A beginner's guide to generalized additive models with R:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Newburgh
Highland Statistics Ltd.
2012
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xii, 196 Seiten Illustrationen, Diagramme |
| ISBN: | 9780957174122 |
Internformat
MARC
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| 245 | 1 | 0 | |a A beginner's guide to generalized additive models with R |c Alain F. Zuur |
| 264 | 1 | |a Newburgh |b Highland Statistics Ltd. |c 2012 | |
| 300 | |a xii, 196 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
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| adam_text | Vil
Contents
Preface
...............................................................................................................
v
Acknowledgements
..........................................................................................
vi
Datasets
used in this book
................................................................................vi
Cover art
...........................................................................................................vi
Contributors
......................................................................................................xi
1
Review of multiple linear regression
........__.................................................1
1.1
Light levels and size of the human visual system
.......................................1
12
The variables
...............................................................................................1
1.3
A protocol for the analysis
..........................................................................3
1.4
Data exploration
..........................................................................................5
1.5
Multiple linear regression
...........................................................................9
13.1
Underlying statistical theory
................................................................9
152
Multiple linear regression
..................................................................20
153
Fitting the model in
R
and estimated parameters
..............................20
1.6
Finding the optimal model
........................................................................21
1.7
Degrees of freedom
...................................................................................22
1.8
Model validation
.......................................................................................23
1.9.
Model interpretation
.................................................................................27
1.10
What happens if we use
collinear covariates?
.........................................28
1.11
Should we have applied a mixed effects model?
....................................29
1.12
What to do if things go wrong
.................................................................30
1.13
What to present in a paper
.......................................................................31
2
Introduction to additive models using deep-sea fisheries data
..................33
2.1
Impact of deep-sea fisheries
......................................................................33
2.2
First encounter with smoothers
.................................................................34
2.2.1
Applying linear regression
.................................................................34
2.2.2
Applying cubic polynomials
..............................................................38
22.3
A simple GAM
..................................................................................39
22.4
Moving average and LOESS smoothers
............................................40
23
Applying GAM in
R
using the mgcv package
..........................................44
2.4
Cross-validation
........................................................................................46
2 5
Model validation
.......................................................................................50
25.1
Normality and homogeneity
..............................................................50
252
Independence
.....................................................................................51
2.6
Extending the GAM with more covariates
...............................................Ü5
2.6.1
GAM with a smoother and a nominal covariate
................................55
2.62
GAM with an interaction term; first implementation
.......................5%
2.63
GAM with an interaction term; second implementation
...................61
2.6.4
GAM with an interaction term; third implementation
.......................61
2.7
Transforming the density data
...................................................................63
2.8
Allowing for heterogeneity
.......................................................................64
2.9
Transforming and allowing for heterogeneity
...........................................67
2.10
What to present in a paper
.......................................................................68
VIH
3
Technical aspects of GAM using pelagic
bioluminescent
organisms
------69
3.1
Pelagic
bioluminescent
organism data
.....................................................69
32
Linear regression
......................................................................................69
33
Polynomial regression model
...................................................................72
3.4.
Linear spline regression
...........................................................................73
33
Quadratic spline regression
......................................................................78
3.6
Cubic regression splines
...........................................................................80
3.7
The number of knots
.................................................................................84
3.8
Penalized quadratic spline regression
.......................................................84
3.9
Other smoothers
.......................................................................................88
3.10
Cubic smoothing spline
..........................................................................89
3.11
Summary of smoother types
...................................................................93
3.12
Degrees of freedom of a smoother*
.......................................................94
3.13
Bias-variance trade-off
...........................................................................96
3.14
Confidence intervals
...............................................................................96
3.15
Using the function gam in mgcv
............................................................99
3.16
The danger of using
G
AM
....................................................................102
3.17
Additive models with multiple smoothers
............................................106
4
Introducing generalized additive models using deep-sea fishery data..... Ill
4.1
From additive models to generalized additive models
...........................
Ill
42
Review of GLM
......................................................................................
Ill
42.1
Distribution
.....................................................................................112
42.2
Predictor function
...........................................................................114
423
Link function
...................................................................................114
4.3
Start with GLM or GAM?
......................................................................114
4.4
Results of
Poisson
and negative binomial GLM
....................................115
45
Using the offset in a GLM or GAM
.......................................................118
4.6
Poisson GLM
with offset
.......................................................................119
4.7
Negative binomial GLM with offset
......................................................120
4.8
Poisson
and negative binomial GAM with offset
..................................120
4.9
What to present in paper
.........................................................................126
5
Additive modelling applied on stable isotope ratios of ocean squid
......... 127
5.1
Stable isotope ratios of squid
..................................................................127
5.2
The variables
..........................................................................................128
5.3
Data exploration
.....................................................................................129
5.4
Brainstorming
.........................................................................................131
55
Applying the multiple linear regression model
......................................132
5.6
Applying an additive model
...................................................................134
5.7
Testing linearity versus non-linearity
.....................................................136
5.7.1
Programming a smoother manually**
............................................137
5.72
Summary of the mathematics
..........................................................141
5.8
Consequences of ignoring collinearicy in the additive model
................141
5.9
Discussion
..............................................................................................142
5.10
What to present in apaper
....................................................................143
6
Generalized Additive Models applied on northern gannets
......__............145
6.1
Northern gannets in the North Sea
..........................................................145
6.2
The variables
...........................................................................................145
6.3
Brainstorming
..........................................................................................146
6.4
Data exploration
......................................................................................150
6.5
Building up the complexity of the GAMs
...............................................152
6.6
Zero-inflated GAM
.................................................................................161
6.6.1
A zero-inflated model for the gannet data
.......................................162
6.6.2
ZIP GAM using gamlss
...................................................................165
6.7
Discussion
...............................................................................................167
6.8
What to present in a paper
.......................................................................168
7
Generalized Additive Models applied on parasites of Argentine hake.
__169
7.1
Parasites of Argentine hake in the Argentine Sea
...................................169
7.2
The variables
...........................................................................................169
7.3
Data exploration
......................................................................................171
7.4
Brainstorming
..........................................................................................173
7.5
Applying binomial GAM
........................................................................174
7.6
Discussion
...............................................................................................183
7.7
What to present in a paper
.......................................................................184
References
··..·.·······■····.····■«·.···.*.···■«.·...■····.«.···.···.···«·*·».··.·.....■···■·■···*··..··.·■■··
«•«••ISS
Index..................................................................
..............—....„..._..__...............189
A Beginner s
Cuide
to Generalized Additive Models with
R
is, as
the title implies, a practical handbook for the non-statistician.
The author s philosophy is that the shortest path to
comprehension of a statistical technique without delving into
extensive mathematical detail is through programming its basic
principles in, for example, R.
Not a series of cookbook exercises, the author uses data from
biological studies to go beyond theory and immerse the reader in
real-world analysis with its inherent untidiness and challenges.
The book begins with a review of multiple linear regression using
research on human crania size and ambient light levels and
continues with an introduction to additive models based on deep
sea fishery data. Research on pelagic
bioluminescent
organisms
demonstrates simple linear regression techniques to program a
smoother. In Chapter
4
thedeep sea fishery study is.revisited for
a discussion of generalized additive models. The remaining
chapters present detailed case studies illustrating the application
of Gaussian,
Poisson,
negative binomial,
zero-inftâUèd Poisson,
and binomial generalized additive models using
seaWrd,
Squid,
and fish parasite studies.
Highland Statistics Ltd. (www.highstat.com) is a consultancy
based in Scotland providing applied statistics training worldwide.
They have presented courses throughout Europe, as well as the
Middle East, New Zealand, Canada, and Central and South
America. Since its inception in
2000,
Highland Statistics has
educated some
5000
ecologists on subject such as R, data
exploration, regression, CLM, CAM, mixed modelling,
multivariate analysis, and time series analysis.
ISBN
978-0-9571741-2-2
03999
|
| any_adam_object | 1 |
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| bvnumber | BV040637559 |
| classification_rvk | ST 250 |
| ctrlnum | (OCoLC)828787519 (DE-599)BSZ376374365 |
| discipline | Informatik |
| format | Book |
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| genre_facet | Einführung |
| id | DE-604.BV040637559 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:28:00Z |
| institution | BVB |
| isbn | 9780957174122 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025464674 |
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| spelling | Zuur, Alain F. Verfasser (DE-588)1068021438 aut A beginner's guide to generalized additive models with R Alain F. Zuur Newburgh Highland Statistics Ltd. 2012 xii, 196 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke R Programm (DE-588)4705956-4 gnd rswk-swf Verallgemeinertes lineares Modell (DE-588)4124382-1 gnd rswk-swf Umweltdaten (DE-588)4452653-2 gnd rswk-swf Datenverarbeitung (DE-588)4011152-0 gnd rswk-swf Verallgemeinertes Regressionsmodell (DE-588)4271471-0 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf (DE-588)4151278-9 Einführung gnd-content R Programm (DE-588)4705956-4 s Verallgemeinertes Regressionsmodell (DE-588)4271471-0 s DE-604 Verallgemeinertes lineares Modell (DE-588)4124382-1 s Statistik (DE-588)4056995-0 s Umweltdaten (DE-588)4452653-2 s Datenverarbeitung (DE-588)4011152-0 s Statistische Analyse (DE-588)4116599-8 s Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025464674&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025464674&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Zuur, Alain F. A beginner's guide to generalized additive models with R R Programm (DE-588)4705956-4 gnd Verallgemeinertes lineares Modell (DE-588)4124382-1 gnd Umweltdaten (DE-588)4452653-2 gnd Datenverarbeitung (DE-588)4011152-0 gnd Verallgemeinertes Regressionsmodell (DE-588)4271471-0 gnd Statistische Analyse (DE-588)4116599-8 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4124382-1 (DE-588)4452653-2 (DE-588)4011152-0 (DE-588)4271471-0 (DE-588)4116599-8 (DE-588)4056995-0 (DE-588)4151278-9 |
| title | A beginner's guide to generalized additive models with R |
| title_auth | A beginner's guide to generalized additive models with R |
| title_exact_search | A beginner's guide to generalized additive models with R |
| title_full | A beginner's guide to generalized additive models with R Alain F. Zuur |
| title_fullStr | A beginner's guide to generalized additive models with R Alain F. Zuur |
| title_full_unstemmed | A beginner's guide to generalized additive models with R Alain F. Zuur |
| title_short | A beginner's guide to generalized additive models with R |
| title_sort | a beginner s guide to generalized additive models with r |
| topic | R Programm (DE-588)4705956-4 gnd Verallgemeinertes lineares Modell (DE-588)4124382-1 gnd Umweltdaten (DE-588)4452653-2 gnd Datenverarbeitung (DE-588)4011152-0 gnd Verallgemeinertes Regressionsmodell (DE-588)4271471-0 gnd Statistische Analyse (DE-588)4116599-8 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | R Programm Verallgemeinertes lineares Modell Umweltdaten Datenverarbeitung Verallgemeinertes Regressionsmodell Statistische Analyse Statistik Einführung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025464674&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025464674&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT zuuralainf abeginnersguidetogeneralizedadditivemodelswithr |