Zero inflated models and generalized linear mixed models with R:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Newburgh
Highland Statistics
2012
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 324 S. Ill., graph. Darst. |
| ISBN: | 9780957174108 9780957174115 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV040108752 | ||
| 003 | DE-604 | ||
| 005 | 20190918 | ||
| 007 | t| | ||
| 008 | 120419s2012 xx ad|| |||| 00||| eng d | ||
| 020 | |a 9780957174108 |c pbk |9 978-0-9571741-0-8 | ||
| 020 | |a 9780957174115 |c hbk |9 978-0-9571741-1-5 | ||
| 035 | |a (OCoLC)793925621 | ||
| 035 | |a (DE-599)OBVAC08942638 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-703 |a DE-11 |a DE-20 |a DE-188 |a DE-M49 | ||
| 084 | |a ST 250 |0 (DE-625)143626: |2 rvk | ||
| 084 | |a DAT 307f |2 stub | ||
| 084 | |a MAT 620f |2 stub | ||
| 100 | 1 | |a Zuur, Alain F. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Zero inflated models and generalized linear mixed models with R |c Alain F. Zuur ; Anatoly A. Savaliev ; Elena N. Ieno |
| 264 | 1 | |a Newburgh |b Highland Statistics |c 2012 | |
| 300 | |a XVIII, 324 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a R |g Programm |0 (DE-588)4705956-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Gemischtes Modell |0 (DE-588)4156565-4 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistische Analyse |0 (DE-588)4116599-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Statistik |0 (DE-588)4056995-0 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Statistik |0 (DE-588)4056995-0 |D s |
| 689 | 0 | 1 | |a R |g Programm |0 (DE-588)4705956-4 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Statistische Analyse |0 (DE-588)4116599-8 |D s |
| 689 | 1 | 1 | |a Gemischtes Modell |0 (DE-588)4156565-4 |D s |
| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Saveliev, Anatoly A. |e Verfasser |4 aut | |
| 700 | 1 | |a Ieno, Elena N. |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024965154&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-024965154 | |
Datensatz im Suchindex
| _version_ | 1823849150572134400 |
|---|---|
| adam_text |
IX
Contents
PREFACE
.
V
CONTENTS
.
IX
CONTRIBUTORS
.
XV
1
INTRODUCTION TO BAYESIAN STATISTICS, MCMC TECHNIQUES, AND WINBUGS
.1
1.1
Probabilities and
Bayes'
Theorem
.1
1.2
Likelihood functions
.3
1.3
Conjugate prior distributions
.6
1.4
MCMC
.9
1.4.1
Markov Chain
.9
1.4.2
Transition rules*
.10
1.5
Using WinBUGS
.16
1.6
Summary
.21
2
ZERO INFLATED GLMM APPLIED TO BARN OWL DATA
.25
2.1
Introduction
.25
2.1.1
Vocal begging behaviour of nestling barn owls
.25
2.1.2
Previous analyses of the owl data
.26
2.1.3
Prerequisite knowledge for this chapter
.28
2.2
Importing and coding the data
.28
2.3
Data exploration
.29
2.4
Overdispersion in the
Poisson
GLMM
.34
2.4.1
Assessing overdispersion using Pearson residuals
.34
2.4.2
Assessing overdispersion using an observation level random effect term
.35
2.4.3
Simulation study demonstrating observation level random effect
.35
2.4.4
A GLMM with observation level random effect
.40
2.5
WHY ZERO INFLATED MODELS?
.41
2.6
Implementing
a Poisson
GLM in WinBUGS
.43
2.6.1
Converting vectors to matrices
.43
2.6.2
Data for WinBUGS.
.45
2.6.3
Modelling code for WinBUGS
.45
2.6.4
Initialising the chains
.46
2.6.5
Parameters, thinning rate and length of the chains
.47
2.6.6
Starting WinBUGS from within
R
.47
2.6.7
Assessing convergence of the chains
.48
2.6.8
Summarising the posterior distributions
.49
2.6.9
Pearson residuals
.51
2.6.10
WinBUGS versus GLM results
.53
2.7
Implementing
a Poisson GLMM
in WinBUGS
.54
2.8
Implementing a zero inflated
Poisson GLM
in WinBUGS using artificial data
.56
2.9
Application of ZIP GLMM in WinBUGS using the owl data
.62
2.10
USING
DICTO
FIND THE OPTIMAL ZIP GLMM FOR THE OWL DATA
.64
2.11
zip
gamm
for 1-way nested data
.66
2.12
Whatto present in a paper?
.66
Contents
3
A
ROADMAP
FOR ANALYSIS OF OVERDISPERSED SANDEEL COUNTS IN SEAL SCAT
.67
3.1
sandeel otoliths and seal scat
. 67
3.2
Data exploration
.
68
3.3
GLM with
a Poisson
distribution
.
71
3.4
GLM WITH A NEGATIVE BINOMIAL DISTRIBUTION
.73
3.5
GAM WITH
A POISSON
DISTRIBUTION
.
75
3.6
GAM WITH A NEGATIVE BINOMIAL DISTRIBUTION
.76
3.6.1
Model and
R
code
.76
3.6.2
Model validation of the negative binomial GAM
.77
3.6.3
Model interpretation
.78
3.7
Zero inflated GAM with
a Poisson
distribution
.80
3.7.1
True and false zeros
.81
3.7.2
The ZIP model
.81
3.7.3
Result for the ZIP GAM
.83
3.7.4
Result for the ZINB GAM
.84
3.8
Final remarks
.87
3.9
Discussion
.88
3.10
whatto present in a paper?
.89
4
GLMM AND ZERO INFLATED
POISSON GLMM
APPLIED TO 2-WAY NESTED MARMOT DATA
.91
4.1
Introduction
.91
4.2
data exploration and visualisation
.93
4.2.1
Import the data and get a first impression
.93
4.2.2
Zero inflation in the response variable
.94
4.2.3
Number of missing values
.95
4.2.4
Outliers
.96
4.2.5
Collinearity
.96
4.2.6
Visualisation of relationships between number of young and the covariates
.98
4.3
WHAT MAKES THIS A DIFFICULT ANALYSIS?
.101
4.4
WHICH STATISTICAL TECHNIQUES AND SOFTWARE TO APPLY?
.102
4.5
SHORT DESCRIPTION OF THE STATISTICAL METHODOLOGY
.103
4.5.1
Viewing it (wrongly) as a Gaussian linear mixed effects model
.103
4.5.2
Viewing the data (potentially wrongly) as
a Poisson GLM
or GLMM
.106
4.5.3
Viewing the data (potentially wrong) as a zero inflated
Poisson GLM
.107
4.5.4
Viewing the data (possibly correct) as a 2-way nested ZIP GLMM
.108
4.6
Dealing with
25
covariates in a 2-way nested ZIP GLMM
.109
4.7
WinBUGS and
R
code for
Poisson GLM,
GLMM, and ZIP models
.112
4.7.1
WinBUGS code for
a Poisson GLM
.112
4.7.2
WinBUGS code for 1-way and 2-way nested
Poisson
GLMMs
.116
4.7.3
WinBUGS code for ZIP GLM
.121
4.7.4
WinBUGS code for 2-way nested ZIP
Polsson
GLMMs
.126
4.8
Validating the 2-way nested ZIP GLMM
.128
4.9
Interpretation of the 2-way nested ZIP GLMM model
.133
4.10
Discussion
.134
4.11
What
то
present in a paper?
.136
Appendix A: Correlation between observations in GLMMs*
.137
A.I Correlations in
a Poisson
GLMM for 1-way nested data*
.137
A.2 Correlations in
a Poisson GLMM
for 2-way nested data*
.138
A.3 Correlations in a binomial GLMM for 1-way nested data*
.140
A.4 Correlations in a GLMM binomial model for 2-way nested data*
.142
A.
5
Correlations in a ZIP model for 1-way nested data*
.144
A.6 Correlations in a ZIP model for 2-way nested data*
.145
A.7 Non-technical summary of the appendix
.145
A.8 Example of correlations for the 2-way nested ZIP GLMM*
.146
XI
5
TWO-STAGE
GAMM
APPLIED TO ZERO INFLATED COMMON
MURRE
DENSITY DATA
.149
5.1
Introduction
.149
5.2
Sampling
.149
5.3
Covariates
.153
5.4
Data exploration
.154
5.4.1
Potential outliers in the bird data
.154
5.4.2
Zero inflation of the bird data
.154
5.4.3
Outliers in the covariates
.155
5.4.4
Collinearity of the covariates
.156
5.4.5
Visualisation of relationships between birds and covariates
.159
5.4.6
Cruise and transect effects
.161
5.4.7
Summary of the data exploration
.162
5.5
GAMM
FOR ZERO INFLATED AND CORRELATED DENSITIES
.163
5.5.1
Brainstorming
.164
5.5.2
Four model selection approaches
.165
5.6
Results of the full model approach
.165
5.6.1
Results of the presence/absence
GAMM
.165
5.6.2
More detailed output for the binomial
GAMM
.167
5.6.3
Post-hoc testing
.168
5.5.4
Validation of the binomial
GAMM
.170
5.5.5
Analysis of the presence-only data using a gamma
GAMM
.172
5.7
Fitting a Gamma
GAMM
with CAR correlation in WinBUGS**
.173
5.7.1
Fitting a Gamma GLM in WinBUGS**
.174
5.7.2
Fitting a ZIP GAM on the Common
Murre
counts in WinBUGS**
.176
5.7.3
Fitting a ZIP GAM with residual CAR in WinBUGS**
.180
5.8
Discussion
.181
5.9
What
то
present in a paper?
.182
6
ZERO INFLATED SPATIALLY CORRELATED COASTAL SKATE DATA
.183
6.1
Introduction
.183
6.2
Importing the data and data coding
.183
6.3
Data exploration
.184
6.3.1
Outliers
.184
6.3.2
Collinearity
.185
6.3.3
Relationships
.187
6.3.4
Geographical position of the sites
.188
6.3.5
Zero inflation in the species data
.189
6.3.6
What makes this a difficult analysis?
.190
6.4
Zero inflated GLM applied
то
R.
agassizi data
.191
6.4.1
What is the starting point?
.191
6.4.2
Poisson
GLM applied to R. agassizi data
.191
6.4.3
NB GLM applied to R. agassizi data
.192
6.4.4
Zero inflated
Poisson
GLM applied to R. agassizi data
.193
6.4.5
Zero inflated negative binomial GLM applied to R. agassizi data
.196
6.4.6
Model validation of the ZIP GLM applied to R. agassizi data
.201
6.5
Zero inflated GLM applied to A. castelnaui data
.203
6.6
ADDING SPATIAL CORRELATION TO A GLM MODEL
.206
6.6.1
Who are the neighbours?
.206
6.6.2
Neighbouring sites
.210
6.6.3
The CAR model*
.210
6.6.4
Applying CAR to simulated spatially correlated data
.212
6.7
Analysis of species
3:
Sympterygia
bonapart»
.218
6.7.1
Poisson
or negative binomial distribution?
.218
6.7.2
Adding spatial correlation to the zero inflated
Poisson GLM
.218
6.7.3
Stetting up the required matrices for car.proper
.219
6.7.4
Preparing MCMC code for a ZIP with residual CAR correlation
.222
ХП
Contents
6.7.5
МСМС
results for ZIP with
residual
CAR structure
.225
6.8
Discussion
.229
6.9
whatto present in a paper?
. 230
7
ZERO
INFLATED
GLMS
WITH
SPATIAL CORRELATION-
ANALYSIS OF
PARROTFISH
ABUNDANCE
.231
7.1
Introduction
.231
7.2
The data
.232
7.2.1
Surveying
.232
7.2.2
Response variable
.234
7.2.3
Covariates
.234
7.3
ANALYSIS OF THE ZERO INFLATED DATA IGNORING THE CORRELATION
.238
7.3.1
Poisson
and NB GLMs
.238
7.3.2
ZIP and ZINBGLMs
.239
7.3.3
Model selection for ZINB
.243
7.3.4
Independence
.244
7.3.5
Independence
-
model misspecification
.245
7.3.6
Adding year to the ZINB?
.245
7.3.7
Dive effect on the residuals
.246
7.3.8
Adjusting the sample variogram for land between transects
.246
7.3.9
Which model to choose?
.249
7.4
ZERO INFLATED MODELS WITH A RANDOM INTERCEPT FOR DIVE
.250
7.5
ZERO INFLATED MODELS WITH SPATIAL CORRELATION
.250
7.5.1
Adding a residual correlation structure to the ZINB
.250
7.5.2
ZINB with a residual CAR correlation in
R
.251
7.5.3
МСМС
results
.253
7.6
Predictions
.254
7.7
Discussion
.255
7.8
What
то
write in a paper
.255
8
ANALYSIS OF ZERO INFLATED CLICK-BEETLE DATA
.257
8.1
Introduction
.257
8.2
The setup of the experiment
.257
8.3
Importing data and coding
.258
8.4
Data exploration
.259
8.4.1
Spatial position of the sites
.259
8.4.2
Response and explanatory variables
.260
8.4.3
Viewing the data as time series
.261
8.4.4
Spatial and temporal patterns
.262
8.4.5
Big trouble
.263
8.4.6
Effects and interactions for the species data
.265
8.4.7
Zero inflation
.267
8.4.8
Direction
.267
8.4.9
Where to go
f
rom
here; zero inflated models?
.268
e.S.ZIPGLM
.268
8.6
zip glm without correlation for the species data
.269
8.7
results for the female-male data
.274
8.8
Discussion
.276
8.9
Whatto write in a paper?
.276
9
ZERO INFLATED GAM FOR TEMPORAL CORRELATED SPERM WHALE STRANDINGS TIME SERIES
.277
9.1
Introduction
.277
9.2
what makes this a difficult analysis?
.277
9.3
Importing and data coding
.278
9.4
Data exploration
.278
9.5
Poisson
GAM with a single smoother in WinBUGS
.280
9.5.1
Fitting
a Poisson
GAM using gam from mgcv
.280
9.5.2
Fitting the binomial GAM from Pierce
et al.
(2007).282
XIII
9.5.3
Knots
.283
9.5.4
Low rank thin plate splines
.284
9.5.5
Mathematics for low rank thin plate splines
.286
9.5.6
Bypassing the mathematics
.288
9.5.7
WinBUGS code for GAM
.289
9.5.8
Results
.290
9.6
Poisson
GAM with two smoothers in WinBUGS
.293
9.7ZERO inflated
Poisson
GAM in WinBUGS
.296
9.7.1
Justification for zero inflated models
.296
9.7.2
Underlying equations for the ZIP GAM
.297
9.7.3
R
code for a ZIP GAM
.298
9.7.4
Results for the ZIP GAM
.299
9.7.5
Model validation of the ZIP GAM
.301
9.8
Zero inflated
Poisson
GAM with temporal correlation in WinBUGS
.302
9.8.1
CAR residual correlation in the ZIP GAM
.303
9.8.2
Auto-regressive residual correlation in the ZIP GAM
.306
9.9
Discussion
.308
9.10
Whatto write in a paper?
.308
10
EPILOGUE
.309
10.1
An excessive number of zeros does not mean zero inflation
.309
10.2
do we need false zeros in order to apply mixture models?
.311
10.3
were the false zeros in our examples really false?
.311
10.4
does the algorithm know which zeros are false?
.312
REFERENCES
.315
INDEX
.321 |
| any_adam_object | 1 |
| author | Zuur, Alain F. Saveliev, Anatoly A. Ieno, Elena N. |
| author_facet | Zuur, Alain F. Saveliev, Anatoly A. Ieno, Elena N. |
| author_role | aut aut aut |
| author_sort | Zuur, Alain F. |
| author_variant | a f z af afz a a s aa aas e n i en eni |
| building | Verbundindex |
| bvnumber | BV040108752 |
| classification_rvk | ST 250 |
| classification_tum | DAT 307f MAT 620f |
| ctrlnum | (OCoLC)793925621 (DE-599)OBVAC08942638 |
| discipline | Informatik Mathematik |
| format | Book |
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| id | DE-604.BV040108752 |
| illustrated | Illustrated |
| indexdate | 2025-02-12T11:01:31Z |
| institution | BVB |
| isbn | 9780957174108 9780957174115 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024965154 |
| oclc_num | 793925621 |
| open_access_boolean | |
| owner | DE-703 DE-11 DE-20 DE-188 DE-M49 DE-BY-TUM |
| owner_facet | DE-703 DE-11 DE-20 DE-188 DE-M49 DE-BY-TUM |
| physical | XVIII, 324 S. Ill., graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | Highland Statistics |
| record_format | marc |
| spelling | Zuur, Alain F. Verfasser aut Zero inflated models and generalized linear mixed models with R Alain F. Zuur ; Anatoly A. Savaliev ; Elena N. Ieno Newburgh Highland Statistics 2012 XVIII, 324 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier R Programm (DE-588)4705956-4 gnd rswk-swf Gemischtes Modell (DE-588)4156565-4 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Statistik (DE-588)4056995-0 s R Programm (DE-588)4705956-4 s DE-604 Statistische Analyse (DE-588)4116599-8 s Gemischtes Modell (DE-588)4156565-4 s Saveliev, Anatoly A. Verfasser aut Ieno, Elena N. Verfasser aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024965154&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zuur, Alain F. Saveliev, Anatoly A. Ieno, Elena N. Zero inflated models and generalized linear mixed models with R R Programm (DE-588)4705956-4 gnd Gemischtes Modell (DE-588)4156565-4 gnd Statistische Analyse (DE-588)4116599-8 gnd Statistik (DE-588)4056995-0 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4156565-4 (DE-588)4116599-8 (DE-588)4056995-0 |
| title | Zero inflated models and generalized linear mixed models with R |
| title_auth | Zero inflated models and generalized linear mixed models with R |
| title_exact_search | Zero inflated models and generalized linear mixed models with R |
| title_full | Zero inflated models and generalized linear mixed models with R Alain F. Zuur ; Anatoly A. Savaliev ; Elena N. Ieno |
| title_fullStr | Zero inflated models and generalized linear mixed models with R Alain F. Zuur ; Anatoly A. Savaliev ; Elena N. Ieno |
| title_full_unstemmed | Zero inflated models and generalized linear mixed models with R Alain F. Zuur ; Anatoly A. Savaliev ; Elena N. Ieno |
| title_short | Zero inflated models and generalized linear mixed models with R |
| title_sort | zero inflated models and generalized linear mixed models with r |
| topic | R Programm (DE-588)4705956-4 gnd Gemischtes Modell (DE-588)4156565-4 gnd Statistische Analyse (DE-588)4116599-8 gnd Statistik (DE-588)4056995-0 gnd |
| topic_facet | R Programm Gemischtes Modell Statistische Analyse Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024965154&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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