Verfügbarkeit: Mixed-effects models in S and S-PLUS | Technische Universität München

Mixed-effects models in S and S-PLUS:

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Bibliographische Detailangaben
Beteiligte Personen:	Pinheiro, José C. (VerfasserIn), Bates, Douglas M. (VerfasserIn)
Format:	Buch
Sprache:	Englisch
Veröffentlicht:	New York [u.a.] Springer 2004
Ausgabe:	[Nachdr.]
Schriftenreihe:	Statistics and computing
Schlagwörter:	S > Programmiersprache Gemischtes Modell S-PLUS
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015713239&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	XVI, 528 S. graph. Darst.
ISBN:	0387989579

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Datensatz im Suchindex

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adam_text	Contents Preface vii I Linear Mixed-Effects Models 1 1 Linear Mixed-Effects Models 3 1.1 A Simple Example of Random Effects ............ 4 1.1.1 Fitting the Random-Effects Model With Ime .... 8 1.1.2 Assessing the Fitted Model .............. 11 1.2 A Randomized Block Design ................. 12 1.2.1 Choosing Contrasts for Fixed-Effects Terms ..... 14 1.2.2 Examining the Model ................. 19 1.3 Mixed-Effects Models for Replicated, Blocked Designs ... 21 1.3.1 Fitting Random Interaction Terms .......... 23 1.3.2 Unbalanced Data .................... 25 1.3.3 More General Models for the Random Interaction Effects .......................... 27 1.4 An Analysis of Covariance Model ............... 30 1.4.1 Modeling Simple Linear Growth Curves ....... 30 1.4.2 Predictions of the Response and the Random Effects 37 1.5 Models for Nested Classification Factors ........... 40 1.5.1 Model Building for Multilevel Models ........ 44 1.6 A Split-Plot Experiment .................... 45 1.7 Chapter Summary ....................... 52 Exercises ............................... 52 Theory and Computational Methods for LME Models 57 2.1 The LME Model Formulation ................. 58 2.1.1 Single Level of Grouping ................ 58 2.1.2 A Multilevel LME Model ............... 60 2.2 Likelihood Estimation for LME Models ........... 62 2.2.1 The Single-Level LME Likelihood Function ..... 62 2.2.2 Orthogonal-Triangular Decompositions ........ 66 2.2.3 Evaluating the Likelihood Through Decompositions 68 2.2.4 Components of the Profiled Log-Likelihood ..... 71 2.2.5 Restricted Likelihood Estimation ........... 75 2.2.6 Multiple Levels of Random Effects .......... 77 2.2.7 Parameterizing Relative Precision Factors ...... 78 2.2.8 Optimization Algorithms ............... 79 2.3 Approximate Distributions ................... 81 2.4 Hypothesis Tests and Confidence Intervals .......... 82 2.4.1 Likelihood Ratio Tests ................. 83 2.4.2 Hypothesis Tests for Fixed-Effects Terms ...... 87 2.4.3 Confidence Intervals .................. 92 2.5 Fitted Values and Predictions ................. 94 2.6 Chapter Summary ....................... 94 Exercises ............................... 96 Describing the Structure of Grouped Data 97 3.1 The Display Formula and Its Components .......... 97 3.2 Constructing groupedData Objects .............. 101 3.2.1 Roles of Other Experimental or Blocking Factors . . 104 3.2.2 Constructors for Balanced Data ............ 108 3.3 Controlling Trellis Graphics Presentations of Grouped Data 110 3.3.1 Layout of the Trellis Plot ............... 110 3.3.2 Modifying the Vertical and Horizontal Scales .... 113 3.3.3 Modifying the Panel Function ............. 114 3.3.4 Plots of Multiply-Nested Data ............ 116 3.4 Summaries ........................... 120 3.5 Chapter Summary ....................... 130 Exercises ............................... 130 Fitting Linear Mixed-Effects Models 133 4.1 Fitting Linear Models in S with lm and lmList ....... 134 4.1.1 The lmList Function ................. 139 4.2 Fitting Linear Mixed-Effects Models with Ime ........ 146 4.2.1 Fitting Single-Level Models .............. 146 4.2.2 Patterned Variance-Covariance Matrices for the Random Effects: The pdMat Classes ......... 157 4.2.3 Fitting Multilevel Models ............... 167 4.3 Examining a Fitted Model ................... 174 4.3.1 Assessing Assumptions on the Within-Group Error . 174 4.3.2 Assessing Assumptions on the Random Effects . . . 187 4.4 Chapter Summary ....................... 196 Exercises ............................... 197 Extending the Basic Linear Mixed-Effects Model 201 5.1 General Formulation of the Extended Model ......... 202 5.1.1 Estimation and Computational Methods ....... 202 5.1.2 The GLS model ..................... 203 5.1.3 Decomposing the Within-Group Variance-Covariance Structure ........................ 205 5.2 Variance Functions for Modeling Heteroscedasticity ..... 206 5.2.1 varFunc classes in nlme ................ 208 5.2.2 Using varFunc classes with Ime ............ 214 5.3 Correlation Structures for Modeling Dependence ...... 226 5.3.1 Serial Correlation Structures ............. 226 5.3.2 Spatial Correlation Structures ............. 230 5.3.3 corStruct classes in nlme ............... 232 5.3.4 Using corStruct Classes with Ime .......... 239 5.4 Fitting Extended Linear Models with gls .......... 249 5.5 Chapter Summary ....................... 266 Exercises ............................... 267 II Nonlinear Mixed-Effects Models 271 6 NLME Models: Basic Concepts and Motivating Examples 273 6.1 LME Models vs. NLME Models ................ 273 6.2 Indomethicin Kinetics ..................... 277 6.3 Growth of Soybean Plants ................... 287 6.4 Clinical Study of Phénobarbital Kinetics ........... 294 6.5 Chapter Summary ....................... 300 Exercises ............................... 301 7 Theory and Computational Methods for NLME Models 305 7.1 The NLME Model Formulation ................ 306 7.1.1 Single-Level of Grouping ................ 306 7.1.2 Multilevel NLME Models ............... 309 7.1.3 Other NLME Models .................. 310 7.2 Estimation and Inference in NLME Models ......... 312 7.2.1 Likelihood Estimation ................. 312 7.2.2 Inference and Predictions ............... 322 7.3 Computational Methods .................... 324 7.4 Extending the Basic NLME Model .............. 328 7.4.1 General model formulation .............. 328 7.4.2 Estimation and Computational Methods ....... 329 7.5 An Extended Nonlinear Regression Model .......... 332 7.5.1 General Model Formulation .............. 333 7.5.2 Estimation and Computational Methods ....... 334 7.6 Chapter Summary ....................... 336 8 Fitting Nonlinear Mixed-Effects Models 337 8.1 Fitting Nonlinear Models in S with nls and nlsList .... 338 8.1.1 Using the nls Function ................ 338 8.1.2 Self-Starting Nonlinear Model Functions ....... 342 8.1.3 Separate Nonlinear Fits by Group: The nlsList Function ......................... 347 8.2 Fitting Nonlinear Mixed-Effects Models with nlme ..... 354 8.2.1 Fitting Single-Level nlme Models ........... 354 8.2.2 Using Covariates with nlme .............. 365 8.2.3 Fitting Multilevel nlme Models ............ 385 8.3 Extending the Basic nlme Model ............... 391 8.3.1 Variance Functions in nlme .............. 391 8.3.2 Correlation Structures in nlme ............ 395 8.3.3 Fitting Extended Nonlinear Regression Models with gnls ........................ 401 8.4 Chapter Summary ....................... 409 Exercises ............................... 410 References 415 A Data Used in Examples and Exercises 423 A.I Alfalfa — Split-Plot Experiment on Varieties of Alfalfa . . . . 425 A. 2 Assay — Bioassay on Cell Culture Plate ............ 425 A.3 BodyWeight—Body Weight Growth in Rats ......... 427 A.4 Cefamandole — Pharmacokinetics of Cefamandole ...... 427 A. 5 CO2—Carbon Dioxide Uptake ................. 428 A.6 Dialyzer — High-Flux Hemodialyzer .............. 429 A. 7 D Nase— Assay Data for the Protein DNase ......... 429 A. 8 Earthquake — Earthquake Intensity .............. 430 A. 9 ergoStool — Ergometrics Experiment with Stool Types . . . 431 A. 10 Glucose2 — Glucose Levels Following Alcohol Ingestion . . . 432 A. 11 IGF — Radioimmunoassay of IGF-I Protein .......... 433 A. 12 Indometh — Indomethacin Kinetics ............... 433 A. 13 Loblolly—Growth of Loblolly Pine Trees ........... 434 A. 14 Machines — Productivity Scores for Machines and Workers . 435 A. 15 Oats — Split-plot Experiment on Varieties of Oats ...... 435 A. 16 Orange — Growth of Orange Trees ............... 436 A. 17 Orthodont—Orthodontic Growth Data ............ 436 Α. 18 Ovary — Counts of Ovarian Follicles .............. 437 A. 19 Oxboys — Heights of Boys in Oxford .............. 437 A. 20 Oxide — Variability in Semiconductor Manufacturing .... 437 A.21 PBG — Effect of Phenylbiguanide on Blood Pressure ..... 438 A.22 PBIB—A Partially Balanced Incomplete Block Design ... 439 A.23 Phenobarb — Phenobarbitol Kinetics .............. 440 A. 24 Pixel — Pixel Intensity in Lymphnodes ............ 440 A. 25 Quinidine — Quinidine Kinetics ................. 441 A.26 Rail — Evaluation of Stress in Rails .............. 443 A. 27 Soybean — Soybean Leaf Weight over Time .......... 443 A.28 Spruce — Growth of Spruce Trees ............... 444 A. 29 Theoph — Theophylline Kinetics ................ 444 A.30 Wafer—Modeling of Analog MOS Circuits .......... 448 A.31 Nheat2—Wheat Yield Trials ................. 448 B S Functions and Classes 451 ACF ................................ 451 ACF.lme ............................. 452 anova.lme ............................ 453 coef.lme ............................ 455 coef . lmList .......................... 457 fitted. Ime ........................... 458 fixef .............................. 459 gapply .............................. 460 getGroups ............................ 461 gls ................................ 462 gnls ............................... 464 groupedData .......................... 466 gsummary ............................ 469 intervals ............................ 471 intervals . Ime ......................... 471 intervals.lmList ....................... 473 Ime ................................ 474 lmeControl ........................... 476 lmList .............................. 478 logLik .............................. 479 nlme ............................... 479 nlmeControl .......................... 483 nlsList ............................. 485 pairs . Ime ............................ 486 plot. Ime ............................ 488 plot.nfnGroupedData ..................... 490 plot.nmGroupedData ..................... 492 plot.Variogram ........................ 494 predict. Ime .......................... 495 qqnorm.lme ........................... 497 ranef .............................. 498 ranef . Ime ............................ 499 ranef .lmList .......................... 501 residuals . Ime ......................... 503 self Start ............................ 504 selfStart.default ...................... 505 selfStart.formula ...................... 506 Variogram ............................ 507 Variogram.lme ......................... 508 С A Collection of Self-Starting Nonlinear Regression Models 511 C.I SSasymp — The Asymptotic Regression Model ........ 511 C.I.I Starting Estimates for SSasymp ............ 511 C.2 SSasympOf f — Asymptotic Regression with an Offset .... 512 C.2.1 Starting Estimates for SSasympOf f .......... 512 C.3 SSasympOrig — Asymptotic Regression Through the Origin 513 C.3.1 Starting Estimates for SSasympOrig ......... 513 C.4 SSbiexp — Biexponential Model ................ 514 C.4.1 Starting Estimates for SSbiexp ............ 515 C.5 SSf ol — First-Order Compartment Model .......... 516 C.5.1 Starting Estimates for SSf ol ............. 516 C.6 SSfpl — Four-Parameter Logistic Model ........... 517 C.6.1 Starting Estimates for SSfpl ............. 518 C.7 SSlogis — Simple Logistic Model ............... 519 C.7.1 Starting Estimates for SSlogis ............ 519 C.8 SSmicmen — Michaelis-Menten Model ............. 520 C.S.I Starting Estimates for SSmicmen ........... 521 Index 523
any_adam_object	1
author	Pinheiro, José C. Bates, Douglas M.
author_facet	Pinheiro, José C. Bates, Douglas M.
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discipline	Biologie Informatik Mathematik
edition	[Nachdr.]
format	Book
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spellingShingle	Pinheiro, José C. Bates, Douglas M. Mixed-effects models in S and S-PLUS S Programmiersprache (DE-588)4234472-4 gnd Gemischtes Modell (DE-588)4156565-4 gnd S-PLUS (DE-588)4321162-8 gnd
subject_GND	(DE-588)4234472-4 (DE-588)4156565-4 (DE-588)4321162-8
title	Mixed-effects models in S and S-PLUS
title_alt	Mixed effects models in S and S-PLUS
title_auth	Mixed-effects models in S and S-PLUS
title_exact_search	Mixed-effects models in S and S-PLUS
title_full	Mixed-effects models in S and S-PLUS José C. Pinheiro ; Douglas M. Bates
title_fullStr	Mixed-effects models in S and S-PLUS José C. Pinheiro ; Douglas M. Bates
title_full_unstemmed	Mixed-effects models in S and S-PLUS José C. Pinheiro ; Douglas M. Bates
title_short	Mixed-effects models in S and S-PLUS
title_sort	mixed effects models in s and s plus
topic	S Programmiersprache (DE-588)4234472-4 gnd Gemischtes Modell (DE-588)4156565-4 gnd S-PLUS (DE-588)4321162-8 gnd
topic_facet	S Programmiersprache Gemischtes Modell S-PLUS
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015713239&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT pinheirojosec mixedeffectsmodelsinsandsplus AT batesdouglasm mixedeffectsmodelsinsandsplus

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