Modeling and Multivariate Methods - SAS

More documents

Recommendations

Info

Contents Introduction to the Nonlinear Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .243 Example of Nonlinear Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .243 Launch the Nonlinear Platform. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .245 Nonlinear Fitting with Fit Curve. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 Fit Curve Models and Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 Fit Curve Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Model Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .252 Fit a Custom Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .256 Create a Model Column . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257 Nonlinear Fit Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .259 Nonlinear Fit Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .261 Use the Model Library. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Additional Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .268 Maximum Likelihood: Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .268 Probit Model with Binomial Errors: Numerical Derivatives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269 Poisson Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Statistical Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Profile Likelihood Confidence Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 How Custom Loss Functions Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .272 Notes Concerning Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .273 Notes on Effective Nonlinear Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .275
Chapter 9 Performing Nonlinear Regression 243 Introduction to the Nonlinear Platform Introduction to the Nonlinear Platform The Nonlinear platform fits models that are nonlinear in the parameters. The Fit Curve personality of the platform has a variety of predefined models, such as polynomial, logistic, Gompertz, exponential, peak, and pharmacokinetic models. You can also fit custom nonlinear models by specifying the model formula and parameters to be estimated. The models can be fit using the default least squares loss function or a custom loss function. The platform minimizes the sum of the loss function across the observations. Specifying a grouping variable lets you estimate separate model parameters for each level of the grouping variable. The fitted models and estimated parameters can be compared across the levels of the grouping variable. The Nonlinear platform is a good choice for models that are nonlinear in the parameters. Some models are linear in the parameters (for example, a quadratic or other polynomial) or can be transformed to be such (for example, when you use a log transformation of x). The Fit Model or Fit Y by X platforms are more appropriate in these situations. For more information about these platforms, see the Basic Analysis and Graphing book. Example of Nonlinear Fitting You have data on toxicity levels of a drug. You want to build a model for toxicity as a function of the concentration of the drug. There are four formulations of the drug to compare. Follow the steps below: 1. Open the Bioassay.jmp sample data table. 2. Select Analyze > Modeling > Nonlinear. 3. Assign Toxicity to the Y, Response role. 4. Assign log Conc to the X, Predictor Formula role. 5. Assign Formulation to the Group role. 6. Click OK. 7. Select Sigmoid Curves > Logistic Curves > Fit Logistic 4P from the Fit Curve red triangle menu. A portion of the report is shown in Figure 9.2.
Page 1 and 2:
Version 10 Modeling and Multivariat
Page 3:
INDIRECT OR CONSEQUENTIAL DAMAGES O
Page 6 and 7:
6 Emphasis Options for Standard Lea
Page 8 and 9:
8 Models with Crossed, Interaction,
Page 10 and 11:
10 8 Analyzing Screening Designs Us
Page 12 and 13:
12 Examining the Residuals . . . .
Page 14 and 15:
14 Response Frequencies . . . . . .
Page 16 and 17:
16 19 Analyzing Principal Component
Page 18 and 19:
18 Interpreting the Profiles . . .
Page 20 and 21:
20 Discriminant Analysis . . . . .
Page 22 and 23:
Contents Book Conventions. . . . .
Page 24 and 25:
24 Learn about JMP Chapter 1 Book C
Page 26 and 27:
26 Learn about JMP Chapter 1 Book C
Page 28 and 29:
28 Learn about JMP Chapter 1 Additi
Page 30 and 31:
30 Learn about JMP Chapter 1 Additi
Page 32 and 33:
Contents Launch the Fit Model Platf
Page 34 and 35:
34 Introduction to the Fit Model Pl
Page 36 and 37:
Page 38 and 39:
Page 40 and 41:
Page 42 and 43:
Page 44 and 45:
Page 46 and 47:
Page 48 and 49:
Page 50 and 51:
Page 52 and 53:
Page 54 and 55:
Contents Example Using Standard Lea
Page 56 and 57:
56 Fitting Standard Least Squares M
Page 58 and 59:
Page 60 and 61:
Page 62 and 63:
Page 64 and 65:
Page 66 and 67:
Page 68 and 69:
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
100 Fitting Standard Least Squares
Page 102 and 103:
Page 104 and 105:
Page 106 and 107:
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Contents Overview of Stepwise Regre
Page 136 and 137:
136 Fitting Stepwise Regression Mod
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Contents Example of a Multiple Resp
Page 158 and 159:
158 Fitting Multiple Response Model
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
Page 172 and 173:
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
Contents Overview of Generalized Li
Page 180 and 181:
180 Fitting Generalized Linear Mode
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193: 192 Fitting Generalized Linear Mode
Page 198 and 199: Contents Introduction to Logistic M
Page 200 and 201: 200 Performing Logistic Regression
Page 230 and 231: Contents Overview of the Screening
Page 232 and 233: 232 Analyzing Screening Designs Cha
Page 244 and 245: 244 Performing Nonlinear Regression
Page 278 and 279: Contents Overview of Neural Network
Page 280 and 281: 280 Creating Neural Networks Chapte
Page 292 and 293:
292 Creating Neural Networks Chapte
Page 294 and 295:
Contents Launching the Platform. .
Page 296 and 297:
296 Modeling Relationships With Gau
Page 298 and 299:
Page 300 and 301:
Page 302 and 303:
Page 304 and 305:
Contents Overview of the Loglinear
Page 306 and 307:
306 Fitting Dispersion Effects with
Page 308 and 309:
Page 310 and 311:
Page 312 and 313:
Page 314 and 315:
Page 316 and 317:
Contents Introduction to Partitioni
Page 318 and 319:
318 Recursively Partitioning Data C
Page 320 and 321:
Page 322 and 323:
Page 324 and 325:
Page 326 and 327:
Page 328 and 329:
Page 330 and 331:
Page 332 and 333:
Page 334 and 335:
Page 336 and 337:
Page 338 and 339:
Page 340 and 341:
Page 342 and 343:
Page 344 and 345:
Page 346 and 347:
Page 348 and 349:
Page 350 and 351:
Page 352 and 353:
Contents Launch the Platform . . .
Page 354 and 355:
354 Performing Time Series Analysis
Page 356 and 357:
Page 358 and 359:
Page 360 and 361:
Page 362 and 363:
Page 364 and 365:
Page 366 and 367:
Page 368 and 369:
Page 370 and 371:
Page 372 and 373:
Page 374 and 375:
Page 376 and 377:
Page 378 and 379:
Page 380 and 381:
Page 382 and 383:
Page 384 and 385:
Contents The Categorical Platform .
Page 386 and 387:
386 Performing Categorical Response
Page 388 and 389:
Page 390 and 391:
Page 392 and 393:
Page 394 and 395:
Page 396 and 397:
Page 398 and 399:
Page 400 and 401:
Page 402 and 403:
Contents Introduction to Choice Mod
Page 404 and 405:
404 Performing Choice Modeling Chap
Page 406 and 407:
Page 408 and 409:
Page 410 and 411:
Page 412 and 413:
Page 414 and 415:
Page 416 and 417:
Page 418 and 419:
Page 420 and 421:
Page 422 and 423:
Page 424 and 425:
Page 426 and 427:
Page 428 and 429:
Page 430 and 431:
Page 432 and 433:
Page 434 and 435:
Page 436 and 437:
Page 438 and 439:
Page 440 and 441:
Page 442 and 443:
Contents Launch the Multivariate Pl
Page 444 and 445:
444 Correlations and Multivariate T
Page 446 and 447:
Page 448 and 449:
Page 450 and 451:
Page 452 and 453:
Page 454 and 455:
Page 456 and 457:
Page 458 and 459:
Page 460 and 461:
Contents Introduction to Clustering
Page 462 and 463:
462 Clustering Data Chapter 18 The
Page 464 and 465:
464 Clustering Data Chapter 18 Hier
Page 466 and 467:
Page 468 and 469:
Page 470 and 471:
470 Clustering Data Chapter 18 K-Me
Page 472 and 473:
472 Clustering Data Chapter 18 K-Me
Page 474 and 475:
474 Clustering Data Chapter 18 Norm
Page 476 and 477:
476 Clustering Data Chapter 18 Norm
Page 478 and 479:
478 Clustering Data Chapter 18 Self
Page 480 and 481:
480 Clustering Data Chapter 18 Self
Page 482 and 483:
Contents Principal Components. . .
Page 484 and 485:
484 Analyzing Principal Components
Page 486 and 487:
Page 488 and 489:
Page 490 and 491:
Page 492 and 493:
Contents Introduction . . . . . . .
Page 494 and 495:
494 Performing Discriminant Analysi
Page 496 and 497:
Page 498 and 499:
Page 500 and 501:
Page 502 and 503:
Page 504 and 505:
Page 506 and 507:
Contents Overview of the Partial Le
Page 508 and 509:
508 Fitting Partial Least Squares M
Page 510 and 511:
Page 512 and 513:
Page 514 and 515:
Page 516 and 517:
Page 518 and 519:
Page 520 and 521:
Page 522 and 523:
Page 524 and 525:
Contents Introduction to Item Respo
Page 526 and 527:
526 Scoring Tests Using Item Respon
Page 528 and 529:
Page 530 and 531:
Page 532 and 533:
Page 534 and 535:
Page 536 and 537:
Contents Surface Plots . . . . . .
Page 538 and 539:
538 Plotting Surfaces Chapter 23 La
Page 540 and 541:
Page 542 and 543:
Page 544 and 545:
544 Plotting Surfaces Chapter 23 Th
Page 546 and 547:
546 Plotting Surfaces Chapter 23 Th
Page 548 and 549:
548 Plotting Surfaces Chapter 23 Pl
Page 550 and 551:
550 Plotting Surfaces Chapter 23 Pl
Page 552 and 553:
552 Plotting Surfaces Chapter 23 Ke
Page 554 and 555:
Contents Introduction to Profiling
Page 556 and 557:
556 Visualizing, Optimizing, and Si
Page 558 and 559:
Page 560 and 561:
Page 562 and 563:
Page 564 and 565:
Page 566 and 567:
Page 568 and 569:
Page 570 and 571:
Page 572 and 573:
Page 574 and 575:
Page 576 and 577:
Page 578 and 579:
Page 580 and 581:
Page 582 and 583:
Page 584 and 585:
Page 586 and 587:
Page 588 and 589:
Page 590 and 591:
Page 592 and 593:
Page 594 and 595:
Page 596 and 597:
Page 598 and 599:
Page 600 and 601:
Page 602 and 603:
Page 604 and 605:
Page 606 and 607:
Page 608 and 609:
Page 610 and 611:
Page 612 and 613:
Page 614 and 615:
Page 616 and 617:
Page 618 and 619:
Page 620 and 621:
Page 622 and 623:
Page 624 and 625:
Page 626 and 627:
Page 628 and 629:
Contents Example of Model Compariso
Page 630 and 631:
630 Comparing Model Performance Cha
Page 632 and 633:
Page 634 and 635:
Page 636 and 637:
Page 638 and 639:
Page 640 and 641:
640 References Becker, R.A. and Cle
Page 642 and 643:
642 References Draper, N. and Smith
Page 644 and 645:
644 References Hooper, J. H. and Am
Page 646 and 647:
646 References Matsumoto, M. and Ni
Page 648 and 649:
648 References Rawlings, J.O. (1988
Page 650 and 651:
650 References Umetrics (1995), Mul
Page 652 and 653:
Contents The Response Models . . .
Page 654 and 655:
654 Statistical Details Appendix A
Page 656 and 657:
Page 658 and 659:
Page 660 and 661:
Page 662 and 663:
Page 664 and 665:
Page 666 and 667:
Page 668 and 669:
Page 670 and 671:
Page 672 and 673:
Page 674 and 675:
Page 676 and 677:
Page 678 and 679:
Page 680 and 681:
Page 682 and 683:
Page 684 and 685:
Page 686 and 687:
Page 688 and 689:
Page 690 and 691:
690 Index Birth Death Subset.jmp 46
Page 692 and 693:
692 Index Ellipses Transparency 451
Page 694 and 695:
694 Index test for curvature 46 Kru
Page 696 and 697:
696 Index Customizing 267 Nonlinear
Page 698 and 699:
698 Index reliability analysis 453-
Page 700 and 701:
700 Index Stable 363 Stable Inverti
Page 702:
702 Index
show all

Modeling and Multivariate Methods - SAS

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?