clain <- read.csv("C:\\Users\\prakruthi\\Desktop\\datascience assignments\\hypothesis\\claimants.csv")
clain## CASENUM ATTORNEY CLMSEX CLMINSUR SEATBELT CLMAGE LOSS
## 1 5 0 0 1 0 50 34.940
## 2 3 1 1 0 0 18 0.891
## 3 66 1 0 1 0 5 0.330
## 4 70 0 0 1 1 31 0.037
## 5 96 1 0 1 0 30 0.038
## 6 97 0 1 1 0 35 0.309
## 7 10 0 0 1 0 9 3.538
## 8 36 0 1 1 0 34 4.881
## 9 51 1 1 1 0 60 0.874
## 10 55 1 0 1 0 NA 0.350
## 11 61 0 1 1 0 37 6.190
## 12 148 0 0 1 0 41 19.610
## 13 150 1 0 1 0 7 1.678
## 14 150 0 1 1 0 40 0.673
## 15 169 1 1 1 0 37 0.143
## 16 171 1 1 0 0 9 0.053
## 17 334 1 1 1 0 58 0.050
## 18 360 0 0 1 0 58 0.758
## 19 376 1 0 1 0 3 0.000
## 20 401 0 1 1 0 38 4.754
## 21 479 0 0 NA 0 37 3.100
## 22 480 1 1 1 0 39 0.130
## 23 550 0 0 0 0 38 16.161
## 24 569 0 0 NA 0 30 0.609
## 25 580 1 0 1 0 54 10.040
## 26 608 1 1 1 0 3 0.787
## 27 603 1 0 1 0 61 0.150
## 28 606 0 0 1 0 0 0.405
## 29 607 1 0 1 0 40 0.405
## 30 630 0 0 1 0 NA 0.595
## 31 640 1 1 1 0 16 3.994
## 32 640 1 0 1 0 NA 0.337
## 33 685 1 0 1 0 9 0.603
## 34 700 0 1 1 0 35 1.673
## 35 710 0 1 1 0 35 4.751
## 36 766 0 1 1 0 17 3.538
## 37 775 0 1 0 0 41 3.185
## 38 805 0 1 1 0 50 3.700
## 39 819 1 0 1 0 33 0.386
## 40 838 0 0 1 0 9 1.970
## 41 851 1 0 1 0 34 0.904
## 42 871 0 1 0 0 33 8.090
## 43 905 0 1 1 0 46 8.090
## 44 941 1 1 1 0 55 13.100
## 45 971 1 0 1 0 5 0.460
## 46 61 1 1 1 0 NA 3.981
## 47 63 0 0 0 0 16 1.740
## 48 78 1 0 1 0 1 0.000
## 49 91 0 0 1 0 NA 1.003
## 50 0 0 0 1 0 40 3.100
## 51 4 0 1 1 0 19 3.777
## 52 54 1 0 1 0 54 0.975
## 53 70 1 1 1 0 NA 0.497
## 54 86 1 1 1 0 NA 0.335
## 55 178 0 1 1 0 60 1.669
## 56 195 0 0 1 0 30 33.633
## 57 301 1 1 1 0 NA 0.305
## 58 304 0 1 0 0 10 6.694
## 59 335 1 1 1 0 44 0.139
## 60 339 1 0 1 0 10 0.987
## 61 354 1 1 1 0 NA 0.000
## 62 359 1 0 1 0 NA 0.530
## 63 366 0 0 NA NA 35 5.563
## 64 387 1 0 1 0 54 0.170
## 65 405 0 1 1 0 38 1.513
## 66 445 1 1 1 0 60 0.730
## 67 470 1 0 1 0 NA 3.638
## 68 505 1 0 1 0 30 0.080
## 69 530 1 1 0 0 17 0.310
## 70 590 0 0 1 0 4 0.300
## 71 607 0 0 1 0 56 44.060
## 72 608 0 0 1 0 63 1.600
## 73 614 0 0 1 0 55 0.895
## 74 664 0 0 1 0 47 0.441
## 75 670 1 0 1 0 0 0.445
## 76 740 0 0 1 0 5 3.500
## 77 743 1 0 1 0 38 0.909
## 78 780 1 1 1 0 NA 0.000
## 79 784 0 0 1 0 40 1.875
## 80 790 1 0 1 0 57 3.889
## 81 840 0 1 1 0 17 1.765
## 82 908 1 1 1 0 1 0.517
## 83 970 0 1 NA 0 NA 9.730
## 84 996 0 1 0 0 11 1.710
## 85 1001 0 0 1 0 40 0.834
## 86 1009 1 1 1 0 10 1.031
## 87 1003 0 0 1 0 18 3.370
## 88 1010 1 1 1 0 35 0.150
## 89 1013 0 1 1 0 11 4.117
## 90 1017 0 1 1 0 16 4.440
## 91 1018 1 1 1 0 NA 0.800
## 92 1054 0 1 1 0 0 4.506
## 93 1063 1 1 1 0 8 0.860
## 94 1091 0 0 1 0 31 3.018
## 95 1010 0 0 1 0 50 1.467
## 96 1011 1 1 1 0 71 0.098
## 97 1013 1 1 1 0 NA 3.681
## 98 1054 0 0 1 0 1 34.571
## 99 1057 0 0 1 0 16 8.698
## 100 1059 1 0 1 0 9 0.460
## 101 1078 1 0 1 0 6 0.710
## 102 1156 1 1 1 0 43 0.535
## 103 1164 0 1 1 0 35 1.435
## 104 1189 0 1 1 0 NA 1.433
## 105 1309 1 0 1 0 7 0.589
## 106 1304 0 0 1 0 43 4.485
## 107 1315 0 0 1 0 19 40.364
## 108 1349 0 1 0 0 10 3.975
## 109 1388 1 1 1 0 31 0.595
## 110 1404 0 0 1 0 NA 0.755
## 111 1470 0 0 1 0 69 1.050
## 112 1473 1 1 1 0 33 0.410
## 113 1491 0 1 0 0 40 5.444
## 114 1496 1 1 1 0 NA 0.365
## 115 1568 1 1 1 0 8 0.380
## 116 1589 0 0 1 0 NA 3.060
## 117 1599 1 1 1 0 NA 0.765
## 118 1606 0 1 1 0 41 3.595
## 119 1655 0 1 1 0 43 3.031
## 120 1661 1 1 1 0 NA 6.350
## 121 1690 0 1 1 0 10 0.305
## 122 1740 0 1 1 0 9 5.813
## 123 1755 0 1 1 0 46 0.050
## 124 1758 0 1 1 0 7 1.990
## 125 1838 0 1 1 0 30 4.414
## 126 1846 1 1 1 0 43 3.863
## 127 1879 1 0 1 0 NA 0.150
## 128 1896 0 0 1 0 33 7.761
## 129 1939 1 0 1 0 54 0.747
## 130 1958 1 0 1 NA 40 1.577
## 131 1990 1 1 1 0 48 0.345
## 132 3067 1 1 1 0 50 0.056
## 133 3087 0 NA NA NA NA 3.040
## 134 3093 0 1 1 0 NA 9.796
## 135 3047 0 1 1 0 31 4.460
## 136 3151 1 0 1 0 1 0.080
## 137 3157 1 0 1 0 NA 0.085
## 138 3190 0 0 1 1 0 1.005
## 139 3198 1 1 1 0 61 0.535
## 140 3305 0 0 1 0 8 0.973
## 141 3314 0 1 1 0 7 0.655
## 142 3337 1 1 1 0 37 3.667
## 143 3384 0 0 1 0 NA 0.615
## 144 3443 0 0 1 0 4 8.616
## 145 3446 0 0 1 0 75 3.000
## 146 3465 1 1 1 0 80 0.091
## 147 3500 1 0 1 0 33 1.380
## 148 3531 0 0 1 0 6 49.560
## 149 3546 0 1 1 0 40 59.497
## 150 3561 1 1 1 0 10 0.443
## 151 3571 0 1 1 0 40 8.740
## 152 3580 0 1 1 0 NA 1.790
## 153 3605 1 1 1 0 6 0.075
## 154 3667 0 0 1 0 66 0.507
## 155 3668 0 1 1 0 41 1.579
## 156 3677 0 1 1 0 56 6.440
## 157 3699 0 0 1 0 17 33.451
## 158 3707 0 1 1 0 13 40.460
## 159 3763 0 0 NA NA 0 9.031
## 160 3791 1 0 0 0 51 0.050
## 161 3800 1 0 0 0 33 1.509
## 162 3848 1 0 1 0 10 0.375
## 163 3874 1 1 1 0 NA 0.800
## 164 3901 0 1 1 0 18 4.089
## 165 3906 1 0 0 NA 39 0.148
## 166 3900 1 0 1 0 0 0.010
## 167 3905 1 0 1 0 46 0.678
## 168 3910 1 1 0 0 39 0.140
## 169 3939 0 0 1 0 10 4.139
## 170 3974 0 1 1 0 34 0.100
## 171 3995 0 0 1 0 8 0.470
## 172 4066 0 1 1 0 41 4.840
## 173 4083 0 1 1 0 7 4.503
## 174 4000 0 1 1 0 18 1.638
## 175 4006 0 1 1 0 41 3.489
## 176 4084 1 0 1 0 10 4.375
## 177 4088 1 1 1 0 1 0.480
## 178 4096 1 1 1 0 6 0.604
## 179 4101 1 1 1 0 4 4.117
## 180 4130 0 0 1 0 15 9.898
## 181 4137 1 0 0 0 8 1.667
## 182 4148 1 0 1 0 5 0.000
## 183 4196 1 0 1 0 0 0.440
## 184 4409 0 1 1 0 NA 1.009
## 185 4465 1 0 1 0 NA 7.064
## 186 4490 0 1 1 0 40 1.530
## 187 4507 0 1 1 0 NA 0.377
## 188 4561 0 0 1 0 61 0.307
## 189 4593 0 0 NA 0 43 0.013
## 190 4681 1 1 0 0 65 0.373
## 191 4684 1 1 1 0 14 1.595
## 192 4695 0 0 1 0 10 5.345
## 193 4707 1 1 1 0 6 0.000
## 194 4708 1 1 1 0 49 3.968
## 195 4780 0 1 1 0 33 0.617
## 196 4789 1 1 1 0 8 1.478
## 197 4799 0 0 1 0 30 3.677
## 198 4835 1 1 1 0 50 0.145
## 199 4844 0 0 1 0 49 0.390
## 200 4915 1 1 NA 0 9 3.600
## 201 4919 1 1 1 0 17 0.054
## 202 4935 1 1 1 0 55 0.500
## 203 4970 0 1 1 0 18 3.086
## 204 5000 0 1 0 0 10 1.847
## 205 5010 0 1 0 0 38 0.714
## 206 5051 0 1 1 0 11 1.100
## 207 5076 1 1 1 0 NA 0.108
## 208 5108 1 0 1 0 41 0.795
## 209 5100 0 1 1 0 37 15.816
## 210 5151 1 0 0 0 13 4.508
## 211 5184 1 1 1 0 49 0.608
## 212 5187 1 1 1 0 10 0.886
## 213 5304 1 1 1 0 4 0.000
## 214 5340 1 1 1 0 40 3.750
## 215 5359 0 1 1 0 60 0.466
## 216 5376 1 1 1 0 77 0.944
## 217 5380 1 1 1 0 10 3.435
## 218 5381 0 1 1 0 8 5.409
## 219 5400 1 1 1 0 0 0.040
## 220 5491 1 1 1 0 14 0.808
## 221 5504 1 1 1 1 7 0.983
## 222 5547 1 0 1 0 44 0.035
## 223 5560 1 0 1 0 NA 3.013
## 224 5639 1 0 1 0 9 1.380
## 225 5641 1 0 1 0 6 0.640
## 226 5666 0 0 1 0 19 1.644
## 227 5685 1 1 1 0 6 1.461
## 228 5708 0 0 1 0 7 0.640
## 229 5730 0 1 1 0 35 4.604
## 230 5745 1 1 1 0 31 3.950
## 231 5748 1 0 1 0 49 0.150
## 232 5758 1 0 1 0 39 1.040
## 233 5800 1 1 1 0 34 4.090
## 234 5819 0 0 0 0 37 1.570
## 235 5881 0 1 NA 0 40 3.068
## 236 5900 0 1 1 0 17 1.619
## 237 5918 0 1 1 0 5 0.901
## 238 5949 0 1 0 0 19 0.885
## 239 5954 1 1 1 0 16 0.968
## 240 5956 1 1 1 0 6 0.011
## 241 5991 1 0 1 0 34 0.660
## 242 6014 1 1 1 0 38 6.509
## 243 6080 0 0 1 0 41 40.000
## 244 6000 0 0 1 0 35 3.033
## 245 6005 1 1 1 0 5 0.094
## 246 6008 1 1 1 0 40 3.700
## 247 6134 1 1 1 0 13 0.958
## 248 6195 1 1 1 0 7 0.500
## 249 6313 0 0 1 0 NA 1.835
## 250 6330 1 1 1 0 8 0.134
## 251 6330 1 1 1 0 49 0.510
## 252 6368 0 1 1 0 50 6.608
## 253 6409 1 0 1 0 50 0.350
## 254 6434 0 0 1 0 10 5.307
## 255 6491 0 1 1 0 NA 3.974
## 256 6543 0 0 1 0 30 4.000
## 257 6570 0 1 1 NA 17 3.630
## 258 6579 0 0 1 0 15 3.070
## 259 6593 0 0 1 0 0 0.709
## 260 6641 0 0 1 1 66 4.530
## 261 6680 1 1 1 0 30 0.877
## 262 6690 1 0 1 0 30 0.118
## 263 6697 1 0 0 0 6 0.598
## 264 6790 1 1 1 0 30 0.760
## 265 6869 0 0 1 0 50 0.675
## 266 6881 0 1 1 0 19 1.740
## 267 6936 1 1 1 0 49 4.164
## 268 6976 1 0 1 NA 40 0.386
## 269 7005 1 1 1 0 54 0.084
## 270 7035 0 0 0 0 3 0.851
## 271 7007 0 1 1 0 5 0.057
## 272 7009 0 1 1 0 6 4.300
## 273 7013 0 0 1 0 39 1.088
## 274 7035 1 1 1 0 9 3.000
## 275 7038 0 1 1 0 NA 0.909
## 276 7041 0 1 1 0 5 0.780
## 277 7057 0 1 1 0 37 4.018
## 278 7060 1 0 1 0 NA 81.000
## 279 7097 1 1 1 0 5 0.518
## 280 7107 0 0 1 0 47 0.150
## 281 7108 0 1 1 0 69 1.076
## 282 7140 1 0 1 0 38 1.031
## 283 7163 1 0 1 0 NA 0.011
## 284 7309 0 1 1 1 19 3.714
## 285 7350 1 1 1 0 NA 0.030
## 286 7381 1 1 1 0 7 7.450
## 287 7386 0 0 1 0 10 1.617
## 288 7406 0 1 1 0 19 68.560
## 289 7477 1 0 1 0 6 0.708
## 290 7484 0 1 1 0 5 6.080
## 291 7498 1 1 1 0 31 0.010
## 292 7586 0 1 1 0 17 3.340
## 293 7605 0 0 1 0 6 1.470
## 294 7647 0 1 1 0 10 3.330
## 295 7667 1 1 1 0 NA 3.099
## 296 7670 0 1 0 0 6 1.617
## 297 7694 1 0 1 0 0 0.500
## 298 7704 1 1 1 0 39 0.434
## 299 7707 1 0 1 0 NA 0.040
## 300 7713 1 1 1 0 46 1.470
## 301 7748 0 0 1 0 7 0.400
## 302 7769 1 1 0 0 NA 0.008
## 303 7776 1 0 1 0 41 3.310
## 304 7779 0 1 1 0 8 0.100
## 305 7791 1 1 1 0 8 0.700
## 306 7855 1 0 1 0 33 0.886
## 307 7883 1 1 1 0 5 4.058
## 308 7904 1 0 1 0 57 0.500
## 309 7909 1 1 1 0 36 0.150
## 310 7930 1 0 1 0 35 9.061
## 311 7971 1 0 1 0 41 0.746
## 312 7980 0 1 1 0 15 1.377
## 313 7996 0 1 0 0 13 1.056
## 314 7999 0 0 0 0 49 0.998
## 315 8005 1 1 1 0 0 1.380
## 316 8033 1 1 1 0 13 0.100
## 317 8036 1 0 1 0 0 0.005
## 318 8051 1 1 1 0 6 0.040
## 319 8174 1 1 1 0 NA 0.050
## 320 8300 0 0 1 0 36 1.104
## 321 8336 1 1 1 0 48 0.470
## 322 8338 1 0 1 0 10 0.505
## 323 8354 1 1 1 0 39 1.113
## 324 8380 1 0 1 0 6 4.478
## 325 8397 0 0 NA 0 NA 18.060
## 326 8400 1 1 1 0 46 0.005
## 327 8406 0 1 1 0 0 0.057
## 328 8410 0 1 1 0 9 3.309
## 329 8414 1 0 1 0 38 1.408
## 330 8418 1 1 1 0 6 5.049
## 331 8434 0 0 0 0 5 0.368
## 332 8471 1 0 1 0 54 1.405
## 333 8509 1 1 1 0 NA 1.000
## 334 8519 0 0 0 0 31 41.115
## 335 8570 0 0 1 0 8 5.830
## 336 8581 1 0 1 0 NA 0.900
## 337 8647 0 0 1 0 46 4.159
## 338 8673 1 1 1 0 33 0.780
## 339 8686 1 0 1 0 7 0.701
## 340 8699 1 1 1 0 11 4.190
## 341 8706 0 1 1 0 6 0.140
## 342 8713 1 1 1 0 41 4.691
## 343 8748 0 0 1 0 39 1.043
## 344 8770 1 1 1 0 16 0.861
## 345 8811 0 0 1 0 36 0.730
## 346 8816 0 0 0 0 19 1.430
## 347 8830 1 1 0 0 61 0.830
## 348 8846 1 1 1 0 56 3.009
## 349 8886 0 1 1 0 14 1.830
## 350 8938 1 1 1 0 34 4.349
## 351 8939 0 0 1 0 45 5.735
## 352 8940 0 1 1 0 53 7.549
## 353 8999 1 0 1 0 38 0.199
## 354 9047 1 1 NA NA 10 0.163
## 355 9081 0 1 0 0 13 3.091
## 356 9030 0 1 0 0 36 4.405
## 357 9034 1 0 1 0 16 1.850
## 358 9071 0 1 1 0 31 9.017
## 359 9086 1 1 1 0 41 0.671
## 360 9097 0 1 1 0 60 3.997
## 361 9103 1 0 1 0 10 0.090
## 362 9113 0 1 1 0 44 3.199
## 363 9114 1 0 NA 0 9 1.770
## 364 9146 0 1 1 0 46 173.604
## 365 9151 1 1 1 0 8 0.405
## 366 9173 1 1 1 0 38 3.055
## 367 9304 0 0 1 0 NA 4.040
## 368 9330 0 0 1 0 34 0.567
## 369 9378 1 0 1 0 16 1.070
## 370 9407 1 0 1 0 0 0.035
## 371 9400 1 0 1 0 59 0.000
## 372 9437 0 1 1 0 30 3.634
## 373 9443 1 1 1 0 9 0.470
## 374 9510 0 1 1 0 NA 3.335
## 375 9514 0 1 1 0 46 8.801
## 376 9544 1 1 1 0 40 5.440
## 377 9546 0 1 1 0 54 1.149
## 378 9553 0 1 1 0 37 4.860
## 379 9555 0 1 1 0 67 1.035
## 380 9581 0 0 1 0 34 3.850
## 381 9600 0 1 1 0 8 0.989
## 382 9603 1 1 1 0 59 3.595
## 383 9604 0 1 1 0 35 8.508
## 384 9608 0 1 1 0 41 1.306
## 385 9605 0 0 1 0 6 0.905
## 386 9647 1 0 0 0 NA 0.371
## 387 9653 0 1 1 0 35 0.860
## 388 9660 1 1 1 0 55 0.604
## 389 9704 0 0 1 0 NA 3.595
## 390 9704 0 1 1 0 16 4.665
## 391 9758 1 1 1 0 36 1.351
## 392 9791 1 0 1 0 8 0.083
## 393 9793 1 1 1 0 6 0.495
## 394 9806 0 1 1 0 3 0.035
## 395 9800 0 1 NA NA 5 5.163
## 396 9817 1 1 1 0 NA 0.756
## 397 9879 1 1 1 0 54 1.677
## 398 9910 1 0 NA 0 40 1.408
## 399 38 1 1 1 0 NA 0.056
## 400 6 0 0 1 0 NA 0.040
## 401 5 0 1 1 0 43 6.109
## 402 7 1 1 NA NA 13 0.647
## 403 19 0 0 1 0 83 18.109
## 404 106 0 0 1 0 10 50.000
## 405 116 1 0 0 0 NA 0.684
## 406 156 0 0 1 0 13 4.776
## 407 188 0 1 1 0 35 5.405
## 408 198 1 1 1 NA 0 0.584
## 409 308 0 1 1 0 15 1.998
## 410 319 0 1 1 0 NA 1.995
## 411 479 0 1 1 0 45 49.015
## 412 545 1 1 1 0 40 0.834
## 413 564 0 0 0 0 5 1.600
## 414 565 1 0 1 0 49 1.680
## 415 575 0 0 1 0 14 4.916
## 416 590 0 0 1 0 48 68.548
## 417 593 1 0 1 0 43 0.745
## 418 606 0 0 1 0 60 5.087
## 419 758 1 1 1 0 9 0.185
## 420 861 0 0 0 0 50 3.719
## 421 870 1 1 1 0 10 1.970
## 422 876 1 0 1 0 40 0.505
## 423 884 0 0 1 0 6 0.396
## 424 895 1 1 1 0 11 0.487
## 425 945 1 0 0 0 15 0.540
## 426 960 0 1 1 0 11 1.130
## 427 993 1 1 1 0 10 0.385
## 428 997 0 1 NA NA NA 1.700
## 429 6 0 1 1 NA 4 0.057
## 430 30 0 1 1 0 16 0.089
## 431 64 0 0 0 0 15 0.984
## 432 94 0 1 1 0 NA 0.406
## 433 7 0 1 1 0 11 6.084
## 434 34 0 1 1 0 59 54.711
## 435 95 1 1 1 0 NA 3.584
## 436 99 0 0 1 0 36 4.045
## 437 158 0 1 1 0 6 3.470
## 438 169 1 1 1 0 40 4.000
## 439 171 0 1 1 0 6 3.883
## 440 189 1 0 1 0 57 3.889
## 441 196 1 0 1 0 NA 0.100
## 442 300 0 0 1 0 NA 1.630
## 443 308 0 1 1 0 40 0.857
## 444 330 1 1 1 0 7 0.557
## 445 331 0 1 1 0 10 3.064
## 446 363 0 1 1 0 NA 3.500
## 447 364 1 1 1 1 5 0.555
## 448 450 1 1 1 0 33 3.089
## 449 453 0 0 1 0 64 3.457
## 450 456 0 1 1 0 19 3.950
## 451 460 1 1 0 0 37 1.868
## 452 579 1 1 0 0 7 0.600
## 453 590 0 1 1 NA 5 0.488
## 454 617 0 1 1 0 5 0.995
## 455 656 0 1 NA NA 0 0.109
## 456 696 1 0 NA 0 35 0.500
## 457 700 0 1 0 0 43 7.068
## 458 703 1 1 1 0 48 0.116
## 459 733 0 1 1 0 NA 93.000
## 460 763 1 1 NA NA 66 0.985
## 461 768 1 1 1 0 10 1.069
## 462 873 1 1 1 0 0 0.080
## 463 883 1 0 1 0 34 1.360
## 464 890 1 0 1 0 14 1.086
## 465 907 1 0 1 0 30 0.500
## 466 901 0 NA NA NA NA 1.069
## 467 904 1 1 1 0 50 0.045
## 468 905 1 0 1 NA 38 0.405
## 469 907 0 1 1 0 1 3.407
## 470 933 1 0 1 0 6 0.548
## 471 936 0 0 0 0 5 1.830
## 472 1003 1 0 1 0 NA 0.470
## 473 1090 0 1 1 0 50 4.005
## 474 1010 0 0 1 0 6 5.505
## 475 1030 0 0 0 0 30 8.070
## 476 1031 0 1 1 0 14 0.080
## 477 1058 0 0 1 1 39 61.047
## 478 1085 0 1 1 0 48 1.050
## 479 1110 0 0 1 1 10 1.318
## 480 1139 0 1 1 0 13 0.160
## 481 1150 1 0 1 0 47 0.090
## 482 1186 0 1 1 0 70 0.566
## 483 1308 1 1 1 0 35 0.975
## 484 1404 1 0 NA NA NA 0.370
## 485 1454 1 1 1 0 NA 0.783
## 486 1474 0 1 1 0 70 1.698
## 487 1478 1 1 1 0 39 0.840
## 488 1495 1 0 1 0 43 3.049
## 489 1500 0 0 0 0 NA 0.081
## 490 1544 0 1 1 0 35 3.603
## 491 1545 1 0 1 0 NA 0.400
## 492 1580 0 NA 1 0 14 5.500
## 493 1598 1 1 1 0 47 0.609
## 494 1670 0 1 1 0 15 3.085
## 495 1700 1 0 0 0 8 0.066
## 496 1767 1 1 1 0 NA 0.085
## 497 1785 0 1 1 0 9 1.645
## 498 1810 1 1 1 0 7 0.405
## 499 1843 1 1 1 0 3 0.100
## 500 1847 0 1 1 0 35 6.416
## 501 1901 0 0 1 0 55 6.850
## 502 1910 1 0 1 0 45 3.955
## 503 1935 0 0 1 0 40 7.700
## 504 1944 1 0 1 0 1 0.300
## 505 1980 1 0 1 1 49 1.633
## 506 1985 1 0 1 0 NA 3.854
## 507 1993 0 0 1 0 50 3.054
## 508 3003 1 0 1 0 47 0.150
## 509 3034 0 0 1 0 8 1.908
## 510 3070 0 1 1 1 NA 1.619
## 511 3090 1 0 1 0 19 0.957
## 512 3043 0 1 1 0 8 3.086
## 513 3060 0 0 1 0 5 0.453
## 514 3096 1 1 1 0 64 0.061
## 515 3133 1 0 1 0 19 0.605
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## 520 3334 1 1 1 0 0 0.151
## 521 3401 0 1 1 0 63 0.340
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## 523 3570 0 NA 1 0 NA 7.857
## 524 3608 0 1 NA NA 7 7.707
## 525 3700 0 0 1 0 5 0.065
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## 527 3734 0 0 1 0 57 6.063
## 528 3748 0 0 NA 0 NA 0.005
## 529 3753 0 1 1 0 51 4.366
## 530 3808 0 0 1 0 30 1.548
## 531 3840 1 0 1 0 NA 0.431
## 532 3906 1 1 1 0 50 0.435
## 533 3930 0 0 1 0 1 0.095
## 534 3957 1 1 1 1 30 1.069
## 535 3978 1 1 1 0 47 0.077
## 536 3990 0 1 1 0 3 0.843
## 537 4050 1 1 1 0 11 5.515
## 538 4084 0 0 1 0 7 1.305
## 539 4000 1 1 1 0 NA 3.510
## 540 4005 1 0 1 0 9 8.080
## 541 4030 0 0 1 0 31 3.035
## 542 4040 0 0 0 0 5 0.656
## 543 4044 0 0 1 0 56 3.010
## 544 4047 0 0 1 0 44 3.666
## 545 4067 1 0 1 0 34 3.394
## 546 4074 0 1 1 0 49 3.760
## 547 4086 1 1 1 0 NA 3.090
## 548 4100 0 1 NA NA 0 0.910
## 549 4161 0 0 0 0 43 5.083
## 550 4163 1 1 1 0 64 0.948
## 551 4171 0 0 1 0 NA 7.438
## 552 4179 0 0 0 0 16 0.830
## 553 4305 0 0 1 0 19 4.781
## 554 4319 0 1 0 0 3 0.579
## 555 4350 1 1 NA NA NA 0.500
## 556 4357 1 1 1 0 34 8.400
## 557 4366 1 0 1 0 58 0.895
## 558 4375 1 0 1 0 30 0.565
## 559 4404 0 0 1 0 57 4.996
## 560 4458 1 1 1 0 NA 0.105
## 561 4489 0 1 0 0 14 3.804
## 562 4507 0 1 1 0 11 3.754
## 563 4565 1 1 1 0 NA 0.060
## 564 4600 1 1 1 0 4 1.883
## 565 4604 1 1 1 0 37 0.500
## 566 4633 0 0 0 0 19 3.040
## 567 4679 1 1 1 0 39 0.000
## 568 4697 1 1 0 0 40 8.406
## 569 4740 0 0 1 0 54 1.077
## 570 4767 0 0 1 0 5 1.737
## 571 4799 0 0 1 0 16 0.091
## 572 4806 1 1 1 0 44 3.313
## 573 4830 0 0 1 0 3 0.600
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## 575 4859 0 1 1 0 49 6.371
## 576 4863 0 0 1 0 70 3.706
## 577 4893 1 0 1 0 10 0.305
## 578 4908 0 1 1 0 15 4.554
## 579 4967 1 0 1 0 7 0.778
## 580 4975 0 0 1 0 NA 66.689
## 581 4991 0 1 1 0 8 6.047
## 582 4995 1 0 1 0 30 0.384
## 583 4998 1 1 1 0 10 0.107
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## 1132 19075 0 0 1 0 8 3.514
## 1133 19033 1 1 1 0 NA 0.050
## 1134 19061 0 1 1 0 13 4.095
## 1135 19067 1 0 1 0 36 1.590
## 1136 19069 1 1 1 0 57 3.915
## 1137 19070 1 1 1 0 61 0.390
## 1138 19087 0 0 1 0 48 0.755
## 1139 19093 1 0 0 0 17 0.579
## 1140 19108 0 0 1 0 10 5.057
## 1141 19173 0 1 1 0 30 1.000
## 1142 19195 0 1 1 0 NA 4.165
## 1143 19350 1 0 1 0 44 4.569
## 1144 19389 0 1 1 0 13 3.807
## 1145 19395 1 1 1 0 39 4.061
## 1146 19406 1 0 1 0 13 0.130
## 1147 19404 0 1 1 0 46 0.507
## 1148 19410 0 0 0 0 51 40.574
## 1149 19440 1 1 1 0 7 3.734
## 1150 19446 0 1 1 NA 7 0.156
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## 1152 19465 0 1 1 0 54 4.097
## 1153 19490 1 0 1 0 NA 0.150
## 1154 19558 1 0 1 0 44 3.549
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## 1156 19609 1 1 1 0 0 0.090
## 1157 19605 0 0 1 0 37 1.951
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## 1159 19683 0 0 0 0 3 0.378
## 1160 19701 0 1 1 0 6 3.530
## 1161 19710 1 1 1 0 5 0.155
## 1162 19761 1 0 1 0 NA 1.340
## 1163 19765 0 1 0 0 40 10.718
## 1164 19790 1 0 NA NA 11 3.318
## 1165 19814 1 0 1 0 34 0.050
## 1166 19837 0 0 1 0 76 0.150
## 1167 19857 1 NA NA NA NA 0.000
## 1168 19884 0 1 1 0 50 1.065
## 1169 19961 1 1 1 0 6 4.065
## 1170 30010 0 0 NA NA NA 3.490
## 1171 30017 0 1 1 0 3 0.095
## 1172 30033 1 0 1 0 9 3.578
## 1173 30085 1 0 1 0 13 0.746
## 1174 30064 1 1 1 0 68 0.004
## 1175 30163 0 0 1 0 16 1.909
## 1176 30306 0 NA 1 0 NA 3.675
## 1177 30357 0 0 1 0 56 15.001
## 1178 30381 0 0 1 0 38 5.060
## 1179 30384 1 1 1 0 13 1.600
## 1180 30430 0 1 1 0 37 13.789
## 1181 30436 1 0 1 0 NA 0.098
## 1182 30448 0 0 0 0 9 3.760
## 1183 30483 0 1 NA NA 33 4.835
## 1184 30490 1 0 1 0 0 0.309
## 1185 30498 0 0 1 0 10 3.190
## 1186 30501 1 1 1 0 34 0.674
## 1187 30548 1 0 1 0 NA 0.040
## 1188 30557 1 0 1 0 40 0.915
## 1189 30588 0 1 1 0 44 13.000
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## 1191 30634 0 0 1 0 48 3.549
## 1192 30670 0 1 1 0 0 0.535
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## 1195 30781 1 1 1 0 7 0.599
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## 1197 30793 0 1 1 0 35 4.333
## 1198 30799 0 0 1 0 76 3.756
## 1199 30816 1 1 1 0 17 0.497
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## 1203 30909 1 1 1 0 11 0.436
## 1204 30915 1 0 1 0 38 0.800
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## 1208 30050 0 0 1 0 40 3.590
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## 1210 30063 1 1 1 0 55 3.348
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## 1215 30179 1 1 1 0 7 0.060
## 1216 30383 0 1 1 0 11 8.641
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## 1218 30408 0 1 1 0 19 5.100
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## 1221 30584 1 1 1 0 47 0.600
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## 1223 30709 0 1 1 0 48 1.334
## 1224 30751 1 NA 1 0 34 0.150
## 1225 30770 0 1 1 0 33 0.706
## 1226 30775 0 1 1 0 44 3.713
## 1227 30783 0 0 1 0 NA 3.899
## 1228 30806 0 0 1 0 NA 0.161
## 1229 30816 0 0 1 0 46 1.807
## 1230 30840 1 1 1 0 NA 0.570
## 1231 30858 1 1 1 0 39 0.944
## 1232 30880 1 0 1 0 NA 0.100
## 1233 30903 1 1 0 0 49 4.491
## 1234 30957 0 1 1 0 58 3.306
## 1235 30961 1 1 1 0 NA 1.640
## 1236 30976 0 1 1 0 0 0.763
## 1237 30998 0 1 1 0 15 1.556
## 1238 31003 0 1 1 0 70 0.095
## 1239 31011 1 1 1 0 40 0.398
## 1240 31016 1 1 1 0 8 0.015
## 1241 31036 0 0 1 0 30 3.570
## 1242 31056 0 1 0 0 31 3.343
## 1243 31059 0 0 1 0 46 6.040
## 1244 31075 0 1 1 0 10 1.673
## 1245 31096 1 1 1 0 NA 0.175
## 1246 31001 1 0 1 0 50 0.135
## 1247 31008 1 NA 1 0 45 0.358
## 1248 31010 1 1 1 0 8 0.000
## 1249 31041 0 0 1 0 50 1.015
## 1250 31056 0 1 1 0 NA 0.370
## 1251 31081 0 0 NA NA 7 1.850
## 1252 31094 0 0 1 0 17 1.968
## 1253 31107 0 1 1 0 0 0.300
## 1254 31111 1 0 1 0 66 1.545
## 1255 31118 0 0 1 0 37 4.318
## 1256 31141 1 0 1 0 48 0.455
## 1257 31159 0 0 1 1 30 30.640
## 1258 31330 1 0 1 0 7 0.453
## 1259 31350 1 1 1 0 36 6.908
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## 1266 31531 0 0 1 0 NA 0.565
## 1267 31556 1 1 1 0 37 0.193
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## 1269 31590 0 0 1 0 50 1.608
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## 1288 33050 1 0 0 0 10 0.040
## 1289 33068 1 1 1 0 4 1.165
## 1290 33073 0 1 1 0 41 5.600
## 1291 33085 1 0 1 1 NA 6.100
## 1292 33144 1 1 1 0 19 1.537
## 1293 33150 1 0 1 0 10 0.964
## 1294 33165 1 0 1 0 40 0.749
## 1295 33177 0 1 1 0 NA 1.041
## 1296 33198 0 1 1 0 18 3.180
## 1297 33307 1 0 1 0 36 4.448
## 1298 33318 0 0 0 0 NA 3.800
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## 1302 33450 1 1 1 0 NA 0.700
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## 1305 33468 1 1 1 0 NA 0.500
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## 1310 33590 0 0 1 0 30 3.034
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## 1313 33661 0 1 1 0 45 14.884
## 1314 33687 1 1 1 0 7 0.986
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## 1316 33741 0 0 1 0 8 1.105
## 1317 33744 1 1 0 NA 10 0.158
## 1318 33750 0 0 1 0 7 3.535
## 1319 33759 1 1 1 0 NA 0.070
## 1320 33778 1 0 1 0 34 0.763
## 1321 33840 1 1 1 0 NA 0.540
## 1322 33858 0 0 1 0 NA 3.437
## 1323 33865 1 1 1 0 1 0.080
## 1324 33881 0 1 1 0 9 5.460
## 1325 33883 0 0 1 0 10 6.564
## 1326 34000 0 1 1 0 9 3.169
## 1327 34035 0 1 1 0 7 0.688
## 1328 34050 0 1 1 0 70 0.505
## 1329 34055 0 0 1 0 14 0.400
## 1330 34085 1 1 1 0 33 0.535
## 1331 34011 1 0 1 0 33 6.817
## 1332 34030 0 1 1 0 3 0.950
## 1333 34066 1 1 1 0 9 0.000
## 1334 34089 1 0 1 0 49 0.000
## 1335 34104 1 1 1 0 16 0.060
## 1336 34100 1 0 1 0 NA 0.576
## 1337 34110 0 1 1 0 46 3.705
## 1338 34113 1 1 1 0 39 0.099
## 1339 34145 0 1 0 0 8 3.177
## 1340 34153 1 1 1 0 30 0.688str(clain)## 'data.frame': 1340 obs. of 7 variables:
## $ CASENUM : int 5 3 66 70 96 97 10 36 51 55 ...
## $ ATTORNEY: int 0 1 1 0 1 0 0 0 1 1 ...
## $ CLMSEX : int 0 1 0 0 0 1 0 1 1 0 ...
## $ CLMINSUR: int 1 0 1 1 1 1 1 1 1 1 ...
## $ SEATBELT: int 0 0 0 1 0 0 0 0 0 0 ...
## $ CLMAGE : int 50 18 5 31 30 35 9 34 60 NA ...
## $ LOSS : num 34.94 0.891 0.33 0.037 0.038 ...summary(clain)## CASENUM ATTORNEY CLMSEX CLMINSUR
## Min. : 0 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 4177 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000
## Median : 8756 Median :0.0000 Median :1.0000 Median :1.0000
## Mean :11202 Mean :0.4888 Mean :0.5587 Mean :0.9076
## 3rd Qu.:15702 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :34153 Max. :1.0000 Max. :1.0000 Max. :1.0000
## NA's :12 NA's :41
## SEATBELT CLMAGE LOSS
## Min. :0.00000 Min. : 0.00 Min. : 0.000
## 1st Qu.:0.00000 1st Qu.: 9.00 1st Qu.: 0.400
## Median :0.00000 Median :30.00 Median : 1.069
## Mean :0.01703 Mean :28.41 Mean : 3.806
## 3rd Qu.:0.00000 3rd Qu.:43.00 3rd Qu.: 3.781
## Max. :1.00000 Max. :95.00 Max. :173.604
## NA's :48 NA's :189class(clain)## [1] "data.frame"colnames(clain)## [1] "CASENUM" "ATTORNEY" "CLMSEX" "CLMINSUR" "SEATBELT" "CLMAGE"
## [7] "LOSS"a <- na.omit(clain)
a## CASENUM ATTORNEY CLMSEX CLMINSUR SEATBELT CLMAGE LOSS
## 1 5 0 0 1 0 50 34.940
## 2 3 1 1 0 0 18 0.891
## 3 66 1 0 1 0 5 0.330
## 4 70 0 0 1 1 31 0.037
## 5 96 1 0 1 0 30 0.038
## 6 97 0 1 1 0 35 0.309
## 7 10 0 0 1 0 9 3.538
## 8 36 0 1 1 0 34 4.881
## 9 51 1 1 1 0 60 0.874
## 11 61 0 1 1 0 37 6.190
## 12 148 0 0 1 0 41 19.610
## 13 150 1 0 1 0 7 1.678
## 14 150 0 1 1 0 40 0.673
## 15 169 1 1 1 0 37 0.143
## 16 171 1 1 0 0 9 0.053
## 17 334 1 1 1 0 58 0.050
## 18 360 0 0 1 0 58 0.758
## 19 376 1 0 1 0 3 0.000
## 20 401 0 1 1 0 38 4.754
## 22 480 1 1 1 0 39 0.130
## 23 550 0 0 0 0 38 16.161
## 25 580 1 0 1 0 54 10.040
## 26 608 1 1 1 0 3 0.787
## 27 603 1 0 1 0 61 0.150
## 28 606 0 0 1 0 0 0.405
## 29 607 1 0 1 0 40 0.405
## 31 640 1 1 1 0 16 3.994
## 33 685 1 0 1 0 9 0.603
## 34 700 0 1 1 0 35 1.673
## 35 710 0 1 1 0 35 4.751
## 36 766 0 1 1 0 17 3.538
## 37 775 0 1 0 0 41 3.185
## 38 805 0 1 1 0 50 3.700
## 39 819 1 0 1 0 33 0.386
## 40 838 0 0 1 0 9 1.970
## 41 851 1 0 1 0 34 0.904
## 42 871 0 1 0 0 33 8.090
## 43 905 0 1 1 0 46 8.090
## 44 941 1 1 1 0 55 13.100
## 45 971 1 0 1 0 5 0.460
## 47 63 0 0 0 0 16 1.740
## 48 78 1 0 1 0 1 0.000
## 50 0 0 0 1 0 40 3.100
## 51 4 0 1 1 0 19 3.777
## 52 54 1 0 1 0 54 0.975
## 55 178 0 1 1 0 60 1.669
## 56 195 0 0 1 0 30 33.633
## 58 304 0 1 0 0 10 6.694
## 59 335 1 1 1 0 44 0.139
## 60 339 1 0 1 0 10 0.987
## 64 387 1 0 1 0 54 0.170
## 65 405 0 1 1 0 38 1.513
## 66 445 1 1 1 0 60 0.730
## 68 505 1 0 1 0 30 0.080
## 69 530 1 1 0 0 17 0.310
## 70 590 0 0 1 0 4 0.300
## 71 607 0 0 1 0 56 44.060
## 72 608 0 0 1 0 63 1.600
## 73 614 0 0 1 0 55 0.895
## 74 664 0 0 1 0 47 0.441
## 75 670 1 0 1 0 0 0.445
## 76 740 0 0 1 0 5 3.500
## 77 743 1 0 1 0 38 0.909
## 79 784 0 0 1 0 40 1.875
## 80 790 1 0 1 0 57 3.889
## 81 840 0 1 1 0 17 1.765
## 82 908 1 1 1 0 1 0.517
## 84 996 0 1 0 0 11 1.710
## 85 1001 0 0 1 0 40 0.834
## 86 1009 1 1 1 0 10 1.031
## 87 1003 0 0 1 0 18 3.370
## 88 1010 1 1 1 0 35 0.150
## 89 1013 0 1 1 0 11 4.117
## 90 1017 0 1 1 0 16 4.440
## 92 1054 0 1 1 0 0 4.506
## 93 1063 1 1 1 0 8 0.860
## 94 1091 0 0 1 0 31 3.018
## 95 1010 0 0 1 0 50 1.467
## 96 1011 1 1 1 0 71 0.098
## 98 1054 0 0 1 0 1 34.571
## 99 1057 0 0 1 0 16 8.698
## 100 1059 1 0 1 0 9 0.460
## 101 1078 1 0 1 0 6 0.710
## 102 1156 1 1 1 0 43 0.535
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## 105 1309 1 0 1 0 7 0.589
## 106 1304 0 0 1 0 43 4.485
## 107 1315 0 0 1 0 19 40.364
## 108 1349 0 1 0 0 10 3.975
## 109 1388 1 1 1 0 31 0.595
## 111 1470 0 0 1 0 69 1.050
## 112 1473 1 1 1 0 33 0.410
## 113 1491 0 1 0 0 40 5.444
## 115 1568 1 1 1 0 8 0.380
## 118 1606 0 1 1 0 41 3.595
## 119 1655 0 1 1 0 43 3.031
## 121 1690 0 1 1 0 10 0.305
## 122 1740 0 1 1 0 9 5.813
## 123 1755 0 1 1 0 46 0.050
## 124 1758 0 1 1 0 7 1.990
## 125 1838 0 1 1 0 30 4.414
## 126 1846 1 1 1 0 43 3.863
## 128 1896 0 0 1 0 33 7.761
## 129 1939 1 0 1 0 54 0.747
## 131 1990 1 1 1 0 48 0.345
## 132 3067 1 1 1 0 50 0.056
## 135 3047 0 1 1 0 31 4.460
## 136 3151 1 0 1 0 1 0.080
## 138 3190 0 0 1 1 0 1.005
## 139 3198 1 1 1 0 61 0.535
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## 142 3337 1 1 1 0 37 3.667
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## 153 3605 1 1 1 0 6 0.075
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## 156 3677 0 1 1 0 56 6.440
## 157 3699 0 0 1 0 17 33.451
## 158 3707 0 1 1 0 13 40.460
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## 162 3848 1 0 1 0 10 0.375
## 164 3901 0 1 1 0 18 4.089
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## 173 4083 0 1 1 0 7 4.503
## 174 4000 0 1 1 0 18 1.638
## 175 4006 0 1 1 0 41 3.489
## 176 4084 1 0 1 0 10 4.375
## 177 4088 1 1 1 0 1 0.480
## 178 4096 1 1 1 0 6 0.604
## 179 4101 1 1 1 0 4 4.117
## 180 4130 0 0 1 0 15 9.898
## 181 4137 1 0 0 0 8 1.667
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## 244 6000 0 0 1 0 35 3.033
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## 1132 19075 0 0 1 0 8 3.514
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## 1188 30557 1 0 1 0 40 0.915
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## 1197 30793 0 1 1 0 35 4.333
## 1198 30799 0 0 1 0 76 3.756
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## 1226 30775 0 1 1 0 44 3.713
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## 1242 31056 0 1 0 0 31 3.343
## 1243 31059 0 0 1 0 46 6.040
## 1244 31075 0 1 1 0 10 1.673
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## 1253 31107 0 1 1 0 0 0.300
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## 1256 31141 1 0 1 0 48 0.455
## 1257 31159 0 0 1 1 30 30.640
## 1258 31330 1 0 1 0 7 0.453
## 1259 31350 1 1 1 0 36 6.908
## 1260 31386 0 0 1 0 39 4.300
## 1261 31388 1 0 1 0 0 0.440
## 1262 31400 0 1 1 0 35 1.701
## 1263 31480 0 0 1 0 0 0.013
## 1264 31500 0 1 1 0 39 0.936
## 1265 31508 1 0 1 0 36 0.507
## 1267 31556 1 1 1 0 37 0.193
## 1269 31590 0 0 1 0 50 1.608
## 1271 31637 1 1 1 0 7 0.449
## 1272 31667 0 0 1 0 40 3.735
## 1273 31700 1 1 1 0 34 0.150
## 1274 31739 1 1 1 0 18 0.850
## 1275 31750 1 1 1 0 0 0.300
## 1276 31784 1 1 1 0 5 0.190
## 1278 31905 0 0 1 0 9 7.600
## 1279 31944 0 1 1 0 9 6.459
## 1280 31960 0 1 1 0 50 4.986
## 1281 31994 0 0 1 0 36 0.465
## 1282 33005 0 0 1 0 30 3.735
## 1283 33033 0 1 1 0 0 0.067
## 1284 33047 0 0 0 0 30 4.147
## 1285 33054 1 0 1 0 8 0.485
## 1286 33015 1 1 1 0 40 0.050
## 1287 33037 0 1 1 1 44 55.709
## 1288 33050 1 0 0 0 10 0.040
## 1289 33068 1 1 1 0 4 1.165
## 1290 33073 0 1 1 0 41 5.600
## 1292 33144 1 1 1 0 19 1.537
## 1293 33150 1 0 1 0 10 0.964
## 1294 33165 1 0 1 0 40 0.749
## 1296 33198 0 1 1 0 18 3.180
## 1297 33307 1 0 1 0 36 4.448
## 1299 33330 1 1 1 0 8 1.195
## 1300 33379 1 1 1 0 19 7.393
## 1301 33440 0 0 1 0 71 1.595
## 1303 33455 1 1 1 0 9 0.364
## 1307 33503 1 1 1 0 38 0.005
## 1308 33553 1 1 1 0 10 0.019
## 1309 33587 0 1 1 0 43 5.434
## 1310 33590 0 0 1 0 30 3.034
## 1311 33607 0 1 1 0 40 0.905
## 1312 33614 0 0 1 0 40 5.168
## 1313 33661 0 1 1 0 45 14.884
## 1314 33687 1 1 1 0 7 0.986
## 1315 33697 1 0 1 0 7 0.107
## 1316 33741 0 0 1 0 8 1.105
## 1318 33750 0 0 1 0 7 3.535
## 1320 33778 1 0 1 0 34 0.763
## 1323 33865 1 1 1 0 1 0.080
## 1324 33881 0 1 1 0 9 5.460
## 1325 33883 0 0 1 0 10 6.564
## 1326 34000 0 1 1 0 9 3.169
## 1327 34035 0 1 1 0 7 0.688
## 1328 34050 0 1 1 0 70 0.505
## 1329 34055 0 0 1 0 14 0.400
## 1330 34085 1 1 1 0 33 0.535
## 1331 34011 1 0 1 0 33 6.817
## 1332 34030 0 1 1 0 3 0.950
## 1333 34066 1 1 1 0 9 0.000
## 1334 34089 1 0 1 0 49 0.000
## 1335 34104 1 1 1 0 16 0.060
## 1337 34110 0 1 1 0 46 3.705
## 1338 34113 1 1 1 0 39 0.099
## 1339 34145 0 1 0 0 8 3.177
## 1340 34153 1 1 1 0 30 0.688model <- glm( ATTORNEY ~ .,data=a )
model##
## Call: glm(formula = ATTORNEY ~ ., data = a)
##
## Coefficients:
## (Intercept) CASENUM CLMSEX CLMINSUR SEATBELT
## 3.299e-01 -5.204e-08 8.628e-02 1.426e-01 -1.181e-01
## CLMAGE LOSS
## 2.996e-04 -1.057e-02
##
## Degrees of Freedom: 1095 Total (i.e. Null); 1089 Residual
## Null Deviance: 273.2
## Residual Deviance: 254.8 AIC: 1527summary(model)##
## Call:
## glm(formula = ATTORNEY ~ ., data = a)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5789 -0.4746 -0.2108 0.4687 1.2622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.299e-01 5.490e-02 6.008 2.55e-09 ***
## CASENUM -5.204e-08 1.556e-06 -0.033 0.97332
## CLMSEX 8.628e-02 2.955e-02 2.920 0.00358 **
## CLMINSUR 1.426e-01 4.988e-02 2.860 0.00432 **
## SEATBELT -1.181e-01 1.102e-01 -1.071 0.28423
## CLMAGE 2.996e-04 7.198e-04 0.416 0.67736
## LOSS -1.057e-02 1.413e-03 -7.477 1.57e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2339434)
##
## Null deviance: 273.18 on 1095 degrees of freedom
## Residual deviance: 254.76 on 1089 degrees of freedom
## AIC: 1527.2
##
## Number of Fisher Scoring iterations: 2m1<-glm(ATTORNEY ~ as.factor(CLMSEX) + CLMINSUR + as.factor(SEATBELT) + CLMAGE + LOSS, data = a )
summary(m1)##
## Call:
## glm(formula = ATTORNEY ~ as.factor(CLMSEX) + CLMINSUR + as.factor(SEATBELT) +
## CLMAGE + LOSS, data = a)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5788 -0.4742 -0.2109 0.4690 1.2620
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3293593 0.0527899 6.239 6.29e-10 ***
## as.factor(CLMSEX)1 0.0862542 0.0295279 2.921 0.00356 **
## CLMINSUR 0.1425363 0.0497813 2.863 0.00427 **
## as.factor(SEATBELT)1 -0.1180436 0.1101692 -1.071 0.28419
## CLMAGE 0.0002997 0.0007194 0.417 0.67706
## LOSS -0.0105640 0.0014112 -7.486 1.46e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2337291)
##
## Null deviance: 273.18 on 1095 degrees of freedom
## Residual deviance: 254.76 on 1090 degrees of freedom
## AIC: 1525.2
##
## Number of Fisher Scoring iterations: 2#apply tranformation
m2<-glm(ATTORNEY ~ as.factor(CLMSEX) + factor(CLMINSUR) + (SEATBELT) + sqrt(CLMAGE) + sqrt(LOSS), data = a )
summary(m2)##
## Call:
## glm(formula = ATTORNEY ~ as.factor(CLMSEX) + factor(CLMINSUR) +
## (SEATBELT) + sqrt(CLMAGE) + sqrt(LOSS), data = a)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73767 -0.43838 0.06143 0.42142 1.12185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.463653 0.057077 8.123 1.22e-15 ***
## as.factor(CLMSEX)1 0.081023 0.028116 2.882 0.00403 **
## factor(CLMINSUR)1 0.133313 0.047359 2.815 0.00497 **
## SEATBELT -0.059923 0.104769 -0.572 0.56747
## sqrt(CLMAGE) 0.010307 0.006532 1.578 0.11490
## sqrt(LOSS) -0.141906 0.010771 -13.175 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.2119388)
##
## Null deviance: 273.18 on 1095 degrees of freedom
## Residual deviance: 231.01 on 1090 degrees of freedom
## AIC: 1417.9
##
## Number of Fisher Scoring iterations: 2library(mvinfluence)## Loading required package: car## Loading required package: carData## Loading required package: heplotsinfluencePlot(m1)
## StudRes Hat CookD
## 364 3.019924 0.2472475 0.49556111
## 808 1.529031 0.1016915 0.04405627
## 1066 1.248779 0.1093471 0.03189303pred <- predict(m2,data=a)
pred## 1 2 3 4 5 6
## -0.16896359 0.45445351 0.53849381 0.56713185 0.62575518 0.66008097
## 7 8 9 11 12 13
## 0.36096675 0.42457297 0.62515803 0.38762291 0.03455491 0.44041321
## 14 15 16 17 18 19
## 0.62675845 0.68701907 0.54292616 0.72475020 0.55191073 0.61481804
## 20 22 23 25 26 27
## 0.43211538 0.69118846 -0.04328649 0.22306048 0.56995139 0.62250313
## 28 29 31 33 34 35
## 0.50665807 0.57184227 0.43561591 0.51769168 0.55541561 0.42965356
## 36 37 38 39 40 41
## 0.45356467 0.35741598 0.47790539 0.56800836 0.42871138 0.52214062
## 42 43 44 45 47 48
## 0.20025964 0.34426880 0.24081036 0.52376725 0.31769223 0.60727314
## 50 51 52 55 56 58
## 0.41229920 0.44712669 0.53258268 0.57449495 -0.16955240 0.21011741
## 59 60 64 65 66 68
## 0.69344852 0.48857794 0.61419439 0.56697255 0.63657854 0.61328066
## 69 70 71 72 73 74
## 0.50816072 0.53985445 -0.26784706 0.49927344 0.53915232 0.57338779
## 75 76 77 79 80 81
## 0.50230338 0.35453056 0.52520484 0.46783778 0.39493243 0.53195707
## 82 84 85 86 87 88
## 0.58626135 0.39329231 0.53255698 0.56649238 0.38018839 0.68400342
## 89 90 92 93 94 95
## 0.42423891 0.42020046 0.37676014 0.57554210 0.40782595 0.49796832
## 96 98 99 100 101 102
## 0.72040989 -0.22709415 0.21967775 0.53164074 0.50264007 0.64177821
## 103 105 106 107 108 109
## 0.56897193 0.51532733 0.36402470 -0.25967609 0.29434388 0.62591244
## 111 112 113 115 118 119
## 0.53716849 0.64633142 0.27875914 0.61966363 0.47492219 0.49851828
## 121 122 123 124 125 126
## 0.63221107 0.36677068 0.71616030 0.50507443 0.43630230 0.46666391
## 128 129 131 132 135 136
## 0.26084277 0.55005549 0.66604392 0.71728624 0.43568596 0.56713601
## 138 139 140 141 142 144
## 0.39478323 0.65469034 0.48614054 0.59041012 0.46893938 0.20104213
## 145 146 147 148 149 150
## 0.44043505 0.72736588 0.48947109 -0.37679101 -0.35140992 0.61613105
## 151 153 154 155 156 157
## 0.32364922 0.66437234 0.57965445 0.56566635 0.39499870 -0.18127896
## 158 160 161 162 164 166
## -0.18748937 0.50552537 0.34854024 0.54265928 0.43476371 0.58277599
## 167 168 169 170 171 172
## 0.55002226 0.55594364 0.34085723 0.69321140 0.52883190 0.43178960
## 173 174 175 176 177 178
## 0.40412894 0.54009837 0.47891856 0.33274065 0.58998026 0.59294910
## 179 180 181 182 183 186
## 0.41066908 0.19043136 0.30958608 0.62001271 0.50283670 0.56764517
## 188 190 191 192 193 194
## 0.59883638 0.54110232 0.53733470 0.30148219 0.70323495 0.46746008
## 195 196 197 198 199 201
## 0.62572933 0.53462085 0.38130552 0.69683110 0.58049192 0.68750810
## 202 203 204 205 206 208
## 0.65408164 0.47242932 0.38441123 0.48830091 0.56333964 0.53643310
## 209 210 211 212 213 214
## 0.17632995 0.19951802 0.63948448 0.57700848 0.69860227 0.46837329
## 215 216 217 218 219 220
## 0.66095212 0.63055306 0.44757591 0.37710564 0.64960798 0.58899492
## 221 222 224 225 226 227
## 0.50464004 0.63878416 0.46118418 0.50868740 0.45994166 0.53171035
## 228 229 230 231 232 233
## 0.51071018 0.43447631 0.45334058 0.61415227 0.51661438 0.45109860
## 234 236 237 238 239 240
## 0.34853745 0.53992279 0.56633652 0.45610350 0.57959811 0.68835170
## 241 242 243 244 245 246
## 0.54177835 0.37948135 -0.23453288 0.41080400 0.65752770 0.47021143
## 247 248 250 251 252 253
## 0.57625575 0.60491490 0.65519429 0.64879362 0.38608292 0.58589194
## 254 256 258 259 260 261
## 0.30265049 0.36960543 0.38824391 0.47747858 0.31874413 0.60154770
## 262 263 264 265 266 267
## 0.60467141 0.37916226 0.61072945 0.55325694 0.53572722 0.46056284
## 269 270 271 272 273 274
## 0.71259809 0.35059669 0.66715567 0.40897204 0.51331244 0.46312009
## 276 277 279 280 281 282
## 0.57570717 0.45623116 0.59890229 0.61266457 0.61640179 0.51641138
## 284 286 287 288 289 290
## 0.38951354 0.31792924 0.44910895 -0.45208252 0.50280860 0.35112794
## 291 292 293 294 296 297
## 0.72118291 0.46114111 0.45016025 0.45162684 0.38947180 0.49662379
## 298 300 301 303 304 305
## 0.64886754 0.57583939 0.53448577 0.40478492 0.66226580 0.58841325
## 306 307 308 309 310 311
## 0.52260026 0.41517272 0.57443636 0.68486835 0.23078204 0.54039439
## 312 313 314 315 316 317
## 0.55138551 0.43601112 0.39403472 0.51128720 0.67027525 0.58693233
## 318 320 321 322 323 324
## 0.67485371 0.50970296 0.65210896 0.52871543 0.59264412 0.32192063
## 326 327 328 329 330 331
## 0.73785720 0.64410958 0.45077214 0.49211561 0.38437205 0.40061477
## 332 334 335 337 338 339
## 0.50449859 -0.38887876 0.28347983 0.37747072 0.61186756 0.50542312
## 340 341 342 343 344 345
## 0.42169740 0.65013852 0.43663262 0.51640580 0.58754046 0.53756108
## 346 347 348 349 350 351
## 0.33888323 0.49588970 0.50895909 0.52458559 0.44215130 0.32626997
## 352 353 355 356 357 358
## 0.36312835 0.59719679 0.33234789 0.30868101 0.44517945 0.30925312
## 359 360 361 362 364 365
## 0.62774133 0.47411733 0.58698686 0.49254516 -1.12184827 0.61683194
## 366 368 369 370 371 372
## 0.49349138 0.55020897 0.49140383 0.57041840 0.67613255 0.46392389
## 373 375 376 377 378 379
## 0.61162282 0.32690582 0.41219418 0.60161517 0.42784342 0.61798360
## 380 381 382 383 384 385
## 0.37862345 0.56601694 0.48809415 0.32504476 0.58181230 0.48721489
## 387 388 390 391 392 393
## 0.60736508 0.64413862 0.41271769 0.57488724 0.58523510 0.60339510
## 394 397 401 403 404 406
## 0.66929243 0.56995928 0.39483277 0.08698648 -0.37386948 0.32400469
## 407 409 411 412 413 414
## 0.40905067 0.51732095 -0.24636776 0.61357959 0.30720063 0.48518087
## 415 416 417 418 419 420
## 0.32089496 -0.50652169 0.54206701 0.35674006 0.64787264 0.26286945
## 421 422 423 424 425 426
## 0.51140651 0.56130752 0.53291287 0.61314233 0.39929092 0.56132376
## 427 430 431 433 434 436
## 0.62253085 0.67688064 0.36280389 0.36214965 -0.29248062 0.37340144
## 437 438 439 440 443 444
## 0.43889303 0.45936106 0.42360416 0.39493243 0.61180477 0.59934969
## 445 447 448 449 450 451
## 0.46218470 0.53539471 0.48778778 0.41557254 0.44088138 0.41341801
## 452 454 457 458 461 462
## 0.46202429 0.55948434 0.23499260 0.70106343 0.56386103 0.63785209
## 463 464 465 467 469 470
## 0.49157386 0.48764780 0.55307496 0.72076452 0.42636446 0.51716343
## 471 473 474 475 476 477
## 0.29473221 0.46687769 0.28926162 0.11698095 0.67641558 -0.50734159
## 478 479 480 481 482 483
## 0.60398437 0.40672161 0.65838746 0.62505275 0.65745957 0.59884233
## 486 487 488 490 493 494
## 0.57930566 0.61229430 0.41676318 0.46960325 0.63790582 0.46865983
## 495 497 498 499 500 501
## 0.45634813 0.52690341 0.61494918 0.65096597 0.37951760 0.30199785
## 502 503 504 505 507 508
## 0.38389350 0.26837715 0.52954792 0.42784910 0.42185382 0.61266457
## 509 511 512 513 514 515
## 0.43010234 0.50307005 0.45785369 0.52450236 0.72539319 0.53151462
## 516 517 518 519 520 521
## 0.44516054 0.70908002 0.38636501 0.31948750 0.62284629 0.67704993
## 522 525 526 527 529 530
## 0.57576420 0.58383359 0.59275492 0.32536134 0.45507993 0.47686004
## 532 533 534 535 536 537
## 0.65727381 0.56353472 0.52779723 0.70926984 0.56554944 0.37891911
## 538 540 541 542 543 544
## 0.46212633 0.22451305 0.40713261 0.37176408 0.42789558 0.39362744
## 545 546 549 550 552 553
## 0.39563239 0.47496862 0.21130294 0.62227407 0.37559666 0.33160680
## 554 556 557 558 559 561
## 0.45454794 0.32680291 0.54120924 0.54675190 0.35759428 0.30646771
## 562 564 565 566 567 568
## 0.43722545 0.50387514 0.64033855 0.26115646 0.74235346 0.19843000
## 569 570 571 572 573 574
## 0.52543555 0.43298704 0.59538501 0.48806235 0.50489799 0.44335359
## 575 576 577 578 579 581
## 0.39195180 0.41001399 0.55118846 0.41507698 0.49906776 0.35818398
## 582 583 585 586 587 588
## 0.56548174 0.66416259 0.69892377 0.27263645 -0.19295526 0.48832267
## 590 591 592 593 594 595
## 0.60515771 0.65836027 0.60731505 0.42494172 0.42593458 0.73370091
## 596 597 598 599 600 601
## 0.32004214 0.71758468 0.63845969 0.60366836 -0.38888644 0.53262802
## 603 604 605 608 609 610
## 0.66018990 0.46114703 0.44292406 0.58375466 0.54200659 0.37610931
## 611 613 614 616 617 618
## 0.54662006 0.62883891 0.32279048 0.58745797 0.47801154 0.17636972
## 619 620 621 622 623 624
## 0.60338670 0.29974299 0.49643926 0.38434783 0.47339912 0.47469847
## 626 627 628 629 630 632
## 0.34607807 0.28095416 0.38594734 0.71521940 0.54271623 0.52234076
## 633 634 635 636 637 638
## 0.42647981 0.54761595 0.24795981 0.65728508 0.61862560 0.38509013
## 639 640 641 642 643 644
## 0.59848241 0.57741905 0.48332699 0.38586265 0.28315414 0.68184750
## 646 647 648 649 650 651
## 0.31842721 0.39553936 0.72946003 0.36119144 0.51336834 0.58072492
## 652 653 654 655 657 658
## 0.56721253 0.62965771 0.29332940 0.57424249 0.39782253 0.48016735
## 659 660 662 663 664 665
## 0.33520487 0.54231625 0.31449717 0.66795493 0.64830438 0.39913031
## 666 669 670 672 673 674
## 0.36679434 0.49306667 0.72192349 0.72984559 0.60388232 0.36818627
## 675 676 678 679 680 681
## 0.40539612 0.61299292 0.36654806 0.40384368 0.41814202 0.61738726
## 682 684 685 687 688 689
## 0.51836309 0.57882911 0.25984079 0.58994716 0.66656362 0.43775255
## 690 691 692 693 695 696
## 0.19264964 0.54036806 0.34799626 0.61284583 0.55817910 0.30094453
## 697 699 700 701 702 703
## 0.43627496 0.63192595 0.33209303 0.61697914 0.58759341 0.47504959
## 704 705 706 708 709 710
## 0.64625803 0.33302021 0.56505268 0.61250348 0.55003001 0.53153124
## 711 712 713 715 716 717
## 0.28585581 0.39653290 0.58958941 0.67205019 0.25924247 0.58527006
## 718 719 720 721 722 724
## 0.39225635 0.12084875 0.61178725 0.44616072 0.23835608 0.39142997
## 725 726 727 728 729 730
## 0.65029770 0.54137751 0.62613652 0.34667025 0.37803459 0.53080667
## 731 732 734 735 736 737
## 0.56523543 0.36910919 0.42011090 0.68320841 0.58017605 0.23436475
## 738 739 740 741 742 743
## 0.65569164 0.26681476 0.33571646 0.73767159 0.41681138 0.61727901
## 744 745 746 747 749 750
## 0.37657178 0.42298704 0.20030475 0.58426357 0.41454780 0.64534337
## 751 753 754 756 757 758
## 0.63658424 0.45519061 0.58012115 0.57016962 0.50559999 0.38686068
## 759 760 761 762 763 764
## 0.51515629 0.07340413 0.49349138 0.50721726 0.27481092 0.67680062
## 765 766 767 768 770 772
## 0.40409116 0.55411754 0.56078749 0.39711867 0.60348410 0.68775666
## 775 776 778 779 781 782
## 0.65638441 0.44712669 0.67279946 0.68565440 0.45960951 0.57368972
## 783 786 787 788 791 793
## 0.59188997 0.49251544 0.44418516 -0.34501757 0.23941518 0.61023850
## 794 795 796 797 798 799
## 0.51890309 0.47688392 0.26052288 0.44778601 0.37213604 0.54201631
## 800 801 802 803 804 805
## 0.72048411 0.43371611 0.66050535 0.43873101 0.34693795 0.43120845
## 807 808 810 812 813 814
## 0.49121396 -0.85960664 0.56368666 0.62271091 0.66245285 -0.63331043
## 815 816 817 818 819 820
## 0.49918811 0.35216566 0.39653033 0.57975195 0.35766000 0.36865841
## 821 822 823 826 827 829
## 0.58314080 0.60929527 0.38735760 0.46719053 0.56638765 0.27885536
## 830 831 832 833 834 835
## 0.60410355 0.38884117 0.42168056 0.45865633 -0.07958255 0.60398437
## 836 838 839 841 842 843
## 0.65671517 0.64742693 0.30204754 0.57046035 0.47601613 0.59965625
## 845 847 848 849 852 853
## 0.37219249 0.43615460 0.36901495 0.35676938 0.64646261 0.49497744
## 854 856 857 858 859 861
## 0.52173238 0.41094302 0.36872649 0.41029233 0.51709365 0.43237053
## 863 864 865 866 867 868
## 0.49208881 0.65718910 0.61209548 0.59688698 0.65671517 0.59528953
## 870 871 872 874 876 878
## 0.39395729 0.57624984 0.45624145 0.47392538 0.17751252 0.25641045
## 879 880 883 885 886 887
## 0.55439476 0.53048033 0.56966211 0.30022783 0.42430885 0.21179537
## 888 889 890 891 892 893
## 0.65567483 0.68167048 0.66060933 0.32875475 0.40584098 0.59553573
## 894 896 897 898 899 900
## 0.52367451 0.44780560 0.36375923 0.17221418 0.45864338 0.34195098
## 901 902 904 905 908 909
## 0.45777990 0.61860026 0.40236526 0.61155860 0.24001048 0.61348560
## 911 912 913 914 915 916
## 0.43529082 0.25515272 0.35603502 0.57016407 0.48256419 0.46031826
## 917 918 919 920 922 924
## 0.55994950 0.39292426 0.49677633 0.67150376 0.46279346 0.60208511
## 925 926 927 928 929 930
## 0.58560793 0.52818188 0.61789606 0.70421969 0.40696806 0.36842957
## 931 932 934 935 936 937
## 0.57571549 0.67631014 0.53566088 0.66544820 0.62027278 0.64788910
## 939 940 941 943 945 946
## 0.67136395 0.56238763 0.67199926 0.66226580 0.35539195 0.38038172
## 948 949 951 952 953 954
## 0.33974153 0.44699687 0.55016098 0.69118314 0.59508982 0.61932385
## 955 956 958 959 960 961
## 0.51648912 0.55876883 0.55722363 0.38625033 0.66334344 0.39716457
## 962 963 964 966 969 970
## -0.11000022 0.66317467 0.59258452 0.36100919 0.46512438 0.16769146
## 971 972 973 974 975 976
## 0.53080667 0.25604871 0.45555511 0.51886707 0.63399728 0.43145260
## 977 978 979 980 981 982
## 0.30893297 -0.66744502 0.39986184 0.30708196 0.57976842 0.56239846
## 984 986 987 988 989 990
## 0.32294325 0.58360564 0.27259634 0.45881296 0.61670437 0.35709935
## 991 993 994 995 996 997
## 0.33209303 0.52754337 0.62171650 0.55406462 0.36635977 0.31662816
## 1000 1001 1003 1004 1005 1006
## 0.56975182 0.67323247 0.52932578 0.49156035 0.47574131 0.41891738
## 1007 1008 1009 1012 1013 1014
## 0.64532265 0.51966989 0.47884048 0.71966469 0.75163411 0.54741489
## 1015 1016 1017 1018 1019 1020
## 0.24399011 0.38003401 0.36796079 0.43187066 0.57126277 0.46745881
## 1021 1022 1023 1025 1026 1027
## 0.44546283 0.50208181 0.53005943 0.67582154 0.47963008 0.64424424
## 1028 1029 1030 1031 1032 1033
## 0.67274791 0.57255978 0.43381357 0.25890747 0.24646072 0.60275489
## 1034 1035 1036 1037 1038 1039
## 0.59814027 0.64322944 0.51401187 0.58960457 0.53924995 0.69355931
## 1040 1041 1042 1044 1045 1046
## 0.70323495 0.55136897 0.59644877 0.38005771 0.61703366 0.60346463
## 1047 1048 1049 1051 1052 1053
## 0.66422749 0.44766825 0.59228301 0.39558635 0.48668531 0.42712077
## 1054 1055 1057 1058 1059 1061
## 0.31944262 0.58057008 0.62537388 0.58887180 0.52298267 0.45863252
## 1062 1063 1064 1065 1066 1067
## 0.65033172 0.57409634 0.68462627 0.53427092 -0.73245824 0.64835915
## 1068 1069 1070 1071 1072 1073
## 0.27197707 0.60773032 0.61603597 0.51962781 0.49247503 0.49018111
## 1074 1075 1076 1077 1078 1079
## 0.44574499 0.47199502 0.51071018 0.62351697 0.30783590 0.43950529
## 1080 1081 1082 1083 1084 1085
## 0.64716879 0.64048014 0.62652245 0.54625305 0.55323434 0.38340402
## 1086 1089 1090 1091 1092 1093
## 0.17483766 0.61236893 0.64715990 0.38346399 0.18469172 0.37964416
## 1094 1095 1096 1097 1098 1099
## 0.36647713 0.57024285 0.62748747 0.42019473 0.45812088 0.40311995
## 1100 1101 1102 1103 1104 1105
## 0.58496578 0.46894757 0.56603072 0.39504595 0.36856157 0.46372459
## 1106 1107 1108 1109 1110 1111
## 0.24566622 0.36403006 0.39372874 0.54997342 0.73313913 0.48240934
## 1112 1114 1115 1116 1117 1118
## 0.53778115 0.31835360 0.29803559 0.53856965 0.63674680 0.37391467
## 1119 1120 1123 1124 1125 1126
## 0.46218638 0.68009802 0.31831748 0.29078613 0.62664679 0.43602894
## 1128 1129 1130 1131 1132 1134
## 0.66847518 0.61300598 0.58468404 0.39877903 0.36010529 0.42798708
## 1135 1136 1137 1138 1139 1140
## 0.47986889 0.47502113 0.66986538 0.54506900 0.39816881 0.31044330
## 1141 1143 1144 1145 1146 1147
## 0.59253421 0.36200481 0.43826919 0.45638522 0.58296232 0.64684870
## 1148 1149 1151 1152 1154 1155
## -0.36665349 0.43104446 0.31582166 0.46649345 0.39799831 0.29338613
## 1156 1157 1158 1159 1160 1161
## 0.63541736 0.46144677 0.61460349 0.39425832 0.43661745 0.64516679
## 1163 1165 1166 1168 1169 1171
## 0.14528253 0.62533229 0.63185680 0.60442184 0.41712591 0.65210222
## 1172 1173 1174 1175 1177 1178
## 0.35946212 0.51156113 0.75400408 0.44212583 0.12447502 0.34129026
## 1179 1180 1182 1184 1185 1186
## 0.53565123 0.21373346 0.21940652 0.51808413 0.37610618 0.62158465
## 1188 1189 1190 1191 1192 1193
## 0.52640956 0.23470499 0.46040083 0.40103826 0.57419380 0.64252589
## 1195 1196 1197 1198 1199 1201
## 0.59542931 0.46091456 0.44357355 0.41179695 0.62044278 0.36102691
## 1203 1204 1205 1206 1207 1208
## 0.61847103 0.53357557 0.42733781 0.66271333 0.72908679 0.39327699
## 1209 1210 1211 1212 1213 1214
## 0.58528125 0.49477106 0.60555874 0.52134555 0.46717773 0.62035086
## 1215 1216 1217 1218 1219 1220
## 0.67049795 0.29503069 0.62795828 0.40244506 0.61350191 0.64582001
## 1221 1222 1223 1225 1226 1229
## 0.63872715 0.52792247 0.58549483 0.61796073 0.47291389 0.47611193
## 1231 1233 1234 1236 1237 1238
## 0.60447789 0.31609424 0.49846177 0.55403435 0.54089284 0.72048138
## 1239 1240 1241 1242 1243 1244
## 0.65364872 0.68976059 0.38529396 0.34260070 0.31811442 0.52703348
## 1246 1248 1249 1252 1253 1254
## 0.61770510 0.70714048 0.52687825 0.44038783 0.60026400 0.50431065
## 1255 1256 1257 1258 1259 1260
## 0.36478060 0.57265137 -0.19200417 0.52872478 0.36685532 0.36706794
## 1261 1262 1263 1264 1265 1267
## 0.50283670 0.55388602 0.58078682 0.60506335 0.55776299 0.67833948
## 1269 1271 1272 1273 1274 1275
## 0.48989788 0.61016999 0.38790083 0.68312605 0.59088508 0.60026400
## 1276 1278 1279 1280 1281 1282
## 0.63917984 0.23667786 0.34826045 0.43400006 0.56203866 0.37916781
## 1283 1284 1285 1286 1287 1288
## 0.64125771 0.23112405 0.52729166 0.71144223 -0.37273377 0.46786409
## 1289 1290 1292 1293 1294 1296
## 0.54543560 0.40817188 0.54698500 0.49023026 0.53933841 0.46866114
## 1297 1299 1300 1301 1303 1307
## 0.35952164 0.55201424 0.33707041 0.50459294 0.62329338 0.73148863
## 1308 1309 1310 1311 1312 1313
## 0.69102090 0.41477697 0.40624020 0.60817596 0.33955216 0.19965649
## 1314 1315 1316 1318 1320 1323
## 0.56434839 0.57781640 0.47694755 0.35742886 0.53310861 0.64815861
## 1324 1325 1326 1327 1328 1329
## 0.37732170 0.26599077 0.45629189 0.58755256 0.66337652 0.54578076
## 1330 1331 1332 1333 1334 1335
## 0.63340029 0.28566480 0.55752761 0.70890880 0.66911230 0.68445555
## 1337 1338 1339 1340
## 0.47474513 0.69770372 0.32089152 0.61673522final <-as.data.frame(pred,a)## Warning in as.data.frame.numeric(pred, a): 'row.names' is not a character
## vector of length 1096 -- omitting it. Will be an error!p<-cbind(final,a)
View(p)#confusion matrix
confusion <- table(pred >0.5,a$ATTORNEY)
confusion##
## 0 1
## FALSE 401 139
## TRUE 177 379acc <- sum(diag(confusion))/sum(confusion)
acc## [1] 0.7116788#do transformation accordingly to increase the accuracy