Predict the Claimants appointed Attorney
claimants <- read.csv("/home/shrikanth/data/Data Science/data science/datasetsandcodesandassignments/claimants.csv")
attach(claimants)
fit1<-glm(ATTORNEY~CLMSEX+CLMINSUR+SEATBELT+CLMAGE+LOSS,data = claimants,family = "binomial")
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(fit1)
##
## Call:
## glm(formula = ATTORNEY ~ CLMSEX + CLMINSUR + SEATBELT + CLMAGE +
## LOSS, family = "binomial", data = claimants)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.74474 -1.01055 -0.02547 0.95764 2.78320
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.199978 0.246769 -0.810 0.41772
## CLMSEX 0.432996 0.135706 3.191 0.00142 **
## CLMINSUR 0.602173 0.231030 2.606 0.00915 **
## SEATBELT -0.781079 0.566125 -1.380 0.16768
## CLMAGE 0.006487 0.003324 1.952 0.05097 .
## LOSS -0.385044 0.034845 -11.050 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1516.1 on 1095 degrees of freedom
## Residual deviance: 1287.8 on 1090 degrees of freedom
## (244 observations deleted due to missingness)
## AIC: 1299.8
##
## Number of Fisher Scoring iterations: 6
# Linear regression technique can not be employed
prob1 <- predict(fit1,type="response")
# Logistic Regression
logit<-glm(ATTORNEY~factor(CLMSEX)+factor(CLMINSUR)+factor(SEATBELT)+CLMAGE+LOSS,family=binomial,data = claimants)
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(logit)
##
## Call:
## glm(formula = ATTORNEY ~ factor(CLMSEX) + factor(CLMINSUR) +
## factor(SEATBELT) + CLMAGE + LOSS, family = binomial, data = claimants)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.74474 -1.01055 -0.02547 0.95764 2.78320
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.199978 0.246769 -0.810 0.41772
## factor(CLMSEX)1 0.432996 0.135706 3.191 0.00142 **
## factor(CLMINSUR)1 0.602173 0.231030 2.606 0.00915 **
## factor(SEATBELT)1 -0.781079 0.566125 -1.380 0.16768
## CLMAGE 0.006487 0.003324 1.952 0.05097 .
## LOSS -0.385044 0.034845 -11.050 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1516.1 on 1095 degrees of freedom
## Residual deviance: 1287.8 on 1090 degrees of freedom
## (244 observations deleted due to missingness)
## AIC: 1299.8
##
## Number of Fisher Scoring iterations: 6
# Odds Ratio
exp(coef(logit))
## (Intercept) factor(CLMSEX)1 factor(CLMINSUR)1 factor(SEATBELT)1
## 0.8187490 1.5418701 1.8260829 0.4579119
## CLMAGE LOSS
## 1.0065085 0.6804208
# Confusion matrix table
prob <- predict(logit,type=c("response"),claimants)
prob
## 1 2 3 4 5
## 2.970231e-06 5.016792e-01 5.762915e-01 4.521417e-01 6.415700e-01
## 6 7 8 9 10
## 7.197599e-01 2.887006e-01 3.049922e-01 7.084578e-01 NA
## 11 12 13 14 15
## 2.127887e-01 1.024493e-03 4.505382e-01 6.975276e-01 7.350044e-01
## 16 17 18 19 20
## 5.673377e-01 7.671356e-01 6.193051e-01 6.038798e-01 3.210857e-01
## 21 22 23 24 25
## NA 7.384916e-01 2.074101e-03 NA 4.256088e-02
## 26 27 28 29 30
## 6.345123e-01 6.770328e-01 5.612539e-01 6.238091e-01 NA
## 31 32 33 34 35
## 3.546010e-01 NA 5.568532e-01 6.030216e-01 3.171081e-01
## 36 37 38 39 40
## 3.972807e-01 3.257734e-01 4.341099e-01 6.148278e-01 4.260576e-01
## 41 42 43 44 45
## 5.682431e-01 6.489410e-02 1.211703e-01 2.079400e-02 5.640246e-01
## 46 47 48 49 50
## NA 3.173112e-01 6.007720e-01 NA 3.700633e-01
## 51 52 53 54 55
## 3.785143e-01 5.931725e-01 NA NA 6.414805e-01
## 56 57 58 59 60
## 4.315197e-06 NA 9.282735e-02 7.440479e-01 5.217440e-01
## 61 62 63 64 65
## NA NA NA 6.653118e-01 6.222573e-01
## 66 67 68 69 70
## 7.197762e-01 NA 6.378427e-01 5.557522e-01 5.775275e-01
## 71 72 73 74 75
## 9.217805e-08 5.485540e-01 6.021393e-01 6.311836e-01 5.574578e-01
## 76 77 78 79 80
## 2.863820e-01 5.741273e-01 NA 4.849379e-01 3.261919e-01
## 81 82 83 84 85
## 5.660798e-01 6.553435e-01 NA 4.124017e-01 5.843275e-01
## 86 87 88 89 90
## 6.231840e-01 3.146153e-01 7.319410e-01 3.365576e-01 3.163482e-01
## 91 92 93 94 95
## NA 2.890883e-01 6.355156e-01 3.638348e-01 5.403384e-01
## 96 97 98 99 100
## 7.786921e-01 NA 2.491388e-06 5.503815e-02 5.703947e-01
## 101 102 103 104 105
## 5.418366e-01 7.126215e-01 6.247389e-01 NA 5.549808e-01
## 106 107 108 109 110
## 2.600384e-01 3.009099e-07 2.257167e-01 6.915123e-01 NA
## 111 112 113 114 115
## 6.095755e-01 7.091804e-01 1.674699e-01 NA 6.771650e-01
## 116 117 118 119 120
## NA NA 4.297040e-01 4.867733e-01 NA
## 121 122 123 124 125
## 6.862460e-01 2.067411e-01 7.529408e-01 5.285603e-01 3.385482e-01
## 126 127 128 129 130
## 4.077497e-01 NA 8.532928e-02 6.141726e-01 NA
## 131 132 133 134 135
## 7.337525e-01 7.573118e-01 NA NA 3.360392e-01
## 136 137 138 139 140
## 5.933615e-01 NA 3.173771e-01 7.359304e-01 5.198513e-01
## 141 142 143 144 145
## 6.521264e-01 4.166037e-01 NA 5.268041e-02 4.337953e-01
## 146 147 148 149 150
## 7.890390e-01 5.212168e-01 8.017384e-09 3.358798e-10 6.746935e-01
## 151 152 153 154 155
## 9.358757e-02 NA 6.995671e-01 6.536552e-01 6.208575e-01
## 156 157 158 159 160
## 2.173464e-01 4.254108e-06 4.300573e-07 NA 5.278776e-01
## 161 162 163 164 165
## 3.619466e-01 5.799767e-01 NA 3.492176e-01 NA
## 166 167 168 169 170
## 5.982900e-01 6.081530e-01 6.063797e-01 2.447841e-01 7.344381e-01
## 171 172 173 174 175
## 5.678596e-01 3.181171e-01 2.987517e-01 5.796319e-01 4.397334e-01
## 176 177 178 179 180
## 2.283771e-01 6.585542e-01 6.551036e-01 3.264942e-01 3.517341e-02
## 181 182 183 184 185
## 3.121801e-01 6.069793e-01 5.579327e-01 NA NA
## 186 187 188 189 190
## 6.237673e-01 NA 6.636755e-01 NA 6.250588e-01
## 191 192 193 194 195
## 5.773416e-01 1.692446e-01 7.056014e-01 4.073862e-01 6.924722e-01
## 196 197 198 199 200
## 5.788357e-01 3.059782e-01 7.509581e-01 6.387423e-01 NA
## 201 202 203 204 205
## 7.159956e-01 7.309554e-01 4.412030e-01 3.981250e-01 5.509766e-01
## 206 207 208 209 210
## 6.184571e-01 NA 5.895409e-01 6.596294e-03 1.357063e-01
## 211 212 213 214 215
## 7.148314e-01 6.362018e-01 7.028990e-01 4.135696e-01 7.398143e-01
## 216 217 218 219 220
## 7.253662e-01 3.959042e-01 2.322568e-01 6.941917e-01 6.490576e-01
## 221 222 223 224 225
## 4.307054e-01 6.624348e-01 NA 4.823127e-01 5.485197e-01
## 226 227 228 229 230
## 4.731368e-01 5.772676e-01 5.501258e-01 3.294901e-01 3.811610e-01
## 231 232 233 234 235
## 6.597814e-01 5.633471e-01 3.730704e-01 3.625153e-01 NA
## 236 237 238 239 240
## 5.798337e-01 6.273123e-01 5.038786e-01 6.379014e-01 7.047208e-01
## 241 242 243 244 245
## 5.911297e-01 1.939510e-01 3.992905e-07 3.685169e-01 6.966581e-01
## 246 247 248 249 250
## 4.182465e-01 6.342876e-01 6.655399e-01 NA 6.975155e-01
## 251 252 253 254 255
## 7.224607e-01 2.002377e-01 6.437778e-01 1.713118e-01 NA
## 256 257 258 259 260
## 2.802224e-01 NA 3.356860e-01 5.322550e-01 1.551240e-01
## 261 262 263 264 265
## 6.664404e-01 6.344561e-01 4.034021e-01 6.763788e-01 6.145928e-01
## 266 267 268 269 270
## 5.716228e-01 3.893014e-01 NA 7.600887e-01 3.756184e-01
## 271 272 273 274 275
## 6.996603e-01 3.139810e-01 5.587956e-01 4.349818e-01 NA
## 276 277 278 279 280
## 6.381386e-01 3.841709e-01 NA 6.610945e-01 6.568630e-01
## 281 282 283 284 285
## 7.044362e-01 5.626036e-01 NA 2.222366e-01 NA
## 286 287 288 289 290
## 1.204714e-01 4.611915e-01 8.942955e-12 5.420278e-01 1.864206e-01
## 291 292 293 294 295
## 7.373905e-01 4.156711e-01 4.688168e-01 4.056131e-01 NA
## 296 297 298 299 300
## 4.132191e-01 5.522272e-01 7.152642e-01 NA 6.382083e-01
## 301 302 303 304 305
## 5.728747e-01 NA 3.528997e-01 7.002705e-01 6.496634e-01
## 306 307 308 309 310
## 5.683519e-01 3.329485e-01 6.409417e-01 7.332120e-01 5.418572e-02
## 311 312 313 314 315
## 5.940986e-01 5.992393e-01 4.777018e-01 4.337984e-01 5.753788e-01
## 316 317 318 319 320
## 7.070342e-01 5.987526e-01 7.023919e-01 NA 5.524693e-01
## 321 322 323 324 325
## 7.242446e-01 5.677365e-01 6.591807e-01 2.170219e-01 NA
## 326 327 328 329 330
## 7.561498e-01 6.928003e-01 4.059985e-01 5.266183e-01 2.554087e-01
## 331 332 333 334 335
## 4.233010e-01 5.526855e-01 NA 1.333955e-07 1.429856e-01
## 336 337 338 339 340
## NA 2.888897e-01 6.789478e-01 5.443063e-01 3.303105e-01
## 341 342 343 344 345
## 6.942808e-01 3.306898e-01 5.630630e-01 6.473627e-01 5.877469e-01
## 346 347 348 349 350
## 3.481161e-01 5.766806e-01 5.099706e-01 5.551212e-01 3.500559e-01
## 351 352 353 354 355
## 1.803321e-01 1.508854e-01 6.392458e-01 NA 2.946686e-01
## 356 357 358 359 360
## 2.262594e-01 4.485978e-01 8.049501e-02 6.990566e-01 4.219957e-01
## 361 362 363 364 365
## 6.064503e-01 4.722487e-01 NA 2.220446e-16 6.750571e-01
## 366 367 368 369 370
## 4.763683e-01 NA 5.997555e-01 5.234818e-01 5.959743e-01
## 371 372 373 374 375
## 6.867461e-01 4.086772e-01 6.709770e-01 NA 9.490528e-02
## 376 377 378 379 380
## 2.689508e-01 6.776721e-01 3.108628e-01 7.050214e-01 2.974119e-01
## 381 382 383 384 385
## 6.239345e-01 4.585240e-01 9.852664e-02 6.452688e-01 5.231472e-01
## 386 387 388 389 390
## NA 6.750492e-01 7.230078e-01 NA 2.979163e-01
## 391 392 393 394 395
## 6.337974e-01 6.039942e-01 6.645237e-01 6.987131e-01 NA
## 396 397 398 399 400
## NA 6.317638e-01 NA NA NA
## 401 402 403 404 405
## 2.247696e-01 NA 2.394672e-03 6.945836e-09 NA
## 406 407 408 409 410
## 2.054721e-01 2.652393e-01 NA 5.407058e-01 NA
## 411 412 413 414 415
## 1.963797e-08 6.842905e-01 3.135422e-01 5.182926e-01 1.978388e-01
## 416 417 418 419 420
## 7.033078e-12 5.973162e-01 2.373470e-01 6.947338e-01 2.128909e-01
## 421 422 423 424 425
## 5.353233e-01 6.147311e-01 5.716637e-01 6.723948e-01 4.229705e-01
## 426 427 428 429 430
## 6.157276e-01 6.795759e-01 NA NA 7.119186e-01
## 431 432 433 434 435
## 3.818861e-01 NA 1.921571e-01 2.399063e-09 NA
## 436 437 438 439 440
## 2.845991e-01 3.865147e-01 3.904338e-01 3.495519e-01 3.261919e-01
## 441 442 443 444 445
## NA NA 6.823741e-01 6.606369e-01 4.305251e-01
## 446 447 448 449 450
## NA 4.682554e-01 4.650258e-01 3.743244e-01 3.629758e-01
## 451 452 453 454 455
## 4.387564e-01 5.118487e-01 NA 6.188122e-01 NA
## 456 457 458 459 460
## NA 9.889933e-02 7.506198e-01 NA NA
## 461 462 463 464 465
## 6.197420e-01 6.909123e-01 5.247564e-01 5.187065e-01 5.997191e-01
## 466 467 468 469 470
## NA 7.580894e-01 NA 3.845771e-01 5.572764e-01
## 471 472 473 474 475
## 2.948027e-01 NA 4.055143e-01 1.572880e-01 4.258804e-02
## 476 477 478 479 480
## 7.099644e-01 5.456396e-11 6.774963e-01 3.054460e-01 7.022260e-01
## 481 482 483 484 485
## 6.620511e-01 7.448579e-01 6.652618e-01 NA NA
## 486 487 488 489 490
## 6.537363e-01 6.823868e-01 3.792233e-01 NA 4.194423e-01
## 491 492 493 494 495
## NA NA 7.121003e-01 4.365051e-01 4.567356e-01
## 496 497 498 499 500
## NA 5.646807e-01 6.736324e-01 6.934183e-01 1.965194e-01
## 501 502 503 504 505
## 1.325556e-01 3.039156e-01 9.086265e-02 5.727719e-01 3.340824e-01
## 506 507 508 509 510
## NA 3.895130e-01 6.568630e-01 4.303142e-01 NA
## 511 512 513 514 515
## 5.391618e-01 4.252752e-01 5.646873e-01 7.732748e-01 5.726091e-01
## 516 517 518 519 520
## 4.507520e-01 7.685146e-01 3.130498e-01 2.864315e-01 6.850439e-01
## 521 522 523 524 525
## 7.526845e-01 6.408286e-01 NA NA 6.009930e-01
## 526 527 528 529 530
## 6.115430e-01 1.732818e-01 NA 3.740095e-01 5.001922e-01
## 531 532 533 534 535
## NA 7.294958e-01 5.919672e-01 4.593704e-01 7.522129e-01
## 536 537 538 539 540
## 6.294975e-01 2.284711e-01 4.862847e-01 NA 6.595373e-02
## 541 542 543 544 545
## 3.623211e-01 3.964857e-01 4.028766e-01 3.265280e-01 3.353529e-01
## 546 547 548 549 550
## 4.268556e-01 NA NA 1.325959e-01 7.079321e-01
## 551 552 553 554 555
## NA 3.975303e-01 2.115781e-01 5.073845e-01 NA
## 556 557 558 559 560
## 1.016882e-01 6.067924e-01 5.936963e-01 2.401781e-01 NA
## 561 562 563 564 565
## 2.421615e-01 3.684430e-01 NA 5.339734e-01 7.073819e-01
## 566 567 568 569 570
## 2.231764e-01 7.480424e-01 6.041702e-02 5.836612e-01 4.417189e-01
## 571 572 573 574 575
## 6.156096e-01 4.613238e-01 5.475140e-01 4.525117e-01 2.141580e-01
## 576 577 578 579 580
## 3.610844e-01 5.865281e-01 3.055541e-01 5.369433e-01 NA
## 581 582 583 584 585
## 1.913488e-01 6.103919e-01 7.024236e-01 NA 7.636955e-01
## 586 587 588 589 590
## 2.334474e-01 3.033343e-07 4.585558e-01 NA 6.704512e-01
## 591 592 593 594 595
## 6.975401e-01 6.673711e-01 3.026624e-01 4.225918e-01 7.524000e-01
## 596 597 598 599 600
## 1.774545e-01 7.769055e-01 6.820762e-01 6.641412e-01 7.440193e-11
## 601 602 603 604 605
## 5.906246e-01 NA 7.030305e-01 4.329154e-01 3.823453e-01
## 606 607 608 609 610
## NA NA 6.476983e-01 5.852684e-01 2.272046e-01
## 611 612 613 614 615
## 5.955104e-01 NA 6.422781e-01 1.984320e-01 NA
## 616 617 618 619 620
## 6.633991e-01 4.383397e-01 6.680468e-03 6.887605e-01 2.987158e-01
## 621 622 623 624 625
## 4.823787e-01 3.080087e-01 5.164925e-01 4.225535e-01 NA
## 626 627 628 629 630
## 1.133302e-01 9.568106e-02 2.066339e-01 7.547767e-01 5.794565e-01
## 631 632 633 634 635
## NA 5.300987e-01 3.848505e-01 5.896475e-01 4.825620e-02
## 636 637 638 639 640
## 7.286319e-01 6.830467e-01 3.070322e-01 6.326547e-01 6.023073e-01
## 641 642 643 644 645
## 5.135574e-01 3.084142e-01 1.166481e-01 7.077080e-01 NA
## 646 647 648 649 650
## 1.887468e-01 4.173698e-01 7.800924e-01 2.941312e-01 4.399378e-01
## 651 652 653 654 655
## 5.975354e-01 6.141894e-01 6.879443e-01 1.293214e-01 6.283652e-01
## 656 657 658 659 660
## NA 3.397309e-01 4.420775e-01 2.259028e-01 5.887241e-01
## 661 662 663 664 665
## NA 8.122428e-02 6.970451e-01 7.206275e-01 4.297107e-01
## 666 667 668 669 670
## 2.682409e-01 NA NA 4.750093e-01 7.684876e-01
## 671 672 673 674 675
## NA 7.650279e-01 6.099499e-01 1.681020e-01 2.581206e-01
## 676 677 678 679 680
## 6.831374e-01 NA 2.656335e-01 4.457652e-01 2.785795e-01
## 681 682 683 684 685
## 6.782023e-01 5.496716e-01 NA 6.079280e-01 1.514905e-01
## 686 687 688 689 690
## NA 6.754864e-01 7.300320e-01 4.376198e-01 7.258517e-02
## 691 692 693 694 695
## 5.777012e-01 1.565836e-01 6.889701e-01 NA 5.966342e-01
## 696 697 698 699 700
## 1.378802e-01 3.661166e-01 NA 6.798176e-01 1.038767e-01
## 701 702 703 704 705
## 6.884223e-01 6.440560e-01 4.304734e-01 6.933737e-01 1.387561e-01
## 706 707 708 709 710
## 5.931194e-01 NA 6.858059e-01 5.939262e-01 5.937805e-01
## 711 712 713 714 715
## 1.351025e-01 2.309650e-01 6.505259e-01 NA 7.455758e-01
## 716 717 718 719 720
## 9.968857e-02 6.466484e-01 3.404721e-01 1.526169e-03 6.788639e-01
## 721 722 723 724 725
## 3.790854e-01 6.125919e-02 NA 3.398802e-01 6.948402e-01
## 726 727 728 729 730
## 5.972095e-01 6.491565e-01 2.567184e-01 3.245249e-01 5.697999e-01
## 731 732 733 734 735
## 5.945828e-01 2.791364e-01 NA 4.392689e-01 7.148197e-01
## 736 737 738 739 740
## 5.979197e-01 7.114870e-02 7.568331e-01 2.178197e-01 2.444715e-01
## 741 742 743 744 745
## 7.817393e-01 2.892032e-01 6.753948e-01 1.861693e-01 2.961766e-01
## 746 747 748 749 750
## 4.684915e-02 6.603834e-01 NA 3.756728e-01 6.530053e-01
## 751 752 753 754 755
## 6.873153e-01 NA 3.802339e-01 6.077583e-01 NA
## 756 757 758 759 760
## 6.571230e-01 5.021222e-01 3.131904e-01 5.618167e-01 2.144142e-02
## 761 762 763 764 765
## 4.763683e-01 5.617280e-01 2.349415e-01 6.881400e-01 2.925535e-01
## 766 767 768 769 770
## 4.467254e-01 5.931898e-01 4.134228e-01 NA 6.729546e-01
## 771 772 773 774 775
## NA 7.408193e-01 NA NA 6.968085e-01
## 776 777 778 779 780
## 3.785143e-01 NA 7.019087e-01 7.353678e-01 NA
## 781 782 783 784 785
## 3.910755e-01 5.961175e-01 6.528857e-01 NA NA
## 786 787 788 789 790
## 5.252250e-01 3.712058e-01 5.791418e-09 NA NA
## 791 792 793 794 795
## 6.096194e-02 NA 6.698581e-01 5.589857e-01 4.387103e-01
## 796 797 798 799 800
## 2.089913e-01 3.988688e-01 1.614656e-01 5.904295e-01 7.202046e-01
## 801 802 803 804 805
## 4.725376e-01 6.996235e-01 3.721241e-01 3.679472e-01 3.128860e-01
## 806 807 808 809 810
## NA 4.676536e-01 2.220446e-16 NA 5.941186e-01
## 811 812 813 814 815
## NA 6.924921e-01 7.062795e-01 1.060724e-13 5.536486e-01
## 816 817 818 819 820
## 3.688157e-01 2.302032e-01 6.372440e-01 1.931517e-01 2.431866e-01
## 821 822 823 824 825
## 6.323535e-01 6.699509e-01 2.069113e-01 NA NA
## 826 827 828 829 830
## 4.301417e-01 6.295651e-01 NA 1.104863e-01 6.651243e-01
## 831 832 833 834 835
## 2.181554e-01 4.577488e-01 3.870070e-01 1.607516e-05 6.774963e-01
## 836 837 838 839 840
## 7.138792e-01 NA 7.177858e-01 3.007239e-01 NA
## 841 842 843 844 845
## 5.962608e-01 4.308739e-01 6.073188e-01 NA 2.757494e-01
## 846 847 848 849 850
## NA 3.415551e-01 3.034519e-01 2.524053e-01 NA
## 851 852 853 854 855
## NA 6.919305e-01 5.285844e-01 5.978518e-01 NA
## 856 857 858 859 860
## 3.039719e-01 1.646278e-01 3.655866e-01 5.559690e-01 NA
## 861 862 863 864 865
## 4.771862e-01 NA 5.253471e-01 7.023748e-01 6.714276e-01
## 866 867 868 869 870
## 6.575564e-01 7.138792e-01 6.602102e-01 NA 3.247216e-01
## 871 872 873 874 875
## 6.384885e-01 4.630591e-01 NA 4.276437e-01 NA
## 876 877 878 879 880
## 5.865779e-02 NA 7.820417e-02 5.908648e-01 5.579492e-01
## 881 882 883 884 885
## NA NA 6.301748e-01 NA 6.427515e-02
## 886 887 888 889 890
## 3.367295e-01 4.179308e-02 7.073340e-01 7.134167e-01 6.962314e-01
## 891 892 893 894 895
## 3.304944e-01 4.354637e-01 6.575354e-01 5.788447e-01 NA
## 896 897 898 899 900
## 4.533646e-01 2.820996e-01 4.442899e-03 3.784481e-01 2.516312e-01
## 901 902 903 904 905
## 5.062237e-01 6.764785e-01 NA 3.464681e-01 6.791029e-01
## 906 907 908 909 910
## NA NA 6.071327e-02 6.726286e-01 NA
## 911 912 913 914 915
## 4.781298e-01 9.333978e-02 2.478837e-01 6.172971e-01 5.134760e-01
## 916 917 918 919 920
## 5.091720e-01 6.262974e-01 3.249212e-01 5.668877e-01 7.015864e-01
## 921 922 923 924 925
## NA 4.319417e-01 NA 6.767003e-01 6.470882e-01
## 926 927 928 929 930
## 5.586741e-01 6.758296e-01 7.366216e-01 2.494023e-01 1.923927e-01
## 931 932 933 934 935
## 6.230606e-01 7.027138e-01 NA 5.905231e-01 7.269442e-01
## 936 937 938 939 940
## 6.778967e-01 7.109915e-01 NA 7.040429e-01 6.139923e-01
## 941 942 943 944 945
## 6.783122e-01 NA 7.002705e-01 NA 2.363663e-01
## 946 947 948 949 950
## 3.150306e-01 NA 1.073839e-01 4.679830e-01 NA
## 951 952 953 954 955
## 5.854212e-01 7.189379e-01 6.092954e-01 6.778584e-01 5.478745e-01
## 956 957 958 959 960
## 6.130210e-01 NA 6.063916e-01 3.122926e-01 7.248873e-01
## 961 962 963 964 965
## 2.732533e-01 2.696661e-06 7.295534e-01 6.547555e-01 NA
## 966 967 968 969 970
## 2.506742e-01 NA NA 4.838598e-01 2.431556e-02
## 971 972 973 974 975
## 5.697999e-01 6.753800e-02 3.805832e-01 5.565472e-01 6.978131e-01
## 976 977 978 979 980
## 3.186438e-01 1.870058e-01 2.220446e-16 2.333527e-01 1.860798e-01
## 981 982 983 984 985
## 6.416741e-01 5.915021e-01 NA 1.711361e-01 NA
## 986 987 988 989 990
## 6.317403e-01 1.152774e-01 4.797320e-01 6.870416e-01 3.125911e-01
## 991 992 993 994 995
## 1.038767e-01 NA 5.666166e-01 6.871546e-01 6.103164e-01
## 996 997 998 999 1000
## 2.837045e-01 1.608226e-01 NA NA 6.254636e-01
## 1001 1002 1003 1004 1005
## 7.106535e-01 NA 5.627953e-01 5.241278e-01 5.064957e-01
## 1006 1007 1008 1009 1010
## 4.572275e-01 7.225683e-01 5.602337e-01 5.116449e-01 NA
## 1011 1012 1013 1014 1015
## NA 7.629451e-01 7.917376e-01 5.895119e-01 8.972415e-02
## 1016 1017 1018 1019 1020
## 3.735916e-01 1.597668e-01 4.319053e-01 6.287596e-01 4.112370e-01
## 1021 1022 1023 1024 1025
## 3.570694e-01 5.398953e-01 5.779457e-01 NA 7.061924e-01
## 1026 1027 1028 1029 1030
## 4.428144e-01 7.131970e-01 7.225287e-01 6.308381e-01 3.321972e-01
## 1031 1032 1033 1034 1035
## 2.082420e-01 6.458059e-02 6.453977e-01 6.592412e-01 6.925544e-01
## 1036 1037 1038 1039 1040
## 5.527493e-01 6.711723e-01 6.159929e-01 7.174029e-01 7.056014e-01
## 1041 1042 1043 1044 1045
## 5.973378e-01 6.590581e-01 NA 3.287593e-01 6.924498e-01
## 1046 1047 1048 1049 1050
## 6.715510e-01 7.381624e-01 4.667224e-01 6.083784e-01 NA
## 1051 1052 1053 1054 1055
## 3.300555e-01 5.164398e-01 3.436435e-01 8.012265e-02 6.071295e-01
## 1056 1057 1058 1059 1060
## NA 6.645189e-01 6.771798e-01 5.483830e-01 NA
## 1061 1062 1063 1064 1065
## 3.798131e-01 7.221752e-01 6.338193e-01 7.132712e-01 5.721656e-01
## 1066 1067 1068 1069 1070
## 2.220446e-16 7.064444e-01 9.861610e-02 6.208667e-01 6.423263e-01
## 1071 1072 1073 1074 1075
## 5.654871e-01 5.349435e-01 4.706817e-01 4.770416e-01 4.920340e-01
## 1076 1077 1078 1079 1080
## 5.501258e-01 6.944317e-01 3.095405e-01 4.825072e-01 6.795331e-01
## 1081 1082 1083 1084 1085
## 7.081662e-01 7.210499e-01 5.951393e-01 5.993289e-01 2.284197e-01
## 1086 1087 1088 1089 1090
## 8.968314e-02 NA NA 6.729215e-01 7.140078e-01
## 1091 1092 1093 1094 1095
## 2.285554e-01 9.688832e-02 2.942303e-01 2.035922e-01 6.275685e-01
## 1096 1097 1098 1099 1100
## 6.629121e-01 3.857656e-01 3.779734e-01 3.484893e-01 6.443684e-01
## 1101 1102 1103 1104 1105
## 4.136206e-01 5.956623e-01 3.282443e-01 1.609592e-01 3.988126e-01
## 1106 1107 1108 1109 1110
## 6.736485e-02 2.639141e-01 3.249289e-01 6.140814e-01 7.489013e-01
## 1111 1112 1113 1114 1115
## 4.466927e-01 5.756190e-01 NA 2.702769e-01 1.272842e-01
## 1116 1117 1118 1119 1120
## 5.765878e-01 6.873980e-01 2.110195e-01 4.744166e-01 7.519279e-01
## 1121 1122 1123 1124 1125
## NA NA 1.610449e-01 1.391823e-01 7.010973e-01
## 1126 1127 1128 1129 1130
## 3.319358e-01 NA 7.007796e-01 6.955594e-01 6.055309e-01
## 1131 1132 1133 1134 1135
## 2.451761e-01 2.892664e-01 NA 3.413628e-01 5.058800e-01
## 1136 1137 1138 1139 1140
## 4.249526e-01 7.466367e-01 6.041725e-01 4.224723e-01 1.854127e-01
## 1141 1142 1143 1144 1145
## 6.558307e-01 NA 2.550941e-01 3.667152e-01 3.833238e-01
## 1146 1147 1148 1149 1150
## 6.074189e-01 7.187750e-01 1.870498e-07 3.642070e-01 NA
## 1151 1152 1153 1154 1155
## 3.261051e-01 4.032323e-01 NA 3.365112e-01 1.745487e-01
## 1156 1157 1158 1159 1160
## 6.900895e-01 4.727789e-01 6.709671e-01 4.191991e-01 3.810511e-01
## 1161 1162 1163 1164 1165
## 6.916718e-01 NA 2.572108e-02 NA 6.464598e-01
## 1166 1167 1168 1169 1170
## 6.979341e-01 NA 6.790672e-01 3.337912e-01 NA
## 1171 1172 1173 1174 1175
## 6.938274e-01 2.855481e-01 5.496579e-01 7.815621e-01 4.429852e-01
## 1176 1177 1178 1179 1180
## NA 6.623049e-03 2.142313e-01 5.752874e-01 1.428517e-02
## 1181 1182 1183 1184 1185
## NA 1.694745e-01 NA 5.703347e-01 3.183813e-01
## 1186 1187 1188 1189 1190
## 6.891702e-01 NA 5.767328e-01 2.013542e-02 4.810630e-01
## 1191 1192 1193 1194 1195
## 3.423293e-01 6.523065e-01 6.904387e-01 NA 6.570019e-01
## 1196 1197 1198 1199 1200
## 4.752634e-01 3.529390e-01 3.656350e-01 6.800736e-01 NA
## 1201 1202 1203 1204 1205
## 3.796165e-01 NA 6.767057e-01 5.843558e-01 3.586220e-01
## 1206 1207 1208 1209 1210
## 7.355720e-01 7.563520e-01 3.272571e-01 6.504305e-01 4.757366e-01
## 1211 1212 1213 1214 1215
## 6.873105e-01 5.550031e-01 4.059645e-01 6.775985e-01 7.021382e-01
## 1216 1217 1218 1219 1220
## 8.161442e-02 6.834204e-01 2.679061e-01 6.842073e-01 7.067488e-01
## 1221 1222 1223 1224 1225
## 7.128102e-01 5.678159e-01 6.531551e-01 NA 6.851268e-01
## 1226 1227 1228 1229 1230
## 4.233511e-01 NA NA 5.012101e-01 NA
## 1231 1232 1233 1234 1235
## 6.736450e-01 NA 2.353522e-01 4.846309e-01 NA
## 1236 1237 1238 1239 1240
## 6.321387e-01 5.825801e-01 7.777718e-01 7.193902e-01 7.070946e-01
## 1241 1242 1243 1244 1245
## 3.147964e-01 2.987883e-01 1.645096e-01 5.636249e-01 NA
## 1246 1247 1248 1249 1250
## 6.625287e-01 NA 7.082894e-01 5.831565e-01 NA
## 1251 1252 1253 1254 1255
## NA 4.389842e-01 6.725362e-01 5.585969e-01 2.649512e-01
## 1256 1257 1258 1259 1260
## 6.314389e-01 6.255736e-06 5.678740e-01 1.692227e-01 2.688460e-01
## 1261 1262 1263 1264 1265
## 5.579327e-01 6.004379e-01 5.980123e-01 6.743219e-01 6.083838e-01
## 1266 1267 1268 1269 1270
## NA 7.312376e-01 NA 5.268276e-01 NA
## 1271 1272 1273 1274 1275
## 6.698968e-01 3.150859e-01 7.306663e-01 6.512815e-01 6.725362e-01
## 1276 1277 1278 1279 1280
## 6.887903e-01 NA 7.829428e-02 1.689032e-01 3.185880e-01
## 1281 1282 1283 1284 1285
## 6.122300e-01 3.012564e-01 6.919803e-01 1.676837e-01 5.664418e-01
## 1286 1287 1288 1289 1290
## 7.456290e-01 6.786941e-10 4.624444e-01 6.017027e-01 2.582524e-01
## 1291 1292 1293 1294 1295
## NA 5.906466e-01 5.239534e-01 5.922544e-01 NA
## 1296 1297 1298 1299 1300
## 4.322995e-01 2.540866e-01 NA 6.051482e-01 1.314524e-01
## 1301 1302 1303 1304 1305
## 5.618452e-01 NA 6.799239e-01 NA NA
## 1306 1307 1308 1309 1310
## NA 7.464536e-01 7.094572e-01 2.732531e-01 3.609124e-01
## 1311 1312 1313 1314 1315
## 6.783549e-01 2.094546e-01 9.913750e-03 6.226825e-01 6.002261e-01
## 1316 1317 1318 1319 1320
## 5.071548e-01 NA 2.862795e-01 NA 5.815106e-01
## 1321 1322 1323 1324 1325
## NA NA 6.922960e-01 2.299202e-01 1.130099e-01
## 1326 1327 1328 1329 1330
## 4.190618e-01 6.492383e-01 7.492959e-01 5.839479e-01 6.991548e-01
## 1331 1332 1333 1334 1335
## 1.183066e-01 6.198383e-01 7.096280e-01 6.726239e-01 7.142033e-01
## 1336 1337 1338 1339 1340
## NA 4.272753e-01 7.407902e-01 2.812303e-01 6.824172e-01
confusion<-table(prob>0.5,claimants$ATTORNEY)
confusion
##
## 0 1
## FALSE 380 125
## TRUE 198 393
# Model Accuracy
Accuracy<-sum(diag(confusion)/sum(confusion))
Accuracy
## [1] 0.705292
# ROC Curve
library(ROCR)
## Loading required package: gplots
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
rocrpred<-prediction(prob,claimants$ATTORNEY)
rocrperf<-performance(rocrpred,'tpr','fpr')
plot(rocrperf,colorize=T,text.adj=c(-0.2,1.7))
# More area under the ROC Curve better is the logistic regression model obtained