Nonlinear regression with R:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2008
|
| Schriftenreihe: | Use R!
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XI, 144 S. graph. Darst. |
| ISBN: | 9780387096155 |
Internformat
MARC
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| 245 | 1 | 0 | |a Nonlinear regression with R |c Christian Ritz ; Jens Carl Streibig |
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Datensatz im Suchindex
| _version_ | 1804138555914584064 |
|---|---|
| adam_text | Titel: Nonlinear regression with R
Autor: Ritz, Christian
Jahr: 2008
Contents
Preface........................................................VII
1 Introduction............................................... 1
1.1 A stock-recruitment model................................ 2
1.2 Competition between plant biotypes....................... 3
1.3 Grouped dose-response data .............................. 4
2 Getting Started............................................ 7
2.1 Background............................................. 7
2.2 Getting started with nls () ............................... 8
2.2.1 Introducing the data example....................... 9
2.2.2 Model fitting ..................................... 9
2.2.3 Prediction........................................ 13
2.2.4 Making plots ..................................... 15
2.2.5 Illustrating the estimation.......................... 16
2.3 Generalised linear models................................. 18
Exercises ................................................... 20
3 Starting Values and Self-starters........................... 23
3.1 Finding starting values................................... 23
3.1.1 Graphical exploration.............................. 23
3.1.2 Searching a grid................................... 27
3.2 Using self-starter functions ............................... 29
3.2.1 Built-in self-starter functions for nls()............... 30
3.2.2 Defining a self-starter function for nls() ............. 31
Exercises ................................................... 35
4 More on nls 0 ............................................. 37
4.1 Arguments and methods ................................. 37
4.2 Supplying gradient information............................ 38
4.2.1 Manual supply.................................... 39
Contents
4.2.2 Automatic supply................................. 40
4.3 Conditionally linear parameters........................... 41
4.3.1 nlsO using the plinear algorithm................ 42
4.3.2 A pedestrian approach............................. 43
4.4 Fitting models with several predictor variables.............. 45
4.4.1 Two-dimensional predictor ......................... 45
4.4.2 General least-squares minimisation.................. 48
4.5 Error messages.......................................... 50
4.6 Controlling nls 0 ....................................... 52
Exercises ................................................... 53
Model Diagnostics......................................... 55
5.1 Model assumptions...................................... 55
5.2 Checking the mean structure.............................. 56
5.2.1 Plot of the fitted regression curve.................... 56
5.2.2 Residual plots .................................... 59
5.2.3 Laek-of-fit tests................................... 60
5.3 Variance homogeneity.................................... 65
5.3.1 Absolute residuals................................. 65
5.3.2 Levene s test...................................... 65
5.4 Normal distribution...................................... 66
5.4.1 QQ plot.......................................... 67
5.4.2 Shapiro-Wilk test ................................. 69
5.5 Independence........................................... 69
Exercises ................................................... 70
Remedies for Model Violations............................ 73
6.1 Variance modelling...................................... 73
6.1.1 Powor-of-the-inoan variance model................... 74
6.1.2 Other variance models............................. 77
6.2 Transformations......................................... 78
6.2.1 Transform-both-sides approach...................... 78
6.2.2 Finding an appropriato transformation............... 81
6.3 Sandwich estimators..................................... 83
6.4 Weighting.............................................. 85
6.4.1 Decline in nitrogen content in soil................... 87
Exercises ................................................... 91
Uncertainty. Hypothesis Testing, and Model Selection..... 93
7.1 Profile likelihood........................................ 94
7.2 Bootstrap .............................................. 96
7.3 Wald confidence intervals................................. 99
7.4 Estimating derived parameters............................100
7.5 .Vested models ..........................................101
7.5.1 Using t-tests.....................................102
Contents XI
7.5.2 Using F-tests.....................................103
7.6 Non-nested models ......................................105
Exercises ...................................................108
8 Grouped Data.............................................109
8.1 Fitting grouped data models..............................109
8.1.1 Using nls()......................................Ill
8.1.2 Using gnlsO.....................................112
8.1.3 Using nlsListO..................................113
8.2 Model reduction and parameter models ....................114
8.2.1 Comparison of entire groups........................114
8.2.2 Comparison of specific parameters...................115
8.3 Common control ........................................118
8.4 Prediction..............................................121
8.5 Nonlinear mixed models..................................123
Exercises ...................................................131
Appendix A: Datasets and Models.............................133
Appendix B: Self-starter Functions............................135
Appendix C: Packages and Functions..........................137
References.....................................................139
Index..........................................................143
|
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| author | Ritz, Christian Streibig, Jens Carl |
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| building | Verbundindex |
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| callnumber-first | Q - Science |
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| callnumber-raw | QA278.2 |
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| callnumber-subject | QA - Mathematics |
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| ctrlnum | (OCoLC)248979178 (DE-599)BVBBV035269044 |
| dewey-full | 519.5360285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5360285 |
| dewey-search | 519.5360285 |
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| dewey-tens | 510 - Mathematics |
| discipline | Informatik Soziologie Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV035269044 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:30:03Z |
| institution | BVB |
| isbn | 9780387096155 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017074444 |
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| physical | XI, 144 S. graph. Darst. |
| publishDate | 2008 |
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| publisher | Springer |
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| series2 | Use R! |
| spelling | Ritz, Christian Verfasser aut Nonlinear regression with R Christian Ritz ; Jens Carl Streibig New York, NY Springer 2008 XI, 144 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Use R! Nichtlineare Regression (DE-588)4251077-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Nichtlineare Regression (DE-588)4251077-6 s R Programm (DE-588)4705956-4 s DE-604 Streibig, Jens Carl Verfasser aut Erscheint auch als Online-Ausgabe 978-0-387-09616-2 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017074444&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ritz, Christian Streibig, Jens Carl Nonlinear regression with R Nichtlineare Regression (DE-588)4251077-6 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4251077-6 (DE-588)4705956-4 |
| title | Nonlinear regression with R |
| title_auth | Nonlinear regression with R |
| title_exact_search | Nonlinear regression with R |
| title_full | Nonlinear regression with R Christian Ritz ; Jens Carl Streibig |
| title_fullStr | Nonlinear regression with R Christian Ritz ; Jens Carl Streibig |
| title_full_unstemmed | Nonlinear regression with R Christian Ritz ; Jens Carl Streibig |
| title_short | Nonlinear regression with R |
| title_sort | nonlinear regression with r |
| topic | Nichtlineare Regression (DE-588)4251077-6 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Nichtlineare Regression R Programm |
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