0.0000000
## 2767 17 18 25 1 0.41120772 2.4318610
## 2768 17 18 26 1 0.33800844 2.9585060
## 2769 17 18 27 1 0.31002048 3.2255934
## 2770 17 19 20 0 0.49068868 0.0000000
## 2771 17 19 21 0 0.47245876 0.0000000
## 2772 17 19 22 1 0.42080732 2.3763845
## 2773 17 19 23 0 0.33573436 0.0000000
## 2774 17 19 24 0 0.30687032 0.0000000
## 2775 17 19 25 0 0.29015956 0.0000000
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## 2777 17 19 27 0 0.21875904 0.0000000
## 2778 17 20 21 0 0.46811098 0.0000000
## 2779 17 20 22 0 0.41693486 0.0000000
## 2780 17 20 23 0 0.33264478 0.0000000
## 2781 17 20 24 0 0.30404636 0.0000000
## 2782 17 20 25 0 0.28748938 0.0000000
## 2783 17 20 26 1 0.23631326 4.2316711
## 2784 17 20 27 0 0.21674592 0.0000000
## 2785 17 21 22 0 0.40144502 0.0000000
## 2786 17 21 23 1 0.32028646 3.1222050
## 2787 17 21 24 0 0.29275052 0.0000000
## 2788 17 21 25 0 0.27680866 0.0000000
## 2789 17 21 26 0 0.22753382 0.0000000
## 2790 17 21 27 0 0.20869344 0.0000000
## 2791 17 22 23 0 0.28527122 0.0000000
## 2792 17 22 24 0 0.26074564 0.0000000
## 2793 17 22 25 0 0.24654662 0.0000000
## 2794 17 22 26 0 0.20265874 0.0000000
## 2795 17 22 27 0 0.18587808 0.0000000
## 2796 17 23 24 0 0.20803172 0.0000000
## 2797 17 23 25 0 0.19670326 0.0000000
## 2798 17 23 26 0 0.16168802 0.0000000
## 2799 17 23 27 0 0.14829984 0.0000000
## 2800 17 24 25 0 0.17979212 0.0000000
## 2801 17 24 26 0 0.14778724 0.0000000
## 2802 17 24 27 0 0.13555008 0.0000000
## 2803 17 25 26 0 0.13973942 0.0000000
## 2804 17 25 27 0 0.12816864 0.0000000
## 2805 17 26 27 1 0.10535328 9.4918734
## 2806 18 19 20 0 0.48647676 0.0000000
## 2807 18 19 21 0 0.46840332 0.0000000
## 2808 18 19 22 1 0.41719524 2.3969593
## 2809 18 19 23 0 0.33285252 0.0000000
## 2810 18 19 24 0 0.30423624 0.0000000
## 2811 18 19 25 0 0.28766892 0.0000000
## 2812 18 19 26 1 0.23646084 4.2290301
## 2813 18 19 27 0 0.21688128 0.0000000
## 2814 18 20 21 1 0.46409286 2.1547412
## 2815 18 20 22 1 0.41335602 2.4192221
## 2816 18 20 23 0 0.32978946 0.0000000
## 2817 18 20 24 0 0.30143652 0.0000000
## 2818 18 20 25 0 0.28502166 0.0000000
## 2819 18 20 26 1 0.23428482 4.2683090
## 2820 18 20 27 0 0.21488544 0.0000000
## 2821 18 21 22 0 0.39799914 0.0000000
## 2822 18 21 23 0 0.31753722 0.0000000
## 2823 18 21 24 0 0.29023764 0.0000000
## 2824 18 21 25 0 0.27443262 0.0000000
## 2825 18 21 26 0 0.22558074 0.0000000
## 2826 18 21 27 0 0.20690208 0.0000000
## 2827 18 22 23 1 0.28282254 3.5357861
## 2828 18 22 24 0 0.25850748 0.0000000
## 2829 18 22 25 0 0.24443034 0.0000000
## 2830 18 22 26 1 0.20091918 4.9771256
## 2831 18 22 27 0 0.18428256 0.0000000
## 2832 18 23 24 0 0.20624604 0.0000000
## 2833 18 23 25 0 0.19501482 0.0000000
## 2834 18 23 26 0 0.16030014 0.0000000
## 2835 18 23 27 0 0.14702688 0.0000000
## 2836 18 24 25 0 0.17824884 0.0000000
## 2837 18 24 26 0 0.14651868 0.0000000
## 2838 18 24 27 1 0.13438656 7.4412203
## 2839 18 25 26 0 0.13853994 0.0000000
## 2840 18 25 27 0 0.12706848 0.0000000
## 2841 18 26 27 0 0.10444896 0.0000000
## 2842 19 20 21 1 0.32747678 3.0536516
## 2843 19 20 22 0 0.29167546 0.0000000
## 2844 19 20 23 0 0.23270858 0.0000000
## 2845 19 20 24 0 0.21270196 0.0000000
## 2846 19 20 25 0 0.20111918 0.0000000
## 2847 19 20 26 0 0.16531786 0.0000000
## 2848 19 20 27 0 0.15162912 0.0000000
## 2849 19 21 22 0 0.28083922 0.0000000
## 2850 19 21 23 0 0.22406306 0.0000000
## 2851 19 21 24 1 0.20479972 4.8828192
## 2852 19 21 25 1 0.19364726 5.1640287
## 2853 19 21 26 1 0.15917602 6.2823533
## 2854 19 21 27 0 0.14599584 0.0000000
## 2855 19 22 23 1 0.19956742 5.0108379
## 2856 19 22 24 0 0.18241004 0.0000000
## 2857 19 22 25 0 0.17247682 0.0000000
## 2858 19 22 26 0 0.14177414 0.0000000
## 2859 19 22 27 0 0.13003488 0.0000000
## 2860 19 23 24 0 0.14553292 0.0000000
## 2861 19 23 25 0 0.13760786 0.0000000
## 2862 19 23 26 0 0.11311222 0.0000000
## 2863 19 23 27 0 0.10374624 0.0000000
## 2864 19 24 25 0 0.12577732 0.0000000
## 2865 19 24 26 0 0.10338764 0.0000000
## 2866 19 24 27 0 0.09482688 0.0000000
## 2867 19 25 26 0 0.09775762 0.0000000
## 2868 19 25 27 0 0.08966304 0.0000000
## 2869 19 26 27 0 0.07370208 0.0000000
## 2870 20 21 22 1 0.27825481 3.5938283
## 2871 20 21 23 1 0.22200113 4.5044816
## 2872 20 21 24 0 0.20291506 0.0000000
## 2873 20 21 25 0 0.19186523 0.0000000
## 2874 20 21 26 0 0.15771121 0.0000000
## 2875 20 21 27 0 0.14465232 0.0000000
## 2876 20 22 23 0 0.19773091 0.0000000
## 2877 20 22 24 0 0.18073142 0.0000000
## 2878 20 22 25 0 0.17088961 0.0000000
## 2879 20 22 26 1 0.14046947 7.1189846
## 2880 20 22 27 0 0.12883824 0.0000000
## 2881 20 23 24 0 0.14419366 0.0000000
## 2882 20 23 25 0 0.13634153 0.0000000
## 2883 20 23 26 0 0.11207131 0.0000000
## 2884 20 23 27 1 0.10279152 9.7284290
## 2885 20 24 25 0 0.12461986 0.0000000
## 2886 20 24 26 0 0.10243622 0.0000000
## 2887 20 24 27 0 0.09395424 0.0000000
## 2888 20 25 26 0 0.09685801 0.0000000
## 2889 20 25 27 0 0.08883792 0.0000000
## 2890 20 26 27 0 0.07302384 0.0000000
## 2891 21 22 23 0 0.19038487 0.0000000
## 2892 21 22 24 1 0.17401694 5.7465670
## 2893 21 22 25 0 0.16454077 0.0000000
## 2894 21 22 26 0 0.13525079 0.0000000
## 2895 21 22 27 0 0.12405168 0.0000000
## 2896 21 23 24 0 0.13883662 0.0000000
## 2897 21 23 25 0 0.13127621 0.0000000
## 2898 21 23 26 0 0.10790767 0.0000000
## 2899 21 23 27 0 0.09897264 0.0000000
## 2900 21 24 25 0 0.11999002 0.0000000
## 2901 21 24 26 1 0.09863054 10.1388475
## 2902 21 24 27 0 0.09046368 0.0000000
## 2903 21 25 26 0 0.09325957 0.0000000
## 2904 21 25 27 0 0.08553744 0.0000000
## 2905 21 26 27 1 0.07031088 14.2225499
## 2906 22 23 24 0 0.12365834 0.0000000
## 2907 22 23 25 1 0.11692447 8.5525297
## 2908 22 23 26 0 0.09611069 0.0000000
## 2909 22 23 27 0 0.08815248 0.0000000
## 2910 22 24 25 0 0.10687214 0.0000000
## 2911 22 24 26 0 0.08784778 0.0000000
## 2912 22 24 27 0 0.08057376 0.0000000
## 2913 22 25 26 0 0.08306399 0.0000000
## 2914 22 25 27 0 0.07618608 0.0000000
## 2915 22 26 27 0 0.06262416 0.0000000
## 2916 23 24 25 0 0.08526622 0.0000000
## 2917 23 24 26 0 0.07008794 0.0000000
## 2918 23 24 27 0 0.06428448 0.0000000
## 2919 23 25 26 0 0.06627127 0.0000000
## 2920 23 25 27 0 0.06078384 0.0000000
## 2921 23 26 27 0 0.04996368 0.0000000
## 2922 24 25 26 0 0.06057374 0.0000000
## 2923 24 25 27 0 0.05555808 0.0000000
## 2924 24 26 27 0 0.04566816 0.0000000
## 2925 25 26 27 0 0.04318128 0.0000000
## log_factorial_O Posterior_OE
## 1 1234.0992537 0.9377797
## 2 1029.8389993 1.0183464
## 3 799.9886918 1.0214165
## 4 758.2481131 0.9545159
## 5 753.0551562 1.1236097
## 6 722.0155119 1.0349692
## 7 595.0055242 1.0205225
## 8 515.8908246 1.0608947
## 9 410.3227765 0.9156656
## 10 433.9593240 0.9979189
## 11 230.4390436 0.9131075
## 12 260.5649410 1.0321847
## 13 256.2211356 1.1078976
## 14 172.3527971 0.9667098
## 15 188.6281734 1.1196417
## 16 156.3608363 1.0184517
## 17 99.3306125 1.0214749
## 18 64.5575386 0.8576083
## 19 121.5330815 1.1546532
## 20 61.2617018 0.9192261
## 21 45.3801389 0.9326440
## 22 42.3356165 0.9532490
## 23 45.3801389 1.0015334
## 24 42.3356165 1.0685155
## 25 19.9872145 0.9039358
## 26 831.5177800 1.0667375
## 27 660.5662611 1.0868614
## 28 545.3441778 0.9248032
## 29 595.0055242 1.1559131
## 30 520.7811737 1.0029431
## 31 405.6222962 0.9555632
## 32 327.1852877 0.9502422
## 33 322.6634991 0.9467781
## 34 359.1342054 1.0619170
## 35 221.9564418 1.0594023
## 36 164.3201123 0.9411758
## 37 136.8027226 0.9237703
## 38 136.8027226 1.0028482
## 39 95.7196945 0.9270581
## 40 81.5579595 0.8695421
## 41 71.2570390 1.0103011
## 42 51.6066756 0.8995373
## 43 61.2617018 0.9772489
## 44 58.0036052 1.0170508
## 45 39.3398842 0.9930941
## 46 27.8992714 0.9360153
## 47 19.9872145 0.8838225
## 48 45.3801389 1.1955662
## 49 10.6046029 0.8735029
## 50 457.8123880 1.0343169
## 51 443.4750881 0.9853576
## 52 313.6528299 0.9230819
## 53 391.5759882 1.0122248
## 54 295.7666014 0.9497535
## 55 273.6731243 1.0271730
## 56 273.6731243 1.0321021
## 57 213.5322415 0.9335585
## 58 156.3608363 1.0337231
## 59 129.1239336 0.9804180
## 60 102.9681986 0.9444383
## 61 71.2570390 0.8697249
## 62 54.7847294 0.8551388
## 63 102.9681986 1.1122672
## 64 36.3954452 0.9037503
## 65 45.3801389 0.9733255
## 66 110.3206397 1.4136564
## 67 27.8992714 0.9048439
## 68 22.5521639 0.9431621
## 69 25.1912212 1.0027443
## 70 10.6046029 0.8600058
## 71 10.6046029 0.9185194
## 72 10.6046029 0.9430526
## 73 382.2585888 1.0620096
## 74 313.6528299 1.0893960
## 75 295.7666014 1.0030463
## 76 209.3425868 0.9117793
## 77 156.3608363 0.8766111
## 78 168.3274454 0.9155535
## 79 125.3172711 0.8215581
## 80 102.9681986 0.9661144
## 81 92.1361756 0.9575945
## 82 125.3172711 1.1788901
## 83 58.0036052 0.9084291
## 84 39.3398842 0.8589419
## 85 58.0036052 0.9844124
## 86 36.3954452 0.9956068
## 87 15.1044126 0.8072779
## 88 39.3398842 1.0403959
## 89 19.9872145 0.9115936
## 90 25.1912212 1.0466000
## 91 10.6046029 0.9062409
## 92 22.5521639 1.0685476
## 93 19.9872145 1.1002911
## 94 6.5792512 0.9357180
## 95 234.7017234 0.8921908
## 96 226.1905483 0.8334430
## 97 256.2211356 0.9996969
## 98 192.7390473 0.9577366
## 99 205.1681995 0.9962596
## 100 172.3527971 0.9449083
## 101 99.3306125 0.9308172
## 102 125.3172711 1.0824791
## 103 81.5579595 0.9508382
## 104 54.7847294 0.8734034
## 105 54.7847294 0.9432723
## 106 88.5808275 1.1467720
## 107 48.4711814 1.0757292
## 108 30.6718601 0.9379461
## 109 30.6718601 0.9535776
## 110 27.8992714 0.9755490
## 111 25.1912212 1.0347234
## 112 15.1044126 0.9533317
## 113 17.5023078 0.9997202
## 114 42.3356165 1.3356065
## 115 4.7874917 0.8964669
## 116 205.1681995 0.9135686
## 117 172.3527971 0.9211755
## 118 221.9564418 1.1884888
## 119 180.4562914 1.0633099
## 120 152.4095926 1.0122814
## 121 144.5657439 1.2542923
## 122 85.0544670 1.0162518
## 123 81.5579595 1.0609019
## 124 39.3398842 0.8671379
## 125 45.3801389 0.9739897
## 126 74.6582363 1.1755233
## 127 30.6718601 1.0121786
## 128 30.6718601 1.0158901
## 129 12.8018275 0.8486620
## 130 22.5521639 0.9948200
## 131 25.1912212 1.1019356
## 132 6.5792512 0.8909835
## 133 22.5521639 1.1195093
## 134 8.5251614 0.9846661
## 135 1.7917595 0.8732676
## 136 213.5322415 0.9925248
## 137 209.3425868 1.1024708
## 138 164.3201123 0.9698326
## 139 136.8027226 0.9195117
## 140 88.5808275 0.9600564
## 141 129.1239336 1.1895924
## 142 48.4711814 0.8415379
## 143 67.8897431 1.0178700
## 144 71.2570390 1.1154997
## 145 67.8897431 1.0989982
## 146 36.3954452 1.0379681
## 147 25.1912212 0.9416759
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## 1826 8.5251614 0.9715800
## 1827 25.1912212 1.1965722
## 1828 0.6931472 0.8789477
## 1829 12.8018275 1.1214489
## 1830 8.5251614 1.0600202
## 1831 3.1780538 0.9755003
## 1832 3.1780538 1.0093721
## 1833 3.1780538 1.0214051
## 1834 0.6931472 0.9539153
## 1835 0.6931472 0.9748562
## 1836 3.1780538 1.0599791
## 1837 25.1912212 1.0713324
## 1838 15.1044126 0.9777718
## 1839 25.1912212 1.1305232
## 1840 6.5792512 0.9203560
## 1841 8.5251614 0.9857600
## 1842 3.1780538 0.8908823
## 1843 1.7917595 0.9233602
## 1844 4.7874917 0.9944981
## 1845 3.1780538 0.9661417
## 1846 6.5792512 1.0562998
## 1847 3.1780538 1.0173669
## 1848 0.0000000 0.8796516
## 1849 0.0000000 0.9230086
## 1850 4.7874917 1.0955637
## 1851 1.7917595 1.0270662
## 1852 4.7874917 0.9235232
## 1853 1.7917595 0.8803464
## 1854 10.6046029 1.0727580
## 1855 8.5251614 1.0665057
## 1856 1.7917595 0.9284091
## 1857 0.6931472 0.9424104
## 1858 3.1780538 1.0173282
## 1859 3.1780538 1.0222741
## 1860 4.7874917 1.0741385
## 1861 6.5792512 1.1379017
## 1862 0.0000000 0.9531613
## 1863 3.1780538 1.0745137
## 1864 0.0000000 0.9716951
## 1865 0.0000000 0.9771842
## 1866 1.7917595 0.8928400
## 1867 0.0000000 0.8457102
## 1868 4.7874917 1.0081918
## 1869 8.5251614 1.0803084
## 1870 4.7874917 1.0618495
## 1871 3.1780538 1.0258635
## 1872 1.7917595 0.9932010
## 1873 0.0000000 0.9305595
## 1874 0.0000000 0.9124928
## 1875 1.7917595 1.0363922
## 1876 0.6931472 1.0017945
## 1877 0.6931472 1.0156320
## 1878 0.6931472 1.0210243
## 1879 6.5792512 1.0378036
## 1880 3.1780538 0.9899598
## 1881 0.0000000 0.8834351
## 1882 0.6931472 0.9625941
## 1883 0.6931472 0.9635933
## 1884 0.0000000 0.9297810
## 1885 4.7874917 1.0940224
## 1886 1.7917595 1.0378931
## 1887 0.6931472 1.0056954
## 1888 0.0000000 0.9702306
## 1889 0.6931472 1.0223073
## 1890 1.7917595 1.0673686
## 1891 3.1780538 1.0076143
## 1892 0.6931472 0.9356534
## 1893 0.0000000 0.9370014
## 1894 0.6931472 0.9760330
## 1895 3.1780538 1.0562863
## 1896 0.6931472 0.9901748
## 1897 0.6931472 1.0079538
## 1898 0.6931472 1.0141319
## 1899 0.0000000 0.9779539
## 1900 1.7917595 1.0693033
## 1901 0.0000000 0.9930645
## 1902 1.7917595 0.9900123
## 1903 0.0000000 0.9495413
## 1904 0.6931472 0.9889857
## 1905 0.0000000 0.9146572
## 1906 0.0000000 0.9232714
## 1907 0.6931472 1.0173686
## 1908 0.0000000 0.9828479
## 1909 1.7917595 1.0661428
## 1910 0.6931472 1.0360246
## 1911 0.6931472 1.0398980
## 1912 3.1780538 1.0662574
## 1913 0.6931472 0.9897362
## 1914 1.7917595 1.0317908
## 1915 0.0000000 0.9630636
## 1916 0.0000000 0.9383196
## 1917 0.0000000 0.9833302
## 1918 0.0000000 0.9462424
## 1919 0.0000000 0.9959048
## 1920 1.7917595 1.0809380
## 1921 0.0000000 0.9762309
## 1922 0.6931472 1.0183524
## 1923 0.0000000 0.9851383
## 1924 0.0000000 0.9556717
## 1925 0.0000000 0.9593277
## 1926 0.0000000 1.0022347
## 1927 0.6931472 1.0502178
## 1928 0.0000000 0.9706633
## 1929 0.6931472 1.0189270
## 1930 0.0000000 0.9455344
## 1931 0.6931472 1.0371590
## 1932 0.0000000 1.0003893
## 1933 0.0000000 1.0025903
## 1934 0.6931472 1.0505262
## 1935 0.0000000 1.0121045
## 1936 0.0000000 0.9876351
## 1937 0.0000000 0.9576249
## 1938 0.0000000 1.0018899
## 1939 0.0000000 0.9631653
## 1940 0.6931472 1.0517616
## 1941 0.0000000 1.0131989
## 1942 0.0000000 0.9620819
## 1943 0.0000000 0.9652285
## 1944 0.0000000 0.9670596
## 1945 0.0000000 1.0140207
## 1946 0.0000000 1.0163127
## 1947 0.0000000 1.0132890
## 1948 0.0000000 1.0148329
## 1949 0.0000000 1.0196350
## 1950 0.0000000 1.0214832
## 1951 0.0000000 0.9757623
## 1952 0.0000000 0.9799905
## 1953 0.6931472 1.0648820
## 1954 0.0000000 1.0226684
## 1955 0.0000000 0.9826006
## 1956 0.0000000 0.9856534
## 1957 30.6718601 0.9867708
## 1958 15.1044126 0.8574436
## 1959 8.5251614 0.8929737
## 1960 15.1044126 1.0004905
## 1961 4.7874917 0.8782566
## 1962 6.5792512 0.9384651
## 1963 8.5251614 1.0031966
## 1964 1.7917595 0.8736643
## 1965 8.5251614 1.0764308
## 1966 3.1780538 0.9723439
## 1967 3.1780538 0.9784761
## 1968 4.7874917 1.0324038
## 1969 4.7874917 1.0642964
## 1970 0.6931472 0.9626613
## 1971 0.6931472 0.9686010
## 1972 4.7874917 1.1032463
## 1973 0.0000000 0.9559342
## 1974 27.8992714 0.9875720
## 1975 25.1912212 1.0995294
## 1976 12.8018275 0.9724978
## 1977 15.1044126 1.0336899
## 1978 3.1780538 0.8763845
## 1979 8.5251614 1.0046147
## 1980 4.7874917 0.9407265
## 1981 6.5792512 1.0423326
## 1982 4.7874917 1.0086623
## 1983 0.6931472 0.9084413
## 1984 3.1780538 0.9971994
## 1985 3.1780538 1.0278359
## 1986 0.0000000 0.9256749
## 1987 0.6931472 0.9692483
## 1988 0.0000000 0.9107331
## 1989 6.5792512 1.1509978
## 1990 12.8018275 0.9704894
## 1991 6.5792512 0.8981994
## 1992 4.7874917 0.8938197
## 1993 6.5792512 0.9536533
## 1994 12.8018275 1.0843368
## 1995 4.7874917 0.9529605
## 1996 4.7874917 1.0169550
## 1997 6.5792512 1.0541015
## 1998 4.7874917 1.0245434
## 1999 6.5792512 1.0786376
## 2000 0.0000000 0.9224398
## 2001 1.7917595 1.0074702
## 2002 0.6931472 0.9752066
## 2003 0.0000000 0.9154194
## 2004 0.0000000 0.9609745
## 2005 17.5023078 1.1534127
## 2006 15.1044126 1.1450853
## 2007 4.7874917 0.9968507
## 2008 1.7917595 0.9490956
## 2009 3.1780538 0.9861429
## 2010 1.7917595 0.9964026
## 2011 1.7917595 0.9974680
## 2012 6.5792512 1.1148250
## 2013 0.6931472 0.9759388
## 2014 0.6931472 0.9961525
## 2015 0.6931472 1.0032023
## 2016 0.0000000 0.9679471
## 2017 0.6931472 1.0203040
## 2018 0.0000000 0.9852664
## 2019 3.1780538 0.9523948
## 2020 6.5792512 1.0435705
## 2021 3.1780538 0.9948498
## 2022 0.6931472 0.9238872
## 2023 4.7874917 1.0793858
## 2024 0.0000000 0.9292432
## 2025 1.7917595 1.0089281
## 2026 1.7917595 1.0206390
## 2027 4.7874917 1.1188316
## 2028 0.6931472 1.0080479
## 2029 0.0000000 0.9723850
## 2030 0.6931472 1.0241957
## 2031 0.0000000 0.9887294
## 2032 4.7874917 1.0259464
## 2033 0.6931472 0.9370381
## 2034 0.0000000 0.8647807
## 2035 0.0000000 0.9006301
## 2036 1.7917595 1.0161524
## 2037 0.6931472 0.9815132
## 2038 0.6931472 0.9918396
## 2039 0.6931472 1.0093297
## 2040 0.6931472 1.0154049
## 2041 1.7917595 1.0587926
## 2042 0.6931472 1.0300893
## 2043 0.0000000 0.9535285
## 2044 1.7917595 0.9890392
## 2045 0.0000000 0.8783260
## 2046 3.1780538 1.0654962
## 2047 0.0000000 0.9503684
## 2048 1.7917595 1.0310852
## 2049 0.6931472 1.0016312
## 2050 0.0000000 0.9378521
## 2051 0.0000000 0.9828800
## 2052 1.7917595 1.0661758
## 2053 0.0000000 0.9550405
## 2054 0.6931472 1.0399226
## 2055 3.1780538 1.0439210
## 2056 0.6931472 0.9998526
## 2057 1.7917595 1.0396921
## 2058 0.0000000 0.9642376
## 2059 0.0000000 0.9722084
## 2060 0.0000000 0.9856281
## 2061 3.1780538 1.1111373
## 2062 0.0000000 0.9929707
## 2063 0.0000000 1.0014258
## 2064 0.0000000 0.9638191
## 2065 1.7917595 1.0396417
## 2066 0.0000000 0.9620968
## 2067 6.5792512 1.1611426
## 2068 0.0000000 0.9727708
## 2069 0.0000000 0.9459686
## 2070 0.0000000 0.9503833
## 2071 0.0000000 0.9529581
## 2072 0.0000000 0.9610055
## 2073 0.0000000 1.0050089
## 2074 0.6931472 1.0244771
## 2075 3.1780538 1.1068703
## 2076 1.7917595 1.0730308
## 2077 0.0000000 1.0020268
## 2078 0.0000000 1.0053755
## 2079 0.0000000 0.9663398
## 2080 0.0000000 0.9721649
## 2081 0.0000000 1.0157376
## 2082 0.0000000 0.9869504
## 2083 0.0000000 0.9927247
## 2084 1.7917595 1.0839513
## 2085 0.0000000 1.0057043
## 2086 0.0000000 0.9666393
## 2087 0.0000000 0.9724140
## 2088 0.0000000 1.0159769
## 2089 0.0000000 0.9540236
## 2090 0.0000000 1.0038162
## 2091 0.0000000 1.0070217
## 2092 0.6931472 1.0499351
## 2093 0.0000000 1.0146961
## 2094 0.0000000 1.0169350
## 2095 0.0000000 0.9668881
## 2096 0.0000000 1.0107734
## 2097 0.0000000 0.9712537
## 2098 0.0000000 0.9762493
## 2099 0.0000000 0.9781730
## 2100 0.0000000 0.9756350
## 2101 0.0000000 0.9769312
## 2102 0.0000000 1.0225641
## 2103 0.0000000 1.0241789
## 2104 0.0000000 1.0203887
## 2105 0.0000000 0.9825679
## 2106 0.0000000 0.9839882
## 2107 0.0000000 0.9835015
## 2108 0.0000000 0.9848470
## 2109 0.0000000 0.9875107
## 2110 33.5050735 1.1037911
## 2111 4.7874917 0.8765851
## 2112 12.8018275 1.0172669
## 2113 15.1044126 1.0775898
## 2114 8.5251614 1.0093242
## 2115 4.7874917 0.9714386
## 2116 3.1780538 0.9390464
## 2117 3.1780538 0.9969038
## 2118 4.7874917 1.0344580
## 2119 3.1780538 1.0037246
## 2120 0.6931472 0.9455982
## 2121 3.1780538 1.0466937
## 2122 0.6931472 0.9796397
## 2123 0.0000000 0.9463365
## 2124 0.6931472 1.0012671
## 2125 0.0000000 0.9682972
## 2126 12.8018275 1.0153954
## 2127 3.1780538 0.8743654
## 2128 19.9872145 1.1579321
## 2129 1.7917595 0.8885912
## 2130 3.1780538 0.9482418
## 2131 0.6931472 0.8809055
## 2132 6.5792512 1.0783336
## 2133 1.7917595 0.9702158
## 2134 6.5792512 1.0853455
## 2135 0.6931472 0.9526094
## 2136 4.7874917 1.0912258
## 2137 0.0000000 0.9080893
## 2138 0.0000000 0.9513650
## 2139 0.0000000 0.9663903
## 2140 0.6931472 1.0118195
## 2141 6.5792512 1.0189579
## 2142 0.0000000 0.8633018
## 2143 0.6931472 0.9163843
## 2144 0.6931472 0.9353511
## 2145 3.1780538 1.0096821
## 2146 0.0000000 0.9375943
## 2147 0.6931472 0.9766455
## 2148 1.7917595 1.0186020
## 2149 1.7917595 1.0294482
## 2150 0.0000000 0.9689763
## 2151 0.0000000 0.9748808
## 2152 1.7917595 1.0579421
## 2153 0.0000000 0.9489109
## 2154 0.0000000 0.9529422
## 2155 6.5792512 1.0504572
## 2156 0.6931472 0.9255659
## 2157 3.1780538 1.0174444
## 2158 3.1780538 1.0185822
## 2159 1.7917595 1.0204449
## 2160 0.6931472 0.9829076
## 2161 0.6931472 0.9863503
## 2162 3.1780538 1.0741344
## 2163 1.7917595 1.0525624
## 2164 0.6931472 1.0187620
## 2165 0.0000000 0.9422277
## 2166 0.0000000 0.9520183
## 2167 0.0000000 0.9963540
## 2168 3.1780538 1.0131645
## 2169 1.7917595 0.9937903
## 2170 0.6931472 0.9573719
## 2171 0.0000000 0.9528984
## 2172 0.0000000 0.9536520
## 2173 1.7917595 1.0345263
## 2174 0.0000000 0.9653584
## 2175 0.0000000 0.9800034
## 2176 0.0000000 0.9850737
## 2177 0.0000000 0.9880333
## 2178 0.0000000 0.9972943
## 2179 1.7917595 1.0823262
## 2180 0.6931472 0.9694887
## 2181 0.0000000 0.9324366
## 2182 0.0000000 0.9623230
## 2183 0.6931472 1.0021852
## 2184 0.0000000 0.9264445
## 2185 0.0000000 0.9735659
## 2186 1.7917595 1.0670352
## 2187 0.0000000 0.9912925
## 2188 0.0000000 0.9939468
## 2189 0.0000000 1.0022417
## 2190 0.0000000 0.9645416
## 2191 0.0000000 0.9454633
## 2192 0.6931472 1.0116272
## 2193 0.0000000 0.9331056
## 2194 0.0000000 0.9751040
## 2195 0.0000000 0.9420806
## 2196 0.0000000 0.9936715
## 2197 0.0000000 0.9976821
## 2198 0.6931472 1.0407062
## 2199 0.0000000 0.9663271
## 2200 0.0000000 0.9690290
## 2201 0.6931472 1.0122134
## 2202 0.6931472 1.0128373
## 2203 0.0000000 0.9756448
## 2204 1.7917595 1.0624759
## 2205 0.0000000 0.9536252
## 2206 0.0000000 0.9980497
## 2207 0.0000000 0.9596665
## 2208 0.0000000 0.9666065
## 2209 0.6931472 1.0515058
## 2210 0.0000000 0.9926296
## 2211 3.1780538 1.1157693
## 2212 0.0000000 0.9587637
## 2213 0.0000000 0.9668237
## 2214 0.0000000 1.0107116
## 2215 0.0000000 0.9711976
## 2216 0.6931472 1.0590085
## 2217 0.0000000 0.9781301
## 2218 0.0000000 0.9543399
## 2219 0.0000000 0.9998064
## 2220 0.0000000 1.0081366
## 2221 0.0000000 1.0109946
## 2222 0.6931472 1.0538581
## 2223 1.7917595 1.1006524
## 2224 0.0000000 0.9783270
## 2225 0.6931472 1.0420674
## 2226 0.0000000 0.9683019
## 2227 0.0000000 1.0121279
## 2228 0.0000000 1.0137317
## 2229 0.0000000 0.9772728
## 2230 0.0000000 0.9791154
## 2231 0.0000000 0.9716691
## 2232 0.0000000 1.0153528
## 2233 0.0000000 0.9754204
## 2234 0.0000000 0.9797070
## 2235 0.0000000 0.9813560
## 2236 0.0000000 0.9791802
## 2237 0.0000000 0.9802916
## 2238 0.0000000 0.9837429
## 2239 0.0000000 1.0268478
## 2240 0.0000000 0.9819554
## 2241 0.0000000 0.9851197
## 2242 0.0000000 1.0281677
## 2243 0.0000000 0.9859186
## 2244 0.0000000 0.9870695
## 2245 0.0000000 0.9893468
## 2246 4.7874917 0.8921978
## 2247 4.7874917 0.9074985
## 2248 17.5023078 1.1270201
## 2249 10.6046029 1.0571521
## 2250 8.5251614 1.0526354
## 2251 0.0000000 0.8474587
## 2252 6.5792512 1.0792782
## 2253 10.6046029 1.1537390
## 2254 0.6931472 0.9393581
## 2255 1.7917595 0.9906050
## 2256 3.1780538 1.0536967
## 2257 0.0000000 0.9086099
## 2258 0.6931472 0.9906121
## 2259 1.7917595 1.0455038
## 2260 0.6931472 1.0122441
## 2261 3.1780538 0.9511716
## 2262 8.5251614 1.0752021
## 2263 6.5792512 1.0607184
## 2264 4.7874917 1.0459546
## 2265 0.6931472 0.9372577
## 2266 0.6931472 0.9763537
## 2267 4.7874917 1.0918727
## 2268 1.7917595 1.0192143
## 2269 0.0000000 0.9137436
## 2270 1.7917595 1.0482853
## 2271 0.0000000 0.9752746
## 2272 0.6931472 1.0185274
## 2273 0.0000000 0.9894714
## 2274 0.0000000 0.9936500
## 2275 1.7917595 0.9448711
## 2276 0.0000000 0.8901947
## 2277 0.6931472 0.9444242
## 2278 4.7874917 1.0563670
## 2279 0.6931472 0.9826310
## 2280 0.0000000 0.9065832
## 2281 0.6931472 0.9869087
## 2282 3.1780538 1.0746814
## 2283 0.6931472 1.0133783
## 2284 0.6931472 1.0191488
## 2285 1.7917595 1.0624961
## 2286 0.0000000 0.9523032
## 2287 0.0000000 0.9966285
## 2288 1.7917595 0.9772415
## 2289 1.7917595 0.9945242
## 2290 1.7917595 0.9955302
## 2291 0.6931472 0.9921997
## 2292 0.0000000 0.9541584
## 2293 0.0000000 0.9571691
## 2294 0.6931472 1.0050986
## 2295 0.0000000 0.9803693
## 2296 0.0000000 0.9453186
## 2297 1.7917595 1.0687802
## 2298 0.0000000 0.9975634
## 2299 0.0000000 1.0011300
## 2300 0.6931472 0.9701316
## 2301 0.6931472 0.9710128
## 2302 0.0000000 0.9627841
## 2303 0.0000000 0.9634614
## 2304 1.7917595 1.0448012
## 2305 0.0000000 0.9343395
## 2306 0.6931472 1.0272301
## 2307 0.0000000 0.9915956
## 2308 0.0000000 0.9942349
## 2309 0.0000000 0.9616949
## 2310 0.0000000 1.0056722
## 2311 0.0000000 0.9460117
## 2312 0.6931472 1.0120528
## 2313 0.0000000 0.9730864
## 2314 0.0000000 0.9754967
## 2315 0.0000000 0.9823910
## 2316 0.0000000 0.9939612
## 2317 0.0000000 0.9573459
## 2318 0.0000000 0.9595747
## 2319 0.0000000 1.0075226
## 2320 0.0000000 1.0103227
## 2321 0.0000000 0.9334561
## 2322 0.6931472 1.0132564
## 2323 0.0000000 0.9760345
## 2324 0.0000000 0.9828767
## 2325 0.0000000 0.9943579
## 2326 0.0000000 0.9983145
## 2327 0.0000000 1.0006196
## 2328 0.0000000 0.9668078
## 2329 0.0000000 1.0105897
## 2330 0.0000000 0.9929251
## 2331 0.0000000 0.9946795
## 2332 0.0000000 0.9996840
## 2333 0.0000000 1.0080373
## 2334 0.0000000 0.9697731
## 2335 0.0000000 0.9713720
## 2336 0.0000000 0.9763475
## 2337 0.0000000 0.9782634
## 2338 0.0000000 0.9950988
## 2339 0.0000000 0.9593721
## 2340 0.6931472 1.0493693
## 2341 0.0000000 0.9700429
## 2342 0.0000000 1.0128368
## 2343 0.0000000 1.0179781
## 2344 0.0000000 0.9784592
## 2345 0.0000000 1.0015730
## 2346 0.0000000 0.9684932
## 2347 0.0000000 0.9711237
## 2348 0.0000000 0.9726532
## 2349 0.0000000 1.0188654
## 2350 0.0000000 1.0207747
## 2351 0.0000000 0.9718407
## 2352 0.0000000 0.9741992
## 2353 0.0000000 1.0169459
## 2354 0.0000000 0.9798310
## 2355 0.6931472 1.0647226
## 2356 0.0000000 0.9793073
## 2357 0.0000000 0.9804120
## 2358 0.0000000 0.9838426
## 2359 0.0000000 1.0269434
## 2360 0.0000000 1.0237174
## 2361 0.0000000 0.9852111
## 2362 0.0000000 1.0282552
## 2363 0.0000000 0.9860052
## 2364 0.0000000 0.9871491
## 2365 0.6931472 1.0733388
## 2366 3.1780538 0.9614707
## 2367 8.5251614 1.0858463
## 2368 3.1780538 0.9978335
## 2369 0.6931472 0.9435708
## 2370 3.1780538 1.0184887
## 2371 0.6931472 0.9819846
## 2372 3.1780538 1.0596922
## 2373 8.5251614 1.1790855
## 2374 0.6931472 0.9961790
## 2375 0.0000000 0.9337107
## 2376 0.0000000 0.9390625
## 2377 0.6931472 1.0221096
## 2378 0.6931472 1.0327375
## 2379 0.0000000 0.9557858
## 2380 12.8018275 1.1690211
## 2381 1.7917595 0.9707620
## 2382 4.7874917 1.0630838
## 2383 1.7917595 0.9897296
## 2384 1.7917595 1.0265938
## 2385 1.7917595 1.0274380
## 2386 4.7874917 1.1083982
## 2387 0.6931472 1.0014070
## 2388 0.6931472 1.0172202
## 2389 0.0000000 0.9827161
## 2390 0.0000000 0.9456819
## 2391 0.0000000 0.9954152
## 2392 0.6931472 1.0397969
## 2393 3.1780538 1.0218075
## 2394 1.7917595 1.0012845
## 2395 0.6931472 0.9645364
## 2396 0.6931472 0.9970818
## 2397 0.0000000 0.9588137
## 2398 0.0000000 0.9616790
## 2399 0.0000000 0.9698911
## 2400 0.0000000 0.9837270
## 2401 0.0000000 0.9482923
## 2402 1.7917595 1.0719681
## 2403 0.0000000 1.0000308
## 2404 0.6931472 1.0442341
## 2405 1.7917595 1.0142089
## 2406 0.6931472 0.9768902
## 2407 0.6931472 1.0063651
## 2408 0.6931472 1.0070353
## 2409 0.6931472 1.0097253
## 2410 1.7917595 1.0572025
## 2411 0.6931472 1.0303671
## 2412 0.6931472 1.0348332
## 2413 0.0000000 0.9563176
## 2414 0.0000000 1.0046895
## 2415 0.6931472 1.0487093
## 2416 0.0000000 0.9510555
## 2417 0.6931472 1.0159612
## 2418 0.6931472 1.0165550
## 2419 0.0000000 0.9392648
## 2420 0.6931472 1.0257481
## 2421 0.0000000 0.9966177
## 2422 0.0000000 1.0003961
## 2423 0.0000000 0.9618045
## 2424 0.0000000 1.0094602
## 2425 0.0000000 0.9709302
## 2426 1.7917595 1.0562566
## 2427 0.0000000 0.9375752
## 2428 0.6931472 1.0194688
## 2429 0.0000000 0.9861065
## 2430 0.0000000 0.9564294
## 2431 0.0000000 0.9600256
## 2432 0.0000000 0.9621199
## 2433 0.0000000 0.9686516
## 2434 0.0000000 0.9711725
## 2435 0.0000000 0.9956358
## 2436 0.0000000 0.9567217
## 2437 6.5792512 1.2058872
## 2438 0.0000000 0.9688559
## 2439 0.0000000 0.9714570
## 2440 0.0000000 0.9729694
## 2441 0.0000000 1.0191389
## 2442 0.0000000 0.9794844
## 2443 0.0000000 0.9976957
## 2444 0.6931472 1.0431808
## 2445 0.0000000 0.9691337
## 2446 0.0000000 0.9717123
## 2447 0.0000000 0.9732114
## 2448 1.7917595 1.1022960
## 2449 0.0000000 0.9796694
## 2450 0.0000000 0.9629860
## 2451 0.0000000 0.9702463
## 2452 0.0000000 0.9727346
## 2453 0.0000000 0.9741810
## 2454 0.0000000 0.9786790
## 2455 0.0000000 0.9804099
## 2456 0.0000000 0.9734126
## 2457 0.0000000 0.9756427
## 2458 0.0000000 0.9769385
## 2459 0.0000000 0.9809656
## 2460 0.0000000 1.0241847
## 2461 0.0000000 1.0220547
## 2462 0.0000000 0.9815146
## 2463 0.0000000 0.9847550
## 2464 0.0000000 0.9859997
## 2465 0.0000000 0.9830770
## 2466 0.0000000 0.9860474
## 2467 0.0000000 0.9871879
## 2468 0.0000000 0.9867972
## 2469 0.0000000 0.9878771
## 2470 0.0000000 0.9900136
## 2471 0.0000000 0.9269843
## 2472 0.6931472 0.9770390
## 2473 0.0000000 0.9124828
## 2474 0.0000000 0.9518982
## 2475 0.0000000 0.9371223
## 2476 0.0000000 0.9774332
## 2477 3.1780538 1.0992843
## 2478 0.0000000 0.9460635
## 2479 0.0000000 0.9970602
## 2480 0.0000000 0.9600843
## 2481 0.0000000 1.0029837
## 2482 0.0000000 1.0097827
## 2483 0.6931472 1.0535981
## 2484 0.0000000 0.9484885
## 2485 0.0000000 0.9597823
## 2486 0.0000000 0.9604358
## 2487 0.0000000 0.9431936
## 2488 0.6931472 1.0237346
## 2489 0.6931472 1.0258809
## 2490 0.0000000 0.9513163
## 2491 0.0000000 0.9607724
## 2492 0.0000000 0.9640236
## 2493 0.0000000 0.9659160
## 2494 0.0000000 0.9718122
## 2495 0.0000000 0.9740858
## 2496 0.0000000 0.9291318
## 2497 0.0000000 0.9691279
## 2498 0.0000000 0.9896080
## 2499 0.0000000 0.9900695
## 2500 1.7917595 1.0726356
## 2501 0.0000000 0.9972006
## 2502 0.0000000 1.0060217
## 2503 0.0000000 1.0090502
## 2504 0.0000000 1.0108119
## 2505 0.0000000 1.0162961
## 2506 0.0000000 0.9769732
## 2507 0.6931472 1.0179041
## 2508 0.0000000 0.9556664
## 2509 0.0000000 0.9560565
## 2510 0.0000000 0.9576198
## 2511 0.0000000 0.9620773
## 2512 0.0000000 1.0106293
## 2513 0.6931472 1.0545125
## 2514 0.6931472 1.0561195
## 2515 0.0000000 0.9781469
## 2516 0.0000000 0.9799201
## 2517 0.6931472 1.0371249
## 2518 0.0000000 0.9969810
## 2519 0.6931472 1.0392274
## 2520 0.6931472 1.0440248
## 2521 0.0000000 1.0108939
## 2522 0.0000000 0.9722915
## 2523 0.0000000 0.9737608
## 2524 0.0000000 0.9783304
## 2525 0.0000000 0.9800890
## 2526 0.0000000 1.0099380
## 2527 0.6931472 1.0522468
## 2528 0.0000000 0.9731647
## 2529 0.0000000 0.9784731
## 2530 0.0000000 0.9802873
## 2531 0.0000000 0.9813408
## 2532 0.0000000 0.9846112
## 2533 0.0000000 0.9858674
## 2534 0.0000000 1.0113878
## 2535 0.0000000 0.9734051
## 2536 0.0000000 0.9786670
## 2537 0.0000000 0.9804652
## 2538 0.0000000 1.0231372
## 2539 0.0000000 0.9847506
## 2540 0.0000000 0.9859956
## 2541 0.0000000 0.9743678
## 2542 0.0000000 0.9794432
## 2543 0.0000000 0.9811773
## 2544 0.0000000 0.9821840
## 2545 0.0000000 1.0270980
## 2546 0.0000000 1.0283489
## 2547 0.0000000 1.0232832
## 2548 0.0000000 0.9832005
## 2549 0.0000000 1.0258386
## 2550 0.0000000 1.0287502
## 2551 0.0000000 0.9879661
## 2552 0.0000000 0.9865511
## 2553 0.0000000 0.9872742
## 2554 0.0000000 0.9895157
## 2555 0.0000000 0.9903755
## 2556 0.0000000 0.9883555
## 2557 0.0000000 0.9904085
## 2558 0.0000000 0.9911957
## 2559 0.0000000 1.0329534
## 2560 0.0000000 0.9916711
## 2561 0.0000000 0.9931436
## 2562 1.7917595 1.0316084
## 2563 1.7917595 1.0431658
## 2564 0.0000000 0.9652857
## 2565 0.0000000 0.9867778
## 2566 0.0000000 0.9872627
## 2567 0.0000000 0.9892071
## 2568 1.7917595 1.0757044
## 2569 0.0000000 0.9631864
## 2570 0.0000000 0.9662445
## 2571 0.6931472 1.0501359
## 2572 0.0000000 0.9735655
## 2573 0.6931472 1.0584644
## 2574 0.6931472 1.0125293
## 2575 1.7917595 1.0527172
## 2576 1.7917595 1.0736100
## 2577 0.0000000 0.9528437
## 2578 3.1780538 1.1164484
## 2579 0.0000000 0.9592861
## 2580 0.0000000 1.0082705
## 2581 0.0000000 1.0111177
## 2582 0.0000000 0.9715671
## 2583 0.0000000 0.9765095
## 2584 0.0000000 1.0199092
## 2585 0.0000000 0.9819783
## 2586 0.0000000 0.9990280
## 2587 0.0000000 0.9587481
## 2588 0.6931472 1.0416698
## 2589 0.0000000 1.0053169
## 2590 0.0000000 0.9714025
## 2591 0.0000000 0.9737967
## 2592 0.0000000 0.9751882
## 2593 0.0000000 0.9795145
## 2594 0.0000000 0.9811788
## 2595 0.6931472 1.0400466
## 2596 0.0000000 0.9590878
## 2597 0.0000000 0.9605477
## 2598 0.0000000 0.9647086
## 2599 0.0000000 0.9716410
## 2600 0.0000000 0.9740158
## 2601 0.0000000 1.0167646
## 2602 0.0000000 0.9796868
## 2603 0.0000000 1.0229580
## 2604 0.0000000 1.0119521
## 2605 0.0000000 0.9718343
## 2606 0.0000000 1.0161809
## 2607 0.0000000 0.9798207
## 2608 0.0000000 0.9815235
## 2609 0.0000000 1.0241825
## 2610 0.0000000 0.9855802
## 2611 0.0000000 0.9867584
## 2612 0.0000000 0.9720862
## 2613 0.0000000 0.9750618
## 2614 0.0000000 1.0215667
## 2615 0.0000000 0.9816904
## 2616 0.0000000 1.0243474
## 2617 0.0000000 0.9857110
## 2618 0.0000000 0.9868786
## 2619 0.0000000 1.0173589
## 2620 0.0000000 1.0223262
## 2621 0.0000000 1.0240226
## 2622 0.0000000 0.9833033
## 2623 0.0000000 1.0280629
## 2624 0.0000000 0.9873599
## 2625 0.0000000 0.9828016
## 2626 0.0000000 0.9842569
## 2627 0.0000000 0.9851014
## 2628 0.0000000 0.9877210
## 2629 0.0000000 1.0306603
## 2630 0.0000000 0.9873995
## 2631 0.0000000 0.9880775
## 2632 0.0000000 0.9901790
## 2633 0.0000000 0.9909849
## 2634 0.0000000 1.0310409
## 2635 0.0000000 0.9910157
## 2636 0.0000000 0.9917535
## 2637 0.0000000 0.9915008
## 2638 0.0000000 0.9921991
## 2639 0.0000000 0.9935788
## 2640 0.0000000 0.9796270
## 2641 0.6931472 1.0200120
## 2642 0.6931472 1.0382722
## 2643 0.6931472 1.0386824
## 2644 0.0000000 0.9996538
## 2645 0.0000000 0.9633011
## 2646 0.0000000 1.0116626
## 2647 0.0000000 1.0142348
## 2648 0.0000000 1.0157299
## 2649 0.0000000 1.0203793
## 2650 0.0000000 0.9805797
## 2651 0.6931472 1.0280332
## 2652 0.0000000 0.9624818
## 2653 0.0000000 1.0036493
## 2654 0.6931472 1.0459293
## 2655 0.0000000 1.0089927
## 2656 0.0000000 1.0155750
## 2657 0.0000000 1.0178279
## 2658 0.0000000 0.9776716
## 2659 0.0000000 0.9815730
## 2660 0.0000000 0.9830730
## 2661 0.0000000 1.0036260
## 2662 0.0000000 0.9631216
## 2663 0.0000000 1.0053471
## 2664 0.0000000 0.9682067
## 2665 0.0000000 0.9744701
## 2666 0.0000000 0.9766137
## 2667 0.0000000 0.9778590
## 2668 0.0000000 0.9817283
## 2669 0.0000000 0.9832159
## 2670 0.0000000 0.9736920
## 2671 0.0000000 1.0159814
## 2672 0.0000000 0.9773538
## 2673 0.0000000 0.9818490
## 2674 0.0000000 0.9833836
## 2675 0.0000000 0.9842742
## 2676 0.0000000 0.9870373
## 2677 0.0000000 0.9880978
## 2678 0.0000000 0.9748721
## 2679 0.0000000 0.9775575
## 2680 0.0000000 1.0236623
## 2681 0.0000000 1.0252478
## 2682 0.0000000 1.0261679
## 2683 0.0000000 0.9871550
## 2684 0.0000000 1.0301180
## 2685 0.0000000 0.9783733
## 2686 0.0000000 0.9826697
## 2687 0.0000000 0.9841360
## 2688 0.0000000 1.0267623
## 2689 0.0000000 0.9876263
## 2690 0.0000000 0.9886393
## 2691 0.0000000 0.9845350
## 2692 0.0000000 1.0276576
## 2693 0.0000000 0.9866062
## 2694 0.0000000 0.9889641
## 2695 0.0000000 0.9898687
## 2696 0.0000000 0.9886749
## 2697 0.0000000 0.9892850
## 2698 0.0000000 1.0332134
## 2699 0.0000000 1.0339690
## 2700 0.6931472 1.0741900
## 2701 0.0000000 0.9919281
## 2702 0.0000000 1.0346895
## 2703 0.0000000 1.0344526
## 2704 0.0000000 0.9929921
## 2705 0.0000000 0.9942324
## 2706 0.0000000 0.9937747
## 2707 0.0000000 1.0076143
## 2708 0.0000000 1.0079239
## 2709 0.0000000 1.0091643
## 2710 0.0000000 0.9714924
## 2711 0.0000000 1.0185658
## 2712 0.0000000 0.9790494
## 2713 1.7917595 1.1048808
## 2714 0.0000000 0.9836402
## 2715 0.0000000 0.9849745
## 2716 1.7917595 1.0899193
## 2717 0.0000000 0.9671896
## 2718 0.0000000 0.9683700
## 2719 0.0000000 1.0129434
## 2720 0.0000000 0.9773157
## 2721 0.0000000 0.9792255
## 2722 0.0000000 0.9803345
## 2723 0.6931472 1.0672268
## 2724 0.0000000 1.0268819
## 2725 0.0000000 0.9766223
## 2726 0.0000000 1.0189279
## 2727 0.0000000 0.9798846
## 2728 0.0000000 0.9838858
## 2729 0.0000000 0.9852507
## 2730 0.0000000 0.9860427
## 2731 0.0000000 1.0304230
## 2732 0.0000000 0.9894409
## 2733 0.0000000 0.9776739
## 2734 0.0000000 0.9800661
## 2735 0.0000000 0.9840317
## 2736 0.0000000 0.9853845
## 2737 0.0000000 0.9861693
## 2738 0.0000000 1.0305321
## 2739 0.0000000 0.9895371
## 2740 0.0000000 0.9807924
## 2741 0.0000000 0.9846158
## 2742 0.0000000 0.9859198
## 2743 0.0000000 1.0285234
## 2744 0.0000000 1.0309686
## 2745 0.0000000 0.9899219
## 2746 0.0000000 0.9862746
## 2747 0.0000000 0.9874398
## 2748 0.0000000 0.9881156
## 2749 0.0000000 0.9902105
## 2750 0.0000000 0.9910138
## 2751 0.0000000 0.9899535
## 2752 0.0000000 0.9904954
## 2753 0.0000000 0.9921741
## 2754 0.0000000 0.9928174
## 2755 0.0000000 1.0333489
## 2756 0.0000000 0.9928421
## 2757 0.0000000 0.9934309
## 2758 0.0000000 0.9932292
## 2759 0.0000000 0.9937864
## 2760 0.0000000 0.9948868
## 2761 0.0000000 0.9710934
## 2762 0.0000000 0.9713518
## 2763 0.0000000 1.0136278
## 2764 0.0000000 1.0166970
## 2765 0.0000000 0.9802196
## 2766 0.0000000 0.9818894
## 2767 0.0000000 1.0245439
## 2768 0.0000000 1.0276797
## 2769 0.0000000 1.0288838
## 2770 0.0000000 0.9796131
## 2771 0.0000000 0.9803556
## 2772 0.0000000 1.0241341
## 2773 0.0000000 0.9859607
## 2774 0.0000000 0.9871522
## 2775 0.0000000 0.9878433
## 2776 0.0000000 0.9899857
## 2777 0.0000000 0.9908072
## 2778 0.0000000 0.9805329
## 2779 0.0000000 0.9826241
## 2780 0.0000000 0.9860881
## 2781 0.0000000 0.9872689
## 2782 0.0000000 0.9879538
## 2783 0.0000000 1.0320682
## 2784 0.0000000 0.9908911
## 2785 0.0000000 0.9832589
## 2786 0.0000000 1.0284418
## 2787 0.0000000 0.9877361
## 2788 0.0000000 0.9883962
## 2789 0.0000000 0.9904420
## 2790 0.0000000 0.9912265
## 2791 0.0000000 0.9880457
## 2792 0.0000000 0.9890622
## 2793 0.0000000 0.9896516
## 2794 0.0000000 0.9914780
## 2795 0.0000000 0.9921782
## 2796 0.0000000 0.9912541
## 2797 0.0000000 0.9917264
## 2798 0.0000000 0.9931892
## 2799 0.0000000 0.9937496
## 2800 0.0000000 0.9924323
## 2801 0.0000000 0.9937711
## 2802 0.0000000 0.9942839
## 2803 0.0000000 0.9941083
## 2804 0.0000000 0.9945935
## 2805 0.0000000 1.0377751
## 2806 0.0000000 0.9797846
## 2807 0.0000000 0.9805210
## 2808 0.0000000 1.0242883
## 2809 0.0000000 0.9860795
## 2810 0.0000000 0.9872611
## 2811 0.0000000 0.9879464
## 2812 0.0000000 1.0320618
## 2813 0.0000000 0.9908854
## 2814 0.0000000 1.0222902
## 2815 0.0000000 1.0244522
## 2816 0.0000000 0.9862058
## 2817 0.0000000 0.9873768
## 2818 0.0000000 0.9880560
## 2819 0.0000000 1.0321561
## 2820 0.0000000 0.9909686
## 2821 0.0000000 0.9834002
## 2822 0.0000000 0.9867115
## 2823 0.0000000 0.9878401
## 2824 0.0000000 0.9884946
## 2825 0.0000000 0.9905233
## 2826 0.0000000 0.9913012
## 2827 0.0000000 1.0300565
## 2828 0.0000000 0.9891550
## 2829 0.0000000 0.9897395
## 2830 0.0000000 1.0336044
## 2831 0.0000000 0.9922448
## 2832 0.0000000 0.9913285
## 2833 0.0000000 0.9917968
## 2834 0.0000000 0.9932472
## 2835 0.0000000 0.9938029
## 2836 0.0000000 0.9924968
## 2837 0.0000000 0.9938242
## 2838 0.0000000 1.0365045
## 2839 0.0000000 0.9941585
## 2840 0.0000000 0.9946396
## 2841 0.0000000 0.9955896
## 2842 0.0000000 1.0281324
## 2843 0.0000000 0.9877806
## 2844 0.0000000 0.9902268
## 2845 0.0000000 0.9910595
## 2846 0.0000000 0.9915422
## 2847 0.0000000 0.9930373
## 2848 0.0000000 0.9936102
## 2849 0.0000000 0.9882292
## 2850 0.0000000 0.9905865
## 2851 0.0000000 1.0334358
## 2852 0.0000000 1.0339206
## 2853 0.0000000 1.0354220
## 2854 0.0000000 0.9938461
## 2855 0.0000000 1.0336632
## 2856 0.0000000 0.9923230
## 2857 0.0000000 0.9927380
## 2858 0.0000000 0.9940230
## 2859 0.0000000 0.9945152
## 2860 0.0000000 0.9938655
## 2861 0.0000000 0.9941976
## 2862 0.0000000 0.9952256
## 2863 0.0000000 0.9956192
## 2864 0.0000000 0.9946938
## 2865 0.0000000 0.9956342
## 2866 0.0000000 0.9959943
## 2867 0.0000000 0.9958710
## 2868 0.0000000 0.9962116
## 2869 0.0000000 0.9968839
## 2870 0.0000000 1.0302538
## 2871 0.0000000 1.0326888
## 2872 0.0000000 0.9914674
## 2873 0.0000000 0.9919283
## 2874 0.0000000 0.9933556
## 2875 0.0000000 0.9939024
## 2876 0.0000000 0.9916835
## 2877 0.0000000 0.9923931
## 2878 0.0000000 0.9928044
## 2879 0.0000000 1.0362387
## 2880 0.0000000 0.9945654
## 2881 0.0000000 0.9939216
## 2882 0.0000000 0.9942507
## 2883 0.0000000 0.9952693
## 2884 0.0000000 1.0378874
## 2885 0.0000000 0.9947424
## 2886 0.0000000 0.9956742
## 2887 0.0000000 0.9960310
## 2888 0.0000000 0.9959088
## 2889 0.0000000 0.9962463
## 2890 0.0000000 0.9969125
## 2891 0.0000000 0.9919900
## 2892 0.0000000 1.0347751
## 2893 0.0000000 0.9930698
## 2894 0.0000000 0.9942964
## 2895 0.0000000 0.9947662
## 2896 0.0000000 0.9941461
## 2897 0.0000000 0.9944631
## 2898 0.0000000 0.9954443
## 2899 0.0000000 0.9958199
## 2900 0.0000000 0.9949367
## 2901 0.0000000 1.0380698
## 2902 0.0000000 0.9961779
## 2903 0.0000000 0.9960602
## 2904 0.0000000 0.9963853
## 2905 0.0000000 1.0393129
## 2906 0.0000000 0.9947827
## 2907 0.0000000 1.0372683
## 2908 0.0000000 0.9959403
## 2909 0.0000000 0.9962752
## 2910 0.0000000 0.9954878
## 2911 0.0000000 0.9962880
## 2912 0.0000000 0.9965943
## 2913 0.0000000 0.9964894
## 2914 0.0000000 0.9967792
## 2915 0.0000000 0.9973510
## 2916 0.0000000 0.9963967
## 2917 0.0000000 0.9970362
## 2918 0.0000000 0.9972810
## 2919 0.0000000 0.9971972
## 2920 0.0000000 0.9974287
## 2921 0.0000000 0.9978854
## 2922 0.0000000 0.9974375
## 2923 0.0000000 0.9976492
## 2924 0.0000000 0.9980669
## 2925 0.0000000 0.9981719#Association Rule Mining (ARM)
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writeattach(dataset)## The following objects are masked _by_ .GlobalEnv:
##
## x1, x10, x11, x12, x13, x14, x15, x16, x17, x18, x19, x2, x20, x21,
## x22, x23, x24, x25, x26, x27, x3, x4, x5, x6, x7, x8, x9rules2=apriori(dataset,
parameter=list(supp=0.000001,conf=0.000001,minlen=2,maxlen=2),
appearance=list(none=c(
"x1=0","x2=0", "x3=0", "x4=0", "x5=0", "x6=0", "x7=0", "x8=0", "x9=0", "x10=0",
"x11=0", "x12=0", "x13=0", "x14=0", "x15=0", "x16=0", "x17=0", "x18=0", "x19=0", "x20=0",
"x21=0", "x22=0", "x23=0", "x24=0", "x25=0", "x26=0", "x27=0")))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 1e-06 0.1 1 none FALSE TRUE 5 1e-06 2
## maxlen target ext
## 2 rules TRUE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 0
##
## set item appearances ...[27 item(s)] done [0.00s].
## set transactions ...[54 item(s), 10000 transaction(s)] done [0.01s].
## sorting and recoding items ... [27 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2## Warning in apriori(dataset, parameter = list(supp = 1e-06, conf = 1e-06, :
## Mining stopped (maxlen reached). Only patterns up to a length of 2 returned!## done [0.00s].
## writing ... [702 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].quality(rules2)=round(quality(rules2),digits=3)
summary(rules2)## set of 702 rules
##
## rule length distribution (lhs + rhs):sizes
## 2
## 702
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 2 2 2 2 2
##
## summary of quality measures:
## support confidence coverage lift
## Min. :0.00000 Min. :0.00500 Min. :0.0140 Min. :0.3150
## 1st Qu.:0.00200 1st Qu.:0.02825 1st Qu.:0.0310 1st Qu.:0.9123
## Median :0.00400 Median :0.06900 Median :0.0670 Median :0.9910
## Mean :0.01026 Mean :0.10206 Mean :0.1033 Mean :0.9798
## 3rd Qu.:0.01200 3rd Qu.:0.14775 3rd Qu.:0.1520 3rd Qu.:1.0673
## Max. :0.11100 Max. :0.42000 Max. :0.3330 Max. :2.1690
## count
## Min. : 1.0
## 1st Qu.: 19.0
## Median : 43.0
## Mean : 102.5
## 3rd Qu.: 120.5
## Max. :1113.0
##
## mining info:
## data ntransactions support confidence
## dataset 10000 1e-06 1e-06
## call
## apriori(data = dataset, parameter = list(supp = 1e-06, conf = 1e-06, minlen = 2, maxlen = 2), appearance = list(none = c("x1=0", "x2=0", "x3=0", "x4=0", "x5=0", "x6=0", "x7=0", "x8=0", "x9=0", "x10=0", "x11=0", "x12=0", "x13=0", "x14=0", "x15=0", "x16=0", "x17=0", "x18=0", "x19=0", "x20=0", "x21=0", "x22=0", "x23=0", "x24=0", "x25=0", "x26=0", "x27=0")))inspect(rules2)## lhs rhs support confidence coverage lift count
## [1] {x27=1} => {x26=1} 0.000 0.021 0.014 1.327 3
## [2] {x26=1} => {x27=1} 0.000 0.019 0.016 1.327 3
## [3] {x27=1} => {x25=1} 0.000 0.007 0.014 0.364 1
## [4] {x25=1} => {x27=1} 0.000 0.005 0.019 0.364 1
## [5] {x27=1} => {x24=1} 0.000 0.014 0.014 0.688 2
## [6] {x24=1} => {x27=1} 0.000 0.010 0.020 0.688 2
## [7] {x27=1} => {x23=1} 0.000 0.021 0.014 0.943 3
## [8] {x23=1} => {x27=1} 0.000 0.014 0.022 0.943 3
## [9] {x27=1} => {x22=1} 0.000 0.028 0.014 1.003 4
## [10] {x22=1} => {x27=1} 0.000 0.014 0.028 1.003 4
## [11] {x27=1} => {x21=1} 0.000 0.035 0.014 1.116 5
## [12] {x21=1} => {x27=1} 0.000 0.016 0.031 1.116 5
## [13] {x27=1} => {x20=1} 0.000 0.014 0.014 0.430 2
## [14] {x20=1} => {x27=1} 0.000 0.006 0.032 0.430 2
## [15] {x27=1} => {x19=1} 0.000 0.014 0.014 0.426 2
## [16] {x19=1} => {x27=1} 0.000 0.006 0.033 0.426 2
## [17] {x27=1} => {x18=1} 0.001 0.056 0.014 1.203 8
## [18] {x18=1} => {x27=1} 0.001 0.017 0.046 1.203 8
## [19] {x27=1} => {x17=1} 0.000 0.021 0.014 0.447 3
## [20] {x17=1} => {x27=1} 0.000 0.006 0.047 0.447 3
## [21] {x27=1} => {x16=1} 0.001 0.056 0.014 1.036 8
## [22] {x16=1} => {x27=1} 0.001 0.015 0.054 1.036 8
## [23] {x27=1} => {x15=1} 0.001 0.069 0.014 1.148 10
## [24] {x15=1} => {x27=1} 0.001 0.017 0.060 1.148 10
## [25] {x27=1} => {x14=1} 0.001 0.076 0.014 1.133 11
## [26] {x14=1} => {x27=1} 0.001 0.016 0.067 1.133 11
## [27] {x27=1} => {x13=1} 0.001 0.062 0.014 0.868 9
## [28] {x13=1} => {x27=1} 0.001 0.013 0.072 0.868 9
## [29] {x27=1} => {x12=1} 0.002 0.104 0.014 0.990 15
## [30] {x12=1} => {x27=1} 0.002 0.014 0.105 0.990 15
## [31] {x27=1} => {x11=1} 0.002 0.139 0.014 1.245 20
## [32] {x11=1} => {x27=1} 0.002 0.018 0.112 1.245 20
## [33] {x27=1} => {x10=1} 0.001 0.097 0.014 0.866 14
## [34] {x10=1} => {x27=1} 0.001 0.012 0.112 0.866 14
## [35] {x27=1} => {x9=1} 0.002 0.146 0.014 1.106 21
## [36] {x9=1} => {x27=1} 0.002 0.016 0.132 1.106 21
## [37] {x27=1} => {x7=1} 0.002 0.174 0.014 1.214 25
## [38] {x7=1} => {x27=1} 0.002 0.017 0.143 1.214 25
## [39] {x27=1} => {x8=1} 0.003 0.181 0.014 1.189 26
## [40] {x8=1} => {x27=1} 0.003 0.017 0.152 1.189 26
## [41] {x27=1} => {x5=1} 0.002 0.174 0.014 1.036 25
## [42] {x5=1} => {x27=1} 0.002 0.015 0.168 1.036 25
## [43] {x27=1} => {x6=1} 0.002 0.174 0.014 1.003 25
## [44] {x6=1} => {x27=1} 0.002 0.014 0.173 1.003 25
## [45] {x27=1} => {x4=1} 0.003 0.215 0.014 1.042 31
## [46] {x4=1} => {x27=1} 0.003 0.015 0.207 1.042 31
## [47] {x27=1} => {x3=1} 0.004 0.271 0.014 1.033 39
## [48] {x3=1} => {x27=1} 0.004 0.015 0.262 1.033 39
## [49] {x27=1} => {x2=1} 0.005 0.354 0.014 1.076 51
## [50] {x2=1} => {x27=1} 0.005 0.015 0.329 1.076 51
## [51] {x27=1} => {x1=1} 0.004 0.264 0.014 0.792 38
## [52] {x1=1} => {x27=1} 0.004 0.011 0.333 0.792 38
## [53] {x26=1} => {x25=1} 0.000 0.019 0.016 1.000 3
## [54] {x25=1} => {x26=1} 0.000 0.016 0.019 1.000 3
## [55] {x26=1} => {x24=1} 0.000 0.006 0.016 0.315 1
## [56] {x24=1} => {x26=1} 0.000 0.005 0.020 0.315 1
## [57] {x26=1} => {x23=1} 0.000 0.013 0.016 0.576 2
## [58] {x23=1} => {x26=1} 0.000 0.009 0.022 0.576 2
## [59] {x26=1} => {x22=1} 0.000 0.019 0.016 0.690 3
## [60] {x22=1} => {x26=1} 0.000 0.011 0.028 0.690 3
## [61] {x26=1} => {x21=1} 0.001 0.038 0.016 1.229 6
## [62] {x21=1} => {x26=1} 0.001 0.019 0.031 1.229 6
## [63] {x26=1} => {x20=1} 0.001 0.070 0.016 2.169 11
## [64] {x20=1} => {x26=1} 0.001 0.034 0.032 2.169 11
## [65] {x26=1} => {x19=1} 0.001 0.038 0.016 1.172 6
## [66] {x19=1} => {x26=1} 0.001 0.018 0.033 1.172 6
## [67] {x26=1} => {x18=1} 0.001 0.051 0.016 1.103 8
## [68] {x18=1} => {x26=1} 0.001 0.017 0.046 1.103 8
## [69] {x26=1} => {x17=1} 0.001 0.038 0.016 0.820 6
## [70] {x17=1} => {x26=1} 0.001 0.013 0.047 0.820 6
## [71] {x26=1} => {x16=1} 0.001 0.057 0.016 1.069 9
## [72] {x16=1} => {x26=1} 0.001 0.017 0.054 1.069 9
## [73] {x26=1} => {x15=1} 0.001 0.064 0.016 1.053 10
## [74] {x15=1} => {x26=1} 0.001 0.017 0.060 1.053 10
## [75] {x26=1} => {x14=1} 0.001 0.038 0.016 0.567 6
## [76] {x14=1} => {x26=1} 0.001 0.009 0.067 0.567 6
## [77] {x26=1} => {x13=1} 0.001 0.070 0.016 0.973 11
## [78] {x13=1} => {x26=1} 0.001 0.015 0.072 0.973 11
## [79] {x26=1} => {x12=1} 0.002 0.121 0.016 1.150 19
## [80] {x12=1} => {x26=1} 0.002 0.018 0.105 1.150 19
## [81] {x26=1} => {x11=1} 0.002 0.121 0.016 1.084 19
## [82] {x11=1} => {x26=1} 0.002 0.017 0.112 1.084 19
## [83] {x26=1} => {x10=1} 0.002 0.127 0.016 1.134 20
## [84] {x10=1} => {x26=1} 0.002 0.018 0.112 1.134 20
## [85] {x26=1} => {x9=1} 0.002 0.121 0.016 0.918 19
## [86] {x9=1} => {x26=1} 0.002 0.014 0.132 0.918 19
## [87] {x26=1} => {x7=1} 0.002 0.096 0.016 0.668 15
## [88] {x7=1} => {x26=1} 0.002 0.010 0.143 0.668 15
## [89] {x26=1} => {x8=1} 0.003 0.178 0.016 1.175 28
## [90] {x8=1} => {x26=1} 0.003 0.018 0.152 1.175 28
## [91] {x26=1} => {x5=1} 0.002 0.153 0.016 0.913 24
## [92] {x5=1} => {x26=1} 0.002 0.014 0.167 0.913 24
## [93] {x26=1} => {x6=1} 0.004 0.236 0.016 1.361 37
## [94] {x6=1} => {x26=1} 0.004 0.021 0.173 1.361 37
## [95] {x26=1} => {x4=1} 0.002 0.134 0.016 0.647 21
## [96] {x4=1} => {x26=1} 0.002 0.010 0.207 0.647 21
## [97] {x26=1} => {x3=1} 0.004 0.255 0.016 0.972 40
## [98] {x3=1} => {x26=1} 0.004 0.015 0.262 0.972 40
## [99] {x26=1} => {x2=1} 0.005 0.325 0.016 0.987 51
## [100] {x2=1} => {x26=1} 0.005 0.015 0.329 0.987 51
## [101] {x26=1} => {x1=1} 0.007 0.420 0.016 1.262 66
## [102] {x1=1} => {x26=1} 0.007 0.020 0.333 1.262 66
## [103] {x25=1} => {x24=1} 0.001 0.031 0.019 1.555 6
## [104] {x24=1} => {x25=1} 0.001 0.030 0.020 1.555 6
## [105] {x25=1} => {x23=1} 0.000 0.016 0.019 0.711 3
## [106] {x23=1} => {x25=1} 0.000 0.014 0.022 0.711 3
## [107] {x25=1} => {x22=1} 0.000 0.021 0.019 0.756 4
## [108] {x22=1} => {x25=1} 0.000 0.014 0.028 0.756 4
## [109] {x25=1} => {x21=1} 0.000 0.026 0.019 0.842 5
## [110] {x21=1} => {x25=1} 0.000 0.016 0.031 0.842 5
## [111] {x25=1} => {x20=1} 0.001 0.037 0.019 1.135 7
## [112] {x20=1} => {x25=1} 0.001 0.022 0.032 1.135 7
## [113] {x25=1} => {x19=1} 0.000 0.021 0.019 0.642 4
## [114] {x19=1} => {x25=1} 0.000 0.012 0.033 0.642 4
## [115] {x25=1} => {x18=1} 0.001 0.037 0.019 0.793 7
## [116] {x18=1} => {x25=1} 0.001 0.015 0.046 0.793 7
## [117] {x25=1} => {x17=1} 0.001 0.052 0.019 1.124 10
## [118] {x17=1} => {x25=1} 0.001 0.021 0.047 1.124 10
## [119] {x25=1} => {x16=1} 0.001 0.058 0.019 1.074 11
## [120] {x16=1} => {x25=1} 0.001 0.021 0.054 1.074 11
## [121] {x25=1} => {x15=1} 0.002 0.094 0.019 1.558 18
## [122] {x15=1} => {x25=1} 0.002 0.030 0.060 1.558 18
## [123] {x25=1} => {x14=1} 0.002 0.079 0.019 1.165 15
## [124] {x14=1} => {x25=1} 0.002 0.022 0.067 1.165 15
## [125] {x25=1} => {x13=1} 0.002 0.084 0.019 1.163 16
## [126] {x13=1} => {x25=1} 0.002 0.022 0.072 1.163 16
## [127] {x25=1} => {x12=1} 0.002 0.089 0.019 0.846 17
## [128] {x12=1} => {x25=1} 0.002 0.016 0.105 0.846 17
## [129] {x25=1} => {x11=1} 0.002 0.084 0.019 0.751 16
## [130] {x11=1} => {x25=1} 0.002 0.014 0.112 0.751 16
## [131] {x25=1} => {x10=1} 0.002 0.089 0.019 0.793 17
## [132] {x10=1} => {x25=1} 0.002 0.015 0.112 0.793 17
## [133] {x25=1} => {x9=1} 0.002 0.105 0.019 0.794 20
## [134] {x9=1} => {x25=1} 0.002 0.015 0.132 0.794 20
## [135] {x25=1} => {x7=1} 0.003 0.141 0.019 0.989 27
## [136] {x7=1} => {x25=1} 0.003 0.019 0.143 0.989 27
## [137] {x25=1} => {x8=1} 0.003 0.147 0.019 0.966 28
## [138] {x8=1} => {x25=1} 0.003 0.018 0.152 0.966 28
## [139] {x25=1} => {x5=1} 0.004 0.220 0.019 1.313 42
## [140] {x5=1} => {x25=1} 0.004 0.025 0.167 1.313 42
## [141] {x25=1} => {x6=1} 0.003 0.157 0.019 0.907 30
## [142] {x6=1} => {x25=1} 0.003 0.017 0.173 0.907 30
## [143] {x25=1} => {x4=1} 0.003 0.147 0.019 0.710 28
## [144] {x4=1} => {x25=1} 0.003 0.014 0.207 0.710 28
## [145] {x25=1} => {x3=1} 0.004 0.236 0.019 0.899 45
## [146] {x3=1} => {x25=1} 0.004 0.017 0.262 0.899 45
## [147] {x25=1} => {x2=1} 0.006 0.325 0.019 0.986 62
## [148] {x2=1} => {x25=1} 0.006 0.019 0.329 0.986 62
## [149] {x25=1} => {x1=1} 0.006 0.330 0.019 0.991 63
## [150] {x1=1} => {x25=1} 0.006 0.019 0.333 0.991 63
## [151] {x24=1} => {x23=1} 0.000 0.010 0.020 0.448 2
## [152] {x23=1} => {x24=1} 0.000 0.009 0.022 0.448 2
## [153] {x24=1} => {x22=1} 0.000 0.020 0.020 0.715 4
## [154] {x22=1} => {x24=1} 0.000 0.014 0.028 0.715 4
## [155] {x24=1} => {x21=1} 0.001 0.035 0.020 1.114 7
## [156] {x21=1} => {x24=1} 0.001 0.023 0.031 1.114 7
## [157] {x24=1} => {x20=1} 0.000 0.025 0.020 0.766 5
## [158] {x20=1} => {x24=1} 0.000 0.015 0.032 0.766 5
## [159] {x24=1} => {x19=1} 0.000 0.020 0.020 0.607 4
## [160] {x19=1} => {x24=1} 0.000 0.012 0.033 0.607 4
## [161] {x24=1} => {x18=1} 0.000 0.020 0.020 0.429 4
## [162] {x18=1} => {x24=1} 0.000 0.009 0.046 0.429 4
## [163] {x24=1} => {x17=1} 0.001 0.059 0.020 1.275 12
## [164] {x17=1} => {x24=1} 0.001 0.026 0.047 1.275 12
## [165] {x24=1} => {x16=1} 0.001 0.059 0.020 1.108 12
## [166] {x16=1} => {x24=1} 0.001 0.022 0.054 1.108 12
## [167] {x24=1} => {x15=1} 0.001 0.069 0.020 1.146 14
## [168] {x15=1} => {x24=1} 0.001 0.023 0.061 1.146 14
## [169] {x24=1} => {x14=1} 0.001 0.045 0.020 0.661 9
## [170] {x14=1} => {x24=1} 0.001 0.013 0.067 0.661 9
## [171] {x24=1} => {x13=1} 0.002 0.084 0.020 1.169 17
## [172] {x13=1} => {x24=1} 0.002 0.024 0.072 1.169 17
## [173] {x24=1} => {x12=1} 0.002 0.079 0.020 0.753 16
## [174] {x12=1} => {x24=1} 0.002 0.015 0.105 0.753 16
## [175] {x24=1} => {x11=1} 0.002 0.104 0.020 0.932 21
## [176] {x11=1} => {x24=1} 0.002 0.019 0.112 0.932 21
## [177] {x24=1} => {x10=1} 0.002 0.124 0.020 1.102 25
## [178] {x10=1} => {x24=1} 0.002 0.022 0.112 1.102 25
## [179] {x24=1} => {x9=1} 0.002 0.109 0.020 0.826 22
## [180] {x9=1} => {x24=1} 0.002 0.017 0.132 0.826 22
## [181] {x24=1} => {x7=1} 0.002 0.104 0.020 0.727 21
## [182] {x7=1} => {x24=1} 0.002 0.015 0.143 0.727 21
## [183] {x24=1} => {x8=1} 0.003 0.149 0.020 0.978 30
## [184] {x8=1} => {x24=1} 0.003 0.020 0.152 0.978 30
## [185] {x24=1} => {x5=1} 0.003 0.134 0.020 0.798 27
## [186] {x5=1} => {x24=1} 0.003 0.016 0.168 0.798 27
## [187] {x24=1} => {x6=1} 0.004 0.183 0.020 1.058 37
## [188] {x6=1} => {x24=1} 0.004 0.021 0.173 1.058 37
## [189] {x24=1} => {x4=1} 0.004 0.203 0.020 0.982 41
## [190] {x4=1} => {x24=1} 0.004 0.020 0.207 0.982 41
## [191] {x24=1} => {x3=1} 0.004 0.218 0.020 0.831 44
## [192] {x3=1} => {x24=1} 0.004 0.017 0.262 0.831 44
## [193] {x24=1} => {x2=1} 0.006 0.312 0.020 0.948 63
## [194] {x2=1} => {x24=1} 0.006 0.019 0.329 0.948 63
## [195] {x24=1} => {x1=1} 0.006 0.307 0.020 0.922 62
## [196] {x1=1} => {x24=1} 0.006 0.019 0.333 0.922 62
## [197] {x23=1} => {x22=1} 0.001 0.027 0.022 0.980 6
## [198] {x22=1} => {x23=1} 0.001 0.022 0.028 0.980 6
## [199] {x23=1} => {x21=1} 0.000 0.023 0.022 0.727 5
## [200] {x21=1} => {x23=1} 0.000 0.016 0.031 0.727 5
## [201] {x23=1} => {x20=1} 0.001 0.045 0.022 1.401 10
## [202] {x20=1} => {x23=1} 0.001 0.031 0.032 1.401 10
## [203] {x23=1} => {x19=1} 0.000 0.018 0.022 0.555 4
## [204] {x19=1} => {x23=1} 0.000 0.012 0.033 0.555 4
## [205] {x23=1} => {x18=1} 0.001 0.027 0.022 0.588 6
## [206] {x18=1} => {x23=1} 0.001 0.013 0.046 0.588 6
## [207] {x23=1} => {x17=1} 0.001 0.050 0.022 1.068 11
## [208] {x17=1} => {x23=1} 0.001 0.024 0.047 1.068 11
## [209] {x23=1} => {x16=1} 0.001 0.050 0.022 0.929 11
## [210] {x16=1} => {x23=1} 0.001 0.021 0.054 0.929 11
## [211] {x23=1} => {x15=1} 0.001 0.054 0.022 0.897 12
## [212] {x15=1} => {x23=1} 0.001 0.020 0.060 0.897 12
## [213] {x23=1} => {x14=1} 0.002 0.072 0.022 1.074 16
## [214] {x14=1} => {x23=1} 0.002 0.024 0.067 1.074 16
## [215] {x23=1} => {x13=1} 0.002 0.081 0.022 1.131 18
## [216] {x13=1} => {x23=1} 0.002 0.025 0.072 1.131 18
## [217] {x23=1} => {x12=1} 0.002 0.081 0.022 0.774 18
## [218] {x12=1} => {x23=1} 0.002 0.017 0.105 0.774 18
## [219] {x23=1} => {x11=1} 0.003 0.127 0.022 1.135 28
## [220] {x11=1} => {x23=1} 0.003 0.025 0.112 1.135 28
## [221] {x23=1} => {x10=1} 0.003 0.136 0.022 1.209 30
## [222] {x10=1} => {x23=1} 0.003 0.027 0.112 1.209 30
## [223] {x23=1} => {x9=1} 0.003 0.149 0.022 1.132 33
## [224] {x9=1} => {x23=1} 0.003 0.025 0.132 1.132 33
## [225] {x23=1} => {x7=1} 0.003 0.154 0.022 1.076 34
## [226] {x7=1} => {x23=1} 0.003 0.024 0.143 1.076 34
## [227] {x23=1} => {x8=1} 0.004 0.176 0.022 1.163 39
## [228] {x8=1} => {x23=1} 0.004 0.026 0.152 1.163 39
## [229] {x23=1} => {x5=1} 0.004 0.195 0.022 1.162 43
## [230] {x5=1} => {x23=1} 0.004 0.026 0.167 1.162 43
## [231] {x23=1} => {x6=1} 0.004 0.176 0.022 1.019 39
## [232] {x6=1} => {x23=1} 0.004 0.023 0.173 1.019 39
## [233] {x23=1} => {x4=1} 0.005 0.213 0.022 1.029 47
## [234] {x4=1} => {x23=1} 0.005 0.023 0.207 1.029 47
## [235] {x23=1} => {x3=1} 0.007 0.339 0.022 1.294 75
## [236] {x3=1} => {x23=1} 0.007 0.029 0.262 1.294 75
## [237] {x23=1} => {x2=1} 0.007 0.303 0.022 0.921 67
## [238] {x2=1} => {x23=1} 0.007 0.020 0.329 0.921 67
## [239] {x23=1} => {x1=1} 0.007 0.321 0.022 0.965 71
## [240] {x1=1} => {x23=1} 0.007 0.021 0.333 0.965 71
## [241] {x22=1} => {x21=1} 0.001 0.022 0.028 0.696 6
## [242] {x21=1} => {x22=1} 0.001 0.019 0.031 0.696 6
## [243] {x22=1} => {x20=1} 0.001 0.032 0.028 1.006 9
## [244] {x20=1} => {x22=1} 0.001 0.028 0.032 1.006 9
## [245] {x22=1} => {x19=1} 0.001 0.051 0.028 1.550 14
## [246] {x19=1} => {x22=1} 0.001 0.043 0.033 1.550 14
## [247] {x22=1} => {x18=1} 0.001 0.047 0.028 1.016 13
## [248] {x18=1} => {x22=1} 0.001 0.028 0.046 1.016 13
## [249] {x22=1} => {x17=1} 0.001 0.029 0.028 0.620 8
## [250] {x17=1} => {x22=1} 0.001 0.017 0.047 0.620 8
## [251] {x22=1} => {x16=1} 0.001 0.047 0.028 0.876 13
## [252] {x16=1} => {x22=1} 0.001 0.024 0.054 0.876 13
## [253] {x22=1} => {x15=1} 0.002 0.069 0.028 1.134 19
## [254] {x15=1} => {x22=1} 0.002 0.031 0.060 1.134 19
## [255] {x22=1} => {x14=1} 0.002 0.069 0.028 1.018 19
## [256] {x14=1} => {x22=1} 0.002 0.028 0.067 1.018 19
## [257] {x22=1} => {x13=1} 0.002 0.069 0.028 0.953 19
## [258] {x13=1} => {x22=1} 0.002 0.026 0.072 0.953 19
## [259] {x22=1} => {x12=1} 0.004 0.134 0.028 1.270 37
## [260] {x12=1} => {x22=1} 0.004 0.035 0.105 1.270 37
## [261] {x22=1} => {x11=1} 0.003 0.119 0.028 1.068 33
## [262] {x11=1} => {x22=1} 0.003 0.030 0.112 1.068 33
## [263] {x22=1} => {x10=1} 0.003 0.105 0.028 0.932 29
## [264] {x10=1} => {x22=1} 0.003 0.026 0.112 0.932 29
## [265] {x22=1} => {x9=1} 0.004 0.130 0.028 0.985 36
## [266] {x9=1} => {x22=1} 0.004 0.027 0.132 0.985 36
## [267] {x22=1} => {x7=1} 0.004 0.144 0.028 1.010 40
## [268] {x7=1} => {x22=1} 0.004 0.028 0.143 1.010 40
## [269] {x22=1} => {x8=1} 0.004 0.130 0.028 0.856 36
## [270] {x8=1} => {x22=1} 0.004 0.024 0.152 0.856 36
## [271] {x22=1} => {x5=1} 0.003 0.119 0.028 0.711 33
## [272] {x5=1} => {x22=1} 0.003 0.020 0.167 0.711 33
## [273] {x22=1} => {x6=1} 0.005 0.173 0.028 1.001 48
## [274] {x6=1} => {x22=1} 0.005 0.028 0.173 1.001 48
## [275] {x22=1} => {x4=1} 0.006 0.213 0.028 1.031 59
## [276] {x4=1} => {x22=1} 0.006 0.029 0.207 1.031 59
## [277] {x22=1} => {x3=1} 0.008 0.274 0.028 1.046 76
## [278] {x3=1} => {x22=1} 0.008 0.029 0.262 1.046 76
## [279] {x22=1} => {x2=1} 0.009 0.307 0.028 0.932 85
## [280] {x2=1} => {x22=1} 0.009 0.026 0.329 0.932 85
## [281] {x22=1} => {x1=1} 0.009 0.336 0.028 1.008 93
## [282] {x1=1} => {x22=1} 0.009 0.028 0.333 1.008 93
## [283] {x21=1} => {x20=1} 0.001 0.026 0.031 0.796 8
## [284] {x20=1} => {x21=1} 0.001 0.025 0.032 0.796 8
## [285] {x21=1} => {x19=1} 0.002 0.051 0.031 1.578 16
## [286] {x19=1} => {x21=1} 0.002 0.049 0.033 1.578 16
## [287] {x21=1} => {x18=1} 0.002 0.061 0.031 1.322 19
## [288] {x18=1} => {x21=1} 0.002 0.041 0.046 1.322 19
## [289] {x21=1} => {x17=1} 0.001 0.042 0.031 0.897 13
## [290] {x17=1} => {x21=1} 0.001 0.028 0.047 0.897 13
## [291] {x21=1} => {x16=1} 0.002 0.061 0.031 1.140 19
## [292] {x16=1} => {x21=1} 0.002 0.035 0.054 1.140 19
## [293] {x21=1} => {x15=1} 0.002 0.055 0.031 0.904 17
## [294] {x15=1} => {x21=1} 0.002 0.028 0.060 0.904 17
## [295] {x21=1} => {x14=1} 0.002 0.074 0.031 1.097 23
## [296] {x14=1} => {x21=1} 0.002 0.034 0.067 1.097 23
## [297] {x21=1} => {x13=1} 0.003 0.090 0.031 1.250 28
## [298] {x13=1} => {x21=1} 0.003 0.039 0.072 1.250 28
## [299] {x21=1} => {x12=1} 0.003 0.106 0.031 1.009 33
## [300] {x12=1} => {x21=1} 0.003 0.031 0.105 1.009 33
## [301] {x21=1} => {x11=1} 0.003 0.106 0.031 0.951 33
## [302] {x11=1} => {x21=1} 0.003 0.030 0.112 0.951 33
## [303] {x21=1} => {x10=1} 0.004 0.116 0.031 1.031 36
## [304] {x10=1} => {x21=1} 0.004 0.032 0.112 1.031 36
## [305] {x21=1} => {x9=1} 0.004 0.135 0.031 1.024 42
## [306] {x9=1} => {x21=1} 0.004 0.032 0.132 1.024 42
## [307] {x21=1} => {x7=1} 0.004 0.129 0.031 0.899 40
## [308] {x7=1} => {x21=1} 0.004 0.028 0.143 0.899 40
## [309] {x21=1} => {x8=1} 0.004 0.132 0.031 0.868 41
## [310] {x8=1} => {x21=1} 0.004 0.027 0.152 0.868 41
## [311] {x21=1} => {x5=1} 0.005 0.148 0.031 0.883 46
## [312] {x5=1} => {x21=1} 0.005 0.027 0.167 0.883 46
## [313] {x21=1} => {x6=1} 0.005 0.170 0.031 0.985 53
## [314] {x6=1} => {x21=1} 0.005 0.031 0.173 0.985 53
## [315] {x21=1} => {x4=1} 0.008 0.244 0.031 1.183 76
## [316] {x4=1} => {x21=1} 0.008 0.037 0.207 1.183 76
## [317] {x21=1} => {x3=1} 0.008 0.248 0.031 0.944 77
## [318] {x3=1} => {x21=1} 0.008 0.029 0.262 0.944 77
## [319] {x21=1} => {x2=1} 0.011 0.350 0.031 1.065 109
## [320] {x2=1} => {x21=1} 0.011 0.033 0.329 1.065 109
## [321] {x21=1} => {x1=1} 0.011 0.344 0.031 1.033 107
## [322] {x1=1} => {x21=1} 0.011 0.032 0.333 1.033 107
## [323] {x20=1} => {x19=1} 0.000 0.015 0.032 0.475 5
## [324] {x19=1} => {x20=1} 0.000 0.015 0.033 0.475 5
## [325] {x20=1} => {x18=1} 0.001 0.031 0.032 0.670 10
## [326] {x18=1} => {x20=1} 0.001 0.022 0.046 0.670 10
## [327] {x20=1} => {x17=1} 0.002 0.056 0.032 1.196 18
## [328] {x17=1} => {x20=1} 0.002 0.039 0.047 1.196 18
## [329] {x20=1} => {x16=1} 0.001 0.034 0.032 0.635 11
## [330] {x16=1} => {x20=1} 0.001 0.021 0.054 0.635 11
## [331] {x20=1} => {x15=1} 0.002 0.046 0.032 0.768 15
## [332] {x15=1} => {x20=1} 0.002 0.025 0.060 0.768 15
## [333] {x20=1} => {x14=1} 0.002 0.071 0.032 1.056 23
## [334] {x14=1} => {x20=1} 0.002 0.034 0.067 1.056 23
## [335] {x20=1} => {x13=1} 0.003 0.080 0.032 1.118 26
## [336] {x13=1} => {x20=1} 0.003 0.036 0.072 1.118 26
## [337] {x20=1} => {x12=1} 0.004 0.133 0.032 1.265 43
## [338] {x12=1} => {x20=1} 0.004 0.041 0.105 1.265 43
## [339] {x20=1} => {x11=1} 0.004 0.130 0.032 1.165 42
## [340] {x11=1} => {x20=1} 0.004 0.038 0.112 1.165 42
## [341] {x20=1} => {x10=1} 0.004 0.127 0.032 1.130 41
## [342] {x10=1} => {x20=1} 0.004 0.037 0.112 1.130 41
## [343] {x20=1} => {x9=1} 0.004 0.133 0.032 1.009 43
## [344] {x9=1} => {x20=1} 0.004 0.033 0.132 1.009 43
## [345] {x20=1} => {x7=1} 0.004 0.124 0.032 0.866 40
## [346] {x7=1} => {x20=1} 0.004 0.028 0.143 0.866 40
## [347] {x20=1} => {x8=1} 0.005 0.142 0.032 0.938 46
## [348] {x8=1} => {x20=1} 0.005 0.030 0.152 0.938 46
## [349] {x20=1} => {x5=1} 0.005 0.146 0.032 0.869 47
## [350] {x5=1} => {x20=1} 0.005 0.028 0.168 0.869 47
## [351] {x20=1} => {x6=1} 0.005 0.152 0.032 0.876 49
## [352] {x6=1} => {x20=1} 0.005 0.028 0.173 0.876 49
## [353] {x20=1} => {x4=1} 0.007 0.214 0.032 1.034 69
## [354] {x4=1} => {x20=1} 0.007 0.033 0.207 1.034 69
## [355] {x20=1} => {x3=1} 0.009 0.269 0.032 1.027 87
## [356] {x3=1} => {x20=1} 0.009 0.033 0.262 1.027 87
## [357] {x20=1} => {x2=1} 0.010 0.294 0.032 0.894 95
## [358] {x2=1} => {x20=1} 0.010 0.029 0.329 0.894 95
## [359] {x20=1} => {x1=1} 0.011 0.328 0.032 0.986 106
## [360] {x1=1} => {x20=1} 0.011 0.032 0.333 0.986 106
## [361] {x19=1} => {x18=1} 0.002 0.055 0.033 1.195 18
## [362] {x18=1} => {x19=1} 0.002 0.039 0.046 1.195 18
## [363] {x19=1} => {x17=1} 0.001 0.021 0.033 0.461 7
## [364] {x17=1} => {x19=1} 0.001 0.015 0.047 0.461 7
## [365] {x19=1} => {x16=1} 0.002 0.058 0.033 1.087 19
## [366] {x16=1} => {x19=1} 0.002 0.035 0.054 1.087 19
## [367] {x19=1} => {x15=1} 0.001 0.043 0.033 0.710 14
## [368] {x15=1} => {x19=1} 0.001 0.023 0.061 0.710 14
## [369] {x19=1} => {x14=1} 0.003 0.089 0.033 1.320 29
## [370] {x14=1} => {x19=1} 0.003 0.043 0.067 1.320 29
## [371] {x19=1} => {x13=1} 0.002 0.055 0.033 0.767 18
## [372] {x13=1} => {x19=1} 0.002 0.025 0.072 0.767 18
## [373] {x19=1} => {x12=1} 0.004 0.107 0.033 1.021 35
## [374] {x12=1} => {x19=1} 0.004 0.033 0.105 1.021 35
## [375] {x19=1} => {x11=1} 0.003 0.101 0.033 0.907 33
## [376] {x11=1} => {x19=1} 0.003 0.030 0.112 0.907 33
## [377] {x19=1} => {x10=1} 0.004 0.129 0.033 1.147 42
## [378] {x10=1} => {x19=1} 0.004 0.037 0.112 1.147 42
## [379] {x19=1} => {x9=1} 0.005 0.166 0.033 1.256 54
## [380] {x9=1} => {x19=1} 0.005 0.041 0.132 1.256 54
## [381] {x19=1} => {x7=1} 0.006 0.172 0.033 1.201 56
## [382] {x7=1} => {x19=1} 0.006 0.039 0.143 1.201 56
## [383] {x19=1} => {x8=1} 0.005 0.144 0.033 0.950 47
## [384] {x8=1} => {x19=1} 0.005 0.031 0.152 0.950 47
## [385] {x19=1} => {x5=1} 0.005 0.160 0.033 0.952 52
## [386] {x5=1} => {x19=1} 0.005 0.031 0.168 0.952 52
## [387] {x19=1} => {x6=1} 0.006 0.190 0.033 1.099 62
## [388] {x6=1} => {x19=1} 0.006 0.036 0.173 1.099 62
## [389] {x19=1} => {x4=1} 0.006 0.178 0.033 0.861 58
## [390] {x4=1} => {x19=1} 0.006 0.028 0.207 0.861 58
## [391] {x19=1} => {x3=1} 0.008 0.239 0.033 0.913 78
## [392] {x3=1} => {x19=1} 0.008 0.030 0.262 0.913 78
## [393] {x19=1} => {x2=1} 0.013 0.383 0.033 1.165 125
## [394] {x2=1} => {x19=1} 0.013 0.038 0.329 1.165 125
## [395] {x19=1} => {x1=1} 0.011 0.331 0.033 0.995 108
## [396] {x1=1} => {x19=1} 0.011 0.032 0.333 0.995 108
## [397] {x18=1} => {x17=1} 0.002 0.050 0.046 1.068 23
## [398] {x17=1} => {x18=1} 0.002 0.049 0.047 1.068 23
## [399] {x18=1} => {x16=1} 0.002 0.054 0.046 1.010 25
## [400] {x16=1} => {x18=1} 0.002 0.047 0.054 1.010 25
## [401] {x18=1} => {x15=1} 0.002 0.050 0.046 0.823 23
## [402] {x15=1} => {x18=1} 0.002 0.038 0.061 0.823 23
## [403] {x18=1} => {x14=1} 0.003 0.067 0.046 0.996 31
## [404] {x14=1} => {x18=1} 0.003 0.046 0.067 0.996 31
## [405] {x18=1} => {x13=1} 0.003 0.071 0.046 0.992 33
## [406] {x13=1} => {x18=1} 0.003 0.046 0.072 0.992 33
## [407] {x18=1} => {x12=1} 0.004 0.084 0.046 0.802 39
## [408] {x12=1} => {x18=1} 0.004 0.037 0.105 0.802 39
## [409] {x18=1} => {x11=1} 0.004 0.091 0.046 0.815 42
## [410] {x11=1} => {x18=1} 0.004 0.038 0.112 0.815 42
## [411] {x18=1} => {x10=1} 0.005 0.104 0.046 0.925 48
## [412] {x10=1} => {x18=1} 0.005 0.043 0.112 0.925 48
## [413] {x18=1} => {x9=1} 0.005 0.106 0.046 0.804 49
## [414] {x9=1} => {x18=1} 0.005 0.037 0.132 0.804 49
## [415] {x18=1} => {x7=1} 0.006 0.139 0.046 0.969 64
## [416] {x7=1} => {x18=1} 0.006 0.045 0.143 0.969 64
## [417] {x18=1} => {x8=1} 0.007 0.162 0.046 1.069 75
## [418] {x8=1} => {x18=1} 0.007 0.049 0.152 1.069 75
## [419] {x18=1} => {x5=1} 0.007 0.152 0.046 0.905 70
## [420] {x5=1} => {x18=1} 0.007 0.042 0.168 0.905 70
## [421] {x18=1} => {x6=1} 0.009 0.184 0.046 1.063 85
## [422] {x6=1} => {x18=1} 0.009 0.049 0.173 1.063 85
## [423] {x18=1} => {x4=1} 0.010 0.208 0.046 1.006 96
## [424] {x4=1} => {x18=1} 0.010 0.046 0.207 1.006 96
## [425] {x18=1} => {x3=1} 0.012 0.258 0.046 0.982 119
## [426] {x3=1} => {x18=1} 0.012 0.045 0.262 0.982 119
## [427] {x18=1} => {x2=1} 0.014 0.301 0.046 0.914 139
## [428] {x2=1} => {x18=1} 0.014 0.042 0.329 0.914 139
## [429] {x18=1} => {x1=1} 0.017 0.366 0.046 1.099 169
## [430] {x1=1} => {x18=1} 0.017 0.051 0.333 1.099 169
## [431] {x17=1} => {x16=1} 0.003 0.060 0.047 1.121 28
## [432] {x16=1} => {x17=1} 0.003 0.052 0.054 1.121 28
## [433] {x17=1} => {x15=1} 0.003 0.073 0.047 1.206 34
## [434] {x15=1} => {x17=1} 0.003 0.056 0.060 1.206 34
## [435] {x17=1} => {x14=1} 0.003 0.064 0.047 0.955 30
## [436] {x14=1} => {x17=1} 0.003 0.045 0.067 0.955 30
## [437] {x17=1} => {x13=1} 0.003 0.067 0.047 0.924 31
## [438] {x13=1} => {x17=1} 0.003 0.043 0.072 0.924 31
## [439] {x17=1} => {x12=1} 0.005 0.105 0.047 1.000 49
## [440] {x12=1} => {x17=1} 0.005 0.047 0.105 1.000 49
## [441] {x17=1} => {x11=1} 0.004 0.088 0.047 0.788 41
## [442] {x11=1} => {x17=1} 0.004 0.037 0.112 0.788 41
## [443] {x17=1} => {x10=1} 0.005 0.109 0.047 0.975 51
## [444] {x10=1} => {x17=1} 0.005 0.045 0.112 0.975 51
## [445] {x17=1} => {x9=1} 0.006 0.127 0.047 0.960 59
## [446] {x9=1} => {x17=1} 0.006 0.045 0.132 0.960 59
## [447] {x17=1} => {x7=1} 0.006 0.120 0.047 0.840 56
## [448] {x7=1} => {x17=1} 0.006 0.039 0.143 0.840 56
## [449] {x17=1} => {x8=1} 0.007 0.159 0.047 1.046 74
## [450] {x8=1} => {x17=1} 0.007 0.049 0.152 1.046 74
## [451] {x17=1} => {x5=1} 0.006 0.133 0.047 0.794 62
## [452] {x5=1} => {x17=1} 0.006 0.037 0.168 0.794 62
## [453] {x17=1} => {x6=1} 0.008 0.170 0.047 0.979 79
## [454] {x6=1} => {x17=1} 0.008 0.046 0.173 0.979 79
## [455] {x17=1} => {x4=1} 0.009 0.191 0.047 0.924 89
## [456] {x4=1} => {x17=1} 0.009 0.043 0.207 0.924 89
## [457] {x17=1} => {x3=1} 0.012 0.264 0.047 1.007 123
## [458] {x3=1} => {x17=1} 0.012 0.047 0.262 1.007 123
## [459] {x17=1} => {x2=1} 0.016 0.339 0.047 1.030 158
## [460] {x2=1} => {x17=1} 0.016 0.048 0.329 1.030 158
## [461] {x17=1} => {x1=1} 0.015 0.328 0.047 0.986 153
## [462] {x1=1} => {x17=1} 0.015 0.046 0.333 0.986 153
## [463] {x16=1} => {x15=1} 0.004 0.071 0.054 1.172 38
## [464] {x15=1} => {x16=1} 0.004 0.063 0.060 1.172 38
## [465] {x16=1} => {x14=1} 0.004 0.073 0.054 1.080 39
## [466] {x14=1} => {x16=1} 0.004 0.058 0.067 1.080 39
## [467] {x16=1} => {x13=1} 0.004 0.067 0.054 0.933 36
## [468] {x13=1} => {x16=1} 0.004 0.050 0.072 0.933 36
## [469] {x16=1} => {x12=1} 0.005 0.103 0.054 0.975 55
## [470] {x12=1} => {x16=1} 0.005 0.052 0.105 0.975 55
## [471] {x16=1} => {x11=1} 0.006 0.106 0.054 0.953 57
## [472] {x11=1} => {x16=1} 0.006 0.051 0.112 0.953 57
## [473] {x16=1} => {x10=1} 0.005 0.093 0.054 0.831 50
## [474] {x10=1} => {x16=1} 0.005 0.045 0.112 0.831 50
## [475] {x16=1} => {x9=1} 0.007 0.136 0.054 1.033 73
## [476] {x9=1} => {x16=1} 0.007 0.055 0.132 1.033 73
## [477] {x16=1} => {x7=1} 0.006 0.121 0.054 0.848 65
## [478] {x7=1} => {x16=1} 0.006 0.045 0.143 0.848 65
## [479] {x16=1} => {x8=1} 0.007 0.134 0.054 0.885 72
## [480] {x8=1} => {x16=1} 0.007 0.047 0.152 0.885 72
## [481] {x16=1} => {x5=1} 0.009 0.164 0.054 0.980 88
## [482] {x5=1} => {x16=1} 0.009 0.053 0.168 0.980 88
## [483] {x16=1} => {x6=1} 0.009 0.175 0.054 1.013 94
## [484] {x6=1} => {x16=1} 0.009 0.054 0.173 1.013 94
## [485] {x16=1} => {x4=1} 0.010 0.188 0.054 0.912 101
## [486] {x4=1} => {x16=1} 0.010 0.049 0.207 0.912 101
## [487] {x16=1} => {x3=1} 0.014 0.267 0.054 1.018 143
## [488] {x3=1} => {x16=1} 0.014 0.055 0.262 1.018 143
## [489] {x16=1} => {x2=1} 0.018 0.336 0.054 1.020 180
## [490] {x2=1} => {x16=1} 0.018 0.055 0.329 1.020 180
## [491] {x16=1} => {x1=1} 0.017 0.315 0.054 0.947 169
## [492] {x1=1} => {x16=1} 0.017 0.051 0.333 0.947 169
## [493] {x15=1} => {x14=1} 0.004 0.069 0.060 1.030 42
## [494] {x14=1} => {x15=1} 0.004 0.062 0.067 1.030 42
## [495] {x15=1} => {x13=1} 0.004 0.069 0.060 0.964 42
## [496] {x13=1} => {x15=1} 0.004 0.058 0.072 0.964 42
## [497] {x15=1} => {x12=1} 0.007 0.119 0.060 1.131 72
## [498] {x12=1} => {x15=1} 0.007 0.068 0.105 1.131 72
## [499] {x15=1} => {x11=1} 0.007 0.114 0.060 1.022 69
## [500] {x11=1} => {x15=1} 0.007 0.062 0.112 1.022 69
## [501] {x15=1} => {x10=1} 0.007 0.121 0.060 1.074 73
## [502] {x10=1} => {x15=1} 0.007 0.065 0.112 1.074 73
## [503] {x15=1} => {x9=1} 0.008 0.126 0.060 0.952 76
## [504] {x9=1} => {x15=1} 0.008 0.058 0.132 0.952 76
## [505] {x15=1} => {x7=1} 0.009 0.144 0.060 1.006 87
## [506] {x7=1} => {x15=1} 0.009 0.061 0.143 1.006 87
## [507] {x15=1} => {x8=1} 0.009 0.149 0.060 0.980 90
## [508] {x8=1} => {x15=1} 0.009 0.059 0.152 0.980 90
## [509] {x15=1} => {x5=1} 0.011 0.185 0.061 1.105 112
## [510] {x5=1} => {x15=1} 0.011 0.067 0.168 1.105 112
## [511] {x15=1} => {x6=1} 0.010 0.167 0.060 0.964 101
## [512] {x6=1} => {x15=1} 0.010 0.058 0.173 0.964 101
## [513] {x15=1} => {x4=1} 0.013 0.208 0.060 1.008 126
## [514] {x4=1} => {x15=1} 0.013 0.061 0.207 1.008 126
## [515] {x15=1} => {x3=1} 0.017 0.278 0.060 1.059 168
## [516] {x3=1} => {x15=1} 0.017 0.064 0.262 1.059 168
## [517] {x15=1} => {x2=1} 0.020 0.324 0.060 0.984 196
## [518] {x2=1} => {x15=1} 0.020 0.060 0.329 0.984 196
## [519] {x15=1} => {x1=1} 0.020 0.331 0.060 0.993 200
## [520] {x1=1} => {x15=1} 0.020 0.060 0.333 0.993 200
## [521] {x14=1} => {x13=1} 0.004 0.058 0.067 0.804 39
## [522] {x13=1} => {x14=1} 0.004 0.054 0.072 0.804 39
## [523] {x14=1} => {x12=1} 0.007 0.108 0.067 1.030 73
## [524] {x12=1} => {x14=1} 0.007 0.069 0.105 1.030 73
## [525] {x14=1} => {x11=1} 0.007 0.107 0.067 0.957 72
## [526] {x11=1} => {x14=1} 0.007 0.065 0.112 0.957 72
## [527] {x14=1} => {x10=1} 0.007 0.105 0.067 0.938 71
## [528] {x10=1} => {x14=1} 0.007 0.063 0.112 0.938 71
## [529] {x14=1} => {x9=1} 0.009 0.128 0.067 0.967 86
## [530] {x9=1} => {x14=1} 0.009 0.065 0.132 0.967 86
## [531] {x14=1} => {x7=1} 0.009 0.131 0.067 0.913 88
## [532] {x7=1} => {x14=1} 0.009 0.062 0.143 0.913 88
## [533] {x14=1} => {x8=1} 0.010 0.154 0.067 1.016 104
## [534] {x8=1} => {x14=1} 0.010 0.069 0.152 1.016 104
## [535] {x14=1} => {x5=1} 0.012 0.181 0.067 1.081 122
## [536] {x5=1} => {x14=1} 0.012 0.073 0.168 1.081 122
## [537] {x14=1} => {x6=1} 0.012 0.184 0.067 1.063 124
## [538] {x6=1} => {x14=1} 0.012 0.072 0.173 1.063 124
## [539] {x14=1} => {x4=1} 0.013 0.194 0.067 0.941 131
## [540] {x4=1} => {x14=1} 0.013 0.063 0.207 0.941 131
## [541] {x14=1} => {x3=1} 0.018 0.266 0.067 1.013 179
## [542] {x3=1} => {x14=1} 0.018 0.068 0.262 1.013 179
## [543] {x14=1} => {x2=1} 0.022 0.325 0.067 0.987 219
## [544] {x2=1} => {x14=1} 0.022 0.067 0.329 0.987 219
## [545] {x14=1} => {x1=1} 0.023 0.338 0.067 1.016 228
## [546] {x1=1} => {x14=1} 0.023 0.068 0.333 1.016 228
## [547] {x13=1} => {x12=1} 0.007 0.100 0.072 0.951 72
## [548] {x12=1} => {x13=1} 0.007 0.068 0.105 0.951 72
## [549] {x13=1} => {x11=1} 0.009 0.119 0.072 1.070 86
## [550] {x11=1} => {x13=1} 0.009 0.077 0.112 1.070 86
## [551] {x13=1} => {x10=1} 0.008 0.108 0.072 0.965 78
## [552] {x10=1} => {x13=1} 0.008 0.069 0.112 0.965 78
## [553] {x13=1} => {x9=1} 0.010 0.136 0.072 1.032 98
## [554] {x9=1} => {x13=1} 0.010 0.074 0.132 1.032 98
## [555] {x13=1} => {x7=1} 0.011 0.157 0.072 1.098 113
## [556] {x7=1} => {x13=1} 0.011 0.079 0.143 1.098 113
## [557] {x13=1} => {x8=1} 0.011 0.153 0.072 1.006 110
## [558] {x8=1} => {x13=1} 0.011 0.072 0.152 1.006 110
## [559] {x13=1} => {x5=1} 0.012 0.165 0.072 0.987 119
## [560] {x5=1} => {x13=1} 0.012 0.071 0.168 0.987 119
## [561] {x13=1} => {x6=1} 0.013 0.176 0.072 1.019 127
## [562] {x6=1} => {x13=1} 0.013 0.073 0.173 1.019 127
## [563] {x13=1} => {x4=1} 0.016 0.218 0.072 1.055 157
## [564] {x4=1} => {x13=1} 0.016 0.076 0.207 1.055 157
## [565] {x13=1} => {x3=1} 0.019 0.269 0.072 1.028 194
## [566] {x3=1} => {x13=1} 0.019 0.074 0.262 1.028 194
## [567] {x13=1} => {x2=1} 0.024 0.326 0.072 0.992 235
## [568] {x2=1} => {x13=1} 0.024 0.071 0.329 0.992 235
## [569] {x13=1} => {x1=1} 0.023 0.322 0.072 0.968 232
## [570] {x1=1} => {x13=1} 0.023 0.070 0.333 0.968 232
## [571] {x12=1} => {x11=1} 0.012 0.115 0.105 1.031 121
## [572] {x11=1} => {x12=1} 0.012 0.108 0.112 1.031 121
## [573] {x12=1} => {x10=1} 0.010 0.099 0.105 0.880 104
## [574] {x10=1} => {x12=1} 0.010 0.093 0.112 0.880 104
## [575] {x12=1} => {x9=1} 0.012 0.112 0.105 0.850 118
## [576] {x9=1} => {x12=1} 0.012 0.089 0.132 0.850 118
## [577] {x12=1} => {x7=1} 0.016 0.153 0.105 1.070 161
## [578] {x7=1} => {x12=1} 0.016 0.113 0.143 1.070 161
## [579] {x12=1} => {x8=1} 0.015 0.143 0.105 0.939 150
## [580] {x8=1} => {x12=1} 0.015 0.099 0.152 0.939 150
## [581] {x12=1} => {x5=1} 0.016 0.150 0.105 0.897 158
## [582] {x5=1} => {x12=1} 0.016 0.094 0.168 0.897 158
## [583] {x12=1} => {x6=1} 0.018 0.166 0.105 0.961 175
## [584] {x6=1} => {x12=1} 0.018 0.101 0.173 0.961 175
## [585] {x12=1} => {x4=1} 0.023 0.221 0.105 1.072 233
## [586] {x4=1} => {x12=1} 0.023 0.113 0.207 1.072 233
## [587] {x12=1} => {x3=1} 0.028 0.261 0.105 0.997 275
## [588] {x3=1} => {x12=1} 0.028 0.105 0.262 0.997 275
## [589] {x12=1} => {x2=1} 0.034 0.322 0.105 0.979 339
## [590] {x2=1} => {x12=1} 0.034 0.103 0.329 0.979 339
## [591] {x12=1} => {x1=1} 0.035 0.332 0.105 0.996 349
## [592] {x1=1} => {x12=1} 0.035 0.105 0.333 0.996 349
## [593] {x11=1} => {x10=1} 0.013 0.112 0.112 0.997 125
## [594] {x10=1} => {x11=1} 0.013 0.111 0.112 0.997 125
## [595] {x11=1} => {x9=1} 0.016 0.143 0.112 1.087 160
## [596] {x9=1} => {x11=1} 0.016 0.121 0.132 1.087 160
## [597] {x11=1} => {x7=1} 0.017 0.149 0.112 1.040 166
## [598] {x7=1} => {x11=1} 0.017 0.116 0.143 1.040 166
## [599] {x11=1} => {x8=1} 0.016 0.144 0.112 0.950 161
## [600] {x8=1} => {x11=1} 0.016 0.106 0.152 0.950 161
## [601] {x11=1} => {x5=1} 0.019 0.168 0.112 1.006 188
## [602] {x5=1} => {x11=1} 0.019 0.112 0.168 1.006 188
## [603] {x11=1} => {x6=1} 0.020 0.177 0.112 1.020 197
## [604] {x6=1} => {x11=1} 0.020 0.114 0.173 1.020 197
## [605] {x11=1} => {x4=1} 0.022 0.195 0.112 0.946 218
## [606] {x4=1} => {x11=1} 0.022 0.106 0.207 0.946 218
## [607] {x11=1} => {x3=1} 0.028 0.255 0.112 0.974 285
## [608] {x3=1} => {x11=1} 0.028 0.109 0.262 0.974 285
## [609] {x11=1} => {x2=1} 0.035 0.315 0.112 0.956 351
## [610] {x2=1} => {x11=1} 0.035 0.107 0.329 0.956 351
## [611] {x11=1} => {x1=1} 0.035 0.311 0.112 0.934 347
## [612] {x1=1} => {x11=1} 0.035 0.104 0.333 0.934 347
## [613] {x10=1} => {x9=1} 0.014 0.122 0.112 0.925 137
## [614] {x9=1} => {x10=1} 0.014 0.104 0.132 0.925 137
## [615] {x10=1} => {x7=1} 0.019 0.166 0.112 1.158 186
## [616] {x7=1} => {x10=1} 0.019 0.130 0.143 1.158 186
## [617] {x10=1} => {x8=1} 0.017 0.154 0.112 1.015 173
## [618] {x8=1} => {x10=1} 0.017 0.114 0.152 1.015 173
## [619] {x10=1} => {x5=1} 0.018 0.157 0.112 0.936 176
## [620] {x5=1} => {x10=1} 0.018 0.105 0.168 0.936 176
## [621] {x10=1} => {x6=1} 0.017 0.151 0.112 0.875 170
## [622] {x6=1} => {x10=1} 0.017 0.098 0.173 0.875 170
## [623] {x10=1} => {x4=1} 0.022 0.200 0.112 0.970 225
## [624] {x4=1} => {x10=1} 0.022 0.109 0.207 0.970 225
## [625] {x10=1} => {x3=1} 0.030 0.264 0.112 1.005 296
## [626] {x3=1} => {x10=1} 0.030 0.113 0.262 1.005 296
## [627] {x10=1} => {x2=1} 0.036 0.318 0.112 0.966 357
## [628] {x2=1} => {x10=1} 0.036 0.108 0.329 0.966 357
## [629] {x10=1} => {x1=1} 0.040 0.357 0.112 1.072 401
## [630] {x1=1} => {x10=1} 0.040 0.120 0.333 1.072 401
## [631] {x9=1} => {x7=1} 0.018 0.138 0.132 0.965 182
## [632] {x7=1} => {x9=1} 0.018 0.127 0.143 0.965 182
## [633] {x9=1} => {x8=1} 0.018 0.139 0.132 0.919 184
## [634] {x8=1} => {x9=1} 0.018 0.121 0.152 0.919 184
## [635] {x9=1} => {x5=1} 0.020 0.152 0.132 0.910 201
## [636] {x5=1} => {x9=1} 0.020 0.120 0.168 0.910 201
## [637] {x9=1} => {x6=1} 0.024 0.180 0.132 1.042 238
## [638] {x6=1} => {x9=1} 0.024 0.137 0.173 1.042 238
## [639] {x9=1} => {x4=1} 0.028 0.210 0.132 1.016 277
## [640] {x4=1} => {x9=1} 0.028 0.134 0.207 1.016 277
## [641] {x9=1} => {x3=1} 0.031 0.238 0.132 0.908 314
## [642] {x3=1} => {x9=1} 0.031 0.120 0.262 0.908 314
## [643] {x9=1} => {x2=1} 0.042 0.317 0.132 0.963 418
## [644] {x2=1} => {x9=1} 0.042 0.127 0.329 0.963 418
## [645] {x9=1} => {x1=1} 0.044 0.333 0.132 0.999 439
## [646] {x1=1} => {x9=1} 0.044 0.132 0.333 0.999 439
## [647] {x7=1} => {x8=1} 0.021 0.145 0.143 0.954 207
## [648] {x8=1} => {x7=1} 0.021 0.136 0.152 0.954 207
## [649] {x7=1} => {x5=1} 0.025 0.176 0.143 1.052 252
## [650] {x5=1} => {x7=1} 0.025 0.150 0.167 1.052 252
## [651] {x7=1} => {x6=1} 0.022 0.157 0.143 0.909 225
## [652] {x6=1} => {x7=1} 0.022 0.130 0.173 0.909 225
## [653] {x7=1} => {x4=1} 0.029 0.201 0.143 0.975 288
## [654] {x4=1} => {x7=1} 0.029 0.139 0.207 0.975 288
## [655] {x7=1} => {x3=1} 0.042 0.294 0.143 1.120 420
## [656] {x3=1} => {x7=1} 0.042 0.160 0.262 1.120 420
## [657] {x7=1} => {x2=1} 0.047 0.331 0.143 1.005 473
## [658] {x2=1} => {x7=1} 0.047 0.144 0.329 1.005 473
## [659] {x7=1} => {x1=1} 0.050 0.348 0.143 1.046 498
## [660] {x1=1} => {x7=1} 0.050 0.150 0.333 1.046 498
## [661] {x8=1} => {x5=1} 0.024 0.160 0.152 0.956 243
## [662] {x5=1} => {x8=1} 0.024 0.145 0.168 0.956 243
## [663] {x8=1} => {x6=1} 0.024 0.157 0.152 0.906 238
## [664] {x6=1} => {x8=1} 0.024 0.137 0.173 0.906 238
## [665] {x8=1} => {x4=1} 0.031 0.206 0.152 0.995 312
## [666] {x4=1} => {x8=1} 0.031 0.151 0.207 0.995 312
## [667] {x8=1} => {x3=1} 0.039 0.260 0.152 0.990 394
## [668] {x3=1} => {x8=1} 0.039 0.150 0.262 0.990 394
## [669] {x8=1} => {x2=1} 0.050 0.332 0.152 1.009 504
## [670] {x2=1} => {x8=1} 0.050 0.153 0.329 1.009 504
## [671] {x8=1} => {x1=1} 0.050 0.329 0.152 0.987 499
## [672] {x1=1} => {x8=1} 0.050 0.150 0.333 0.987 499
## [673] {x5=1} => {x6=1} 0.028 0.166 0.167 0.959 278
## [674] {x6=1} => {x5=1} 0.028 0.161 0.173 0.959 278
## [675] {x5=1} => {x4=1} 0.034 0.205 0.167 0.991 343
## [676] {x4=1} => {x5=1} 0.034 0.166 0.207 0.991 343
## [677] {x5=1} => {x3=1} 0.045 0.268 0.168 1.022 449
## [678] {x3=1} => {x5=1} 0.045 0.171 0.262 1.022 449
## [679] {x5=1} => {x2=1} 0.056 0.331 0.168 1.007 555
## [680] {x2=1} => {x5=1} 0.056 0.169 0.329 1.007 555
## [681] {x5=1} => {x1=1} 0.056 0.337 0.167 1.011 564
## [682] {x1=1} => {x5=1} 0.056 0.169 0.333 1.011 564
## [683] {x6=1} => {x4=1} 0.036 0.207 0.173 1.001 358
## [684] {x4=1} => {x6=1} 0.036 0.173 0.207 1.001 358
## [685] {x6=1} => {x3=1} 0.044 0.257 0.173 0.980 445
## [686] {x3=1} => {x6=1} 0.044 0.170 0.262 0.980 445
## [687] {x6=1} => {x2=1} 0.055 0.319 0.173 0.969 552
## [688] {x2=1} => {x6=1} 0.055 0.168 0.329 0.969 552
## [689] {x6=1} => {x1=1} 0.058 0.333 0.173 1.001 577
## [690] {x1=1} => {x6=1} 0.058 0.173 0.333 1.001 577
## [691] {x4=1} => {x3=1} 0.057 0.274 0.207 1.047 567
## [692] {x3=1} => {x4=1} 0.057 0.216 0.262 1.047 567
## [693] {x4=1} => {x2=1} 0.067 0.324 0.207 0.985 670
## [694] {x2=1} => {x4=1} 0.067 0.204 0.329 0.985 670
## [695] {x4=1} => {x1=1} 0.070 0.338 0.207 1.016 699
## [696] {x1=1} => {x4=1} 0.070 0.210 0.333 1.016 699
## [697] {x3=1} => {x2=1} 0.083 0.315 0.262 0.958 827
## [698] {x2=1} => {x3=1} 0.083 0.251 0.329 0.958 827
## [699] {x3=1} => {x1=1} 0.087 0.332 0.262 0.998 871
## [700] {x1=1} => {x3=1} 0.087 0.262 0.333 0.998 871
## [701] {x2=1} => {x1=1} 0.111 0.338 0.329 1.016 1113
## [702] {x1=1} => {x2=1} 0.111 0.334 0.333 1.016 1113inspect(sort(rules2,by="lift") [1:20]) # summarise twenty rules with highest lift## lhs rhs support confidence coverage lift count
## [1] {x26=1} => {x20=1} 0.001 0.070 0.016 2.169 11
## [2] {x20=1} => {x26=1} 0.001 0.034 0.032 2.169 11
## [3] {x21=1} => {x19=1} 0.002 0.051 0.031 1.578 16
## [4] {x19=1} => {x21=1} 0.002 0.049 0.033 1.578 16
## [5] {x25=1} => {x15=1} 0.002 0.094 0.019 1.558 18
## [6] {x15=1} => {x25=1} 0.002 0.030 0.060 1.558 18
## [7] {x25=1} => {x24=1} 0.001 0.031 0.019 1.555 6
## [8] {x24=1} => {x25=1} 0.001 0.030 0.020 1.555 6
## [9] {x22=1} => {x19=1} 0.001 0.051 0.028 1.550 14
## [10] {x19=1} => {x22=1} 0.001 0.043 0.033 1.550 14
## [11] {x23=1} => {x20=1} 0.001 0.045 0.022 1.401 10
## [12] {x20=1} => {x23=1} 0.001 0.031 0.032 1.401 10
## [13] {x26=1} => {x6=1} 0.004 0.236 0.016 1.361 37
## [14] {x6=1} => {x26=1} 0.004 0.021 0.173 1.361 37
## [15] {x27=1} => {x26=1} 0.000 0.021 0.014 1.327 3
## [16] {x26=1} => {x27=1} 0.000 0.019 0.016 1.327 3
## [17] {x21=1} => {x18=1} 0.002 0.061 0.031 1.322 19
## [18] {x18=1} => {x21=1} 0.002 0.041 0.046 1.322 19
## [19] {x19=1} => {x14=1} 0.003 0.089 0.033 1.320 29
## [20] {x14=1} => {x19=1} 0.003 0.043 0.067 1.320 29write(rules2,file="P://rules.csv",sep=",") #write all rules as a csv file
##ARM is based on the concept of support, confidence, and lift. For a rule of A→B :
##support shows proportion of data used to assess each rule. For pairs, it shows number of subjects who have both conditions.
##Confidence describes how likely disease B to occur given that disease A is occurred
## If the lift of A→B is four, condition B is four times more likely to occur with condition A than if pairs of conditions were independent
#appearance shows which rules to be shown.
#The main algorithm to extract triads of conditions is A-Priori algorithm.
#The idea behind the A-Priori algorithm is to reduce the number of combinations to be examined, by assuming that if condition A is infrequent,
#then joint frequency of condition A with other conditions must be equally or more infrequent.
#The main parameters to reduce the number of combinations to be examined are support and confidence.
#In this work, to extract all possible combinations, the minimum support and minimum confidence were set at 0.000001 and 0.000001.
# the argument maxlen controls the length of rules. For example maxlen of two only calculates the lift for pairs
# The appearance argument restricts the rules. We applied a restriction so as to calculate the lifts when conditions are present.#Due to sensitivity of national mortality data, we cannot share the individual records used to prepare this manuscript.We uploaded csv files containing top piars and weight to replicate our network results. The codes are given below. Moreover, we uploaded the joint frequency of pairs and triads of conditions. These information is enough to replicate results of the Bayesian Poisson-Gamma model.
library(igraph)
library(ipfr)
library(xlsx)
library(readxl)
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## ✖ tidyr::unpack() masks Matrix::unpack()library(dplyr)
library(haven)
library(psych)##
## Attaching package: 'psych'
##
## The following objects are masked from 'package:ggplot2':
##
## %+%, alphalibrary(ggplot2)
library(devtools)## Loading required package: usethis### Network analysis using normalised joint frequencies as weight
# A csv file containing the node list (values 1 to 27)
NL=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/node.csv")
# A csv file of top pairs (pairs their normalised joint frequency>90th percentile of the distribution)
ELFN=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/FN_edge.csv")
gfn=graph_from_data_frame(d=ELFN,vertices=NL,directed=FALSE)
gfn=set_edge_attr(gfn, "weight", value= ELFN$Weight)
is_weighted(gfn)## [1] TRUEdegree(gfn) #degree of each condition## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 1 0 1 6 1 4 2 4 1 3 2 3 1 4 3 4 4 4 3 2 1 2 2 3 1 4
## 27
## 4strength(gfn,weights=ELFN$Weight) #weighted degree## 1 2 3 4 5 6 7 8 9 10 11
## 0.0656 0.0000 0.0730 0.3885 0.0565 0.2273 0.1120 0.2317 0.0557 0.2115 0.1851
## 12 13 14 15 16 17 18 19 20 21 22
## 0.2045 0.0549 0.2744 0.3471 0.2617 0.2546 0.3118 0.2400 0.1966 0.0573 0.1188
## 23 24 25 26 27
## 0.1719 0.2071 0.0874 0.3967 0.2785closeness(gfn,normalized=TRUE) #when true, it calculates the inverse average distance to all reachable vertices## 1 2 3 4 5 6 7 8
## 3.804828 NaN 3.704692 5.003803 3.935644 5.288543 5.015850 4.438920
## 9 10 11 12 13 14 15 16
## 3.587418 4.506778 4.463011 4.203800 3.359763 4.106237 3.643677 5.429943
## 17 18 19 20 21 22 23 24
## 5.234725 4.237719 2.784740 2.665984 2.414829 3.513506 3.905823 4.888732
## 25 26 27
## 3.054032 5.490162 4.082699closeness(gfn,normalized=FALSE) #when false, it calculates the inverse of sum of distance to all reachable vertices## 1 2 3 4 5 6 7
## 0.15219310 NaN 0.14818766 0.20015212 0.15742577 0.21154172 0.20063400
## 8 9 10 11 12 13 14
## 0.17755682 0.14349673 0.18027113 0.17852042 0.16815201 0.13439054 0.16424946
## 15 16 17 18 19 20 21
## 0.14574710 0.21719772 0.20938900 0.16950876 0.11138959 0.10663937 0.09659316
## 22 23 24 25 26 27
## 0.14054024 0.15623291 0.19554930 0.12216128 0.21960647 0.16330797betweenness(gfn,normalized=TRUE)## 1 2 3 4 5 6 7
## 0.00000000 0.00000000 0.00000000 0.28000000 0.00000000 0.26769231 0.15692308
## 8 9 10 11 12 13 14
## 0.15384615 0.00000000 0.06769231 0.00000000 0.06769231 0.00000000 0.08000000
## 15 16 17 18 19 20 21
## 0.20307692 0.26153846 0.36000000 0.00000000 0.07384615 0.00000000 0.00000000
## 22 23 24 25 26 27
## 0.00000000 0.00000000 0.16923077 0.00000000 0.40615385 0.07384615#Community detection
cfn=cluster_leiden(gfn,objective_function = "modularity",n_iterations = 3,resolution_parameter = 1)
plot(cfn,gfn,vertex.size=5*degree(gfn),layout=layout_with_fr,vertex.size=25,edge.arrow.size=5)
plot(cfn,gfn,vertex.size=5*degree(gfn),layout=layout_with_fr,edge.width=strength(gfn,weights=ELFN$Weight)*20) #tickness by weighted degree
node_cols=c("red","yellow","white","green","orange")[membership(cfn)] #color of nodes
poly_cols=c("white","white","white","white","white") # color of polygens
plot(cfn,gfn,col=node_cols,mark.border="black",mark.col=poly_cols)
##Network analysis using OR as weight
NL=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/node.csv")
ELOR=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/OR_edge.csv")
gor=graph_from_data_frame(d=ELOR,vertices=NL,directed=FALSE)
gor=set_edge_attr(gor, "weight", value= ELOR$Weight)
is_weighted(gor)## [1] TRUEcor=cluster_leiden(gor,objective_function = "modularity",n_iterations = 3,resolution_parameter = 1)
plot(cor,gor,vertex.size=5*degree(gor),layout=layout_with_fr,vertex.size=25,edge.arrow.size=5)
node_cols=c("yellow","yellow","red", "yellow", "white","yellow","green","yellow")[membership(cor)] #color of nodes
poly_cols=c("white","white","white","white", "white","white","white","white") # color of polygens
plot(cor,gor,col=node_cols,mark.border="black",mark.col=poly_cols)
##Network analysis using OE as weight
NL=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/node.csv")
ELOE=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/OE_edge.csv")
goe=graph_from_data_frame(d=ELOE,vertices=NL,directed=FALSE)
goe=set_edge_attr(goe, "weight", value= ELOE$Weight)
is_weighted(goe)## [1] TRUEcoe=cluster_leiden(goe,objective_function = "modularity",n_iterations = 3,resolution_parameter = 1)
plot(coe,goe,vertex.size=5*degree(goe),layout=layout_with_fr,vertex.size=25,edge.arrow.size=5)
node_cols=c("yellow","yellow","red", "yellow", "white","yellow","orange","green","yellow")[membership(coe)] #color of nodes
poly_cols=c("white","white","white","white", "white","white","white","white","white") # color of polygens
plot(coe,goe,col=node_cols,mark.border="black",mark.col=poly_cols)
## Network analysis using Phi as weight
NL=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/node.csv")
ELP=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/Phi_edge.csv")
gp=graph_from_data_frame(d=ELP,vertices=NL,directed=FALSE)
gp=set_edge_attr(gp, "weight", value= ELP$Weight)
is_weighted(gp)## [1] TRUEcp=cluster_leiden(gp,objective_function = "modularity",n_iterations = 3,resolution_parameter = 1)
plot(cp,gp,vertex.size=5*degree(gp),layout=layout_with_fr,vertex.size=25,edge.arrow.size=5)
node_cols=c("red", "yellow", "white", "green", "orange", "lightgray", "yellow")[membership(cp)] #color of nodes
poly_cols=c("white", "white", "white", "white", "white", "white", "white") # color of polygens
plot(cp,gp,col=node_cols,mark.border="black",mark.col=poly_cols,layout=layout_with_fr)
## Network analysis using SCI as weight
NL=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/node.csv")
ELSCI=read.csv("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/SCI_edge.csv")
gsci=graph_from_data_frame(d=ELSCI,vertices=NL,directed=FALSE)
gsci=set_edge_attr(gsci, "weight", value= ELSCI$Weight)
is_weighted(gsci)## [1] TRUEcsci=cluster_leiden(gsci,objective_function = "modularity",n_iterations = 3,resolution_parameter = 1)
plot(csci,gsci,vertex.size=5*degree(gsci),layout=layout_with_fr,vertex.size=25,edge.arrow.size=5)
node_cols=c("red", "white", "green", "orange", "lightgray", "yellow", "yellow","yellow")[membership(csci)] #color of nodes
poly_cols=c("white", "white", "white", "white", "white", "white","white","white") # color of polygens
plot(csci,gsci,col=node_cols,mark.border="black",mark.col=poly_cols)
##SCATTERPLOT OF WEIGHTS
ws=read_sav("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/weights.sav")
p1=ggplot(ws,aes(y=Phi,x=SCI))
p1=p1+geom_point()+geom_smooth(se=FALSE,size=2)
p1=p1+theme_bw()+ theme(panel.grid.minor = element_blank()) + ylab("Phi")+xlab("SCI")
p1=p1+theme(text=element_text(size=10))
p1## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Graph of OE ratios versus prevalence of triad
ratios=read_sav("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/OE graph.sav")
ratios$Expected_frequency=factor(ratios$Expected_frequency,levels=c(0,1),labels=c(">=100","<100"))
p2=ggplot(ratios,aes(y=Ratio,x=Triad_prevalence,color=Expected_frequency,shape=Expected_frequency))
p2=p2+geom_point()
p2=p2+theme_bw()+ theme(panel.grid.minor = element_blank()) + ylab("OE ratio")+xlab("Prevalence of triad")
p2=p2+theme(text=element_text(size=10))
p2=p2+theme(legend.position=c(0.85,0.85))
p2
#Calculation of posterior OE ratio in our data set
triads_OE=read_excel("S://SPH-CLLRData/Linked Data Project 15/ICD coding/PM9 31 March 2022/MANUSCRIPT/comparison of methods/GITHUB PATTERN FINDING/data/triads.xlsx")
num_sub=283195 #sample size
for (m in 1:n){
O<-triads_OE$O[m]
if (O>0){
triads_OE$log_factorial_O[m]<-sum(log(1:O))
}
else {
triads_OE$log_factorial_O[m]<-0
}
}## Warning: Unknown or uninitialised column: `log_factorial_O`.#the likelihood function for Poisson-Gamma model
PG <- function (theta){
O <- O
E <- E
log_factorial_O<-log_factorial_O
a = theta
b = theta
LL <- a * log (b) +
O * log (E) +
lgamma ( O + a ) -
lgamma ( a ) -
log_factorial_O -
( O + a ) * log ( b + num_sub )
return(-sum ( LL ))
}
O<-triads_OE$O
E<-triads_OE$E
log_factorial_O<-triads_OE$log_factorial_O
# set the initial value for parameters
theta <- 1
#R function to estimate parameter; lower shows the minimum value for estimated parameter
par.optim <- optim( par= 60 , PG ,lower=80, upper=Inf,method="L-BFGS-B")
triads_OE$Posterior_OE <- ( par.optim$par [1] + O ) / ( par.optim$par [1] + E )
triads_OE## # A tibble: 2,925 × 7
## condition1 condition2 condi…¹ O E log_f…² Poste…³
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Dementia, Alzheimer's disease Hypertensi… Cerebr… 5567 5202. 42451. 1.07
## 2 Dementia, Alzheimer's disease IHD Hypert… 5453 8132. 41469. 0.674
## 3 IHD Hypertensi… Cardia… 3954 3676. 28800. 1.07
## 4 Hypertensive disease Cerebrovas… Cardia… 3953 2352. 28792. 1.66
## 5 IHD Hypertensi… Other … 3937 4163. 28659. 0.947
## 6 IHD Hypertensi… Cerebr… 3718 5016. 26852. 0.745
## 7 IHD Hypertensi… Heart … 3188 3993. 22535. 0.802
## 8 IHD Hypertensi… Diabet… 3177 3043. 22446. 1.04
## 9 Dementia, Alzheimer's disease Hypertensi… Muscul… 3146 2778. 22196. 1.13
## 10 Hypertensive disease Cerebrovas… Other … 3101 2664. 21834. 1.16
## # … with 2,915 more rows, and abbreviated variable names ¹condition3,
## # ²log_factorial_O, ³Posterior_OE