A beginner's guide to generalised additive mixed models with R:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Newburgh, United Kingdom
Highland Statistics Ltd
2014
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | Aus dem Vorwort: All datasets used are downloadable from www.highstat.com/BGGAM.htm. All R code can also be downloaded from the website for this book. |
| Beschreibung: | xvi, 332 Seiten Illustrationen, Diagramme |
| ISBN: | 9780957174160 9780957174153 |
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Datensatz im Suchindex
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vii Contents Preface. v Acknowledgements. v Datasets used in this book. vi Chapter 1 of Zuur et al. (2012a) and Zuur (2012b).vi Cover art. vi Contributors. XIII 1 INTRODUCTION. 1 1.1 GAM APPLIED ON STABLE ISOTOPE RATIOS. 1 1.2 GAM USING MGCV APPLIED ON THE SQUID DATA. 5 1.3 Linear spline regression. 7 1.4 Linear spline regression in JAGS. 13 1.5 В-Splines in JAGS. 17 1.6 Low RANK THIN-PLATE REGRESSION SPLINES IN JAGS.19 1.6.1 Using Ime to estimate the low rank thin-plate regression spline. 22 1.6.2 Using JAGS to estimate the low rank thin-plate regression
spline. 24 1.7 O’Sullivan splines inJAGS. 28 1.8 Effective degrees of freedom of a smoother. 30 2 ADDITIVE MIXED EFFECTS MODELS APPLIED ON POLAR BEAR MOVEMENT DATA. 35 2.1 Introduction. 35 2.2 The variables. 36 2.3 . Housekeeping.37 2.4 Data exploration.37 2.4.1 Checking for outliers in the movement data. 38 2.4.2 Relationships between movement and the covariates. 38 2.4.3 Collinearity. 44 2.5 Model formulation. 45 2.5.1 Distribution. 45 2.5.2 Predictorfunction. 46 2.5.3 Linkfunction. 48 2.6 Frequentist approach. 48 2.6.1 Fitting the additive mixed effects model using mgcv. 48 2.6.2 Model
validation of the additive mixed effects model.52 2.6.3 Understanding $lme outputfrom the gammfunction. 55 2.7 MCMC and Gaussian additive mixed effects models. 58 2.7.1 Data for JAGS. 58 2.7.2 JAGS modelling code. 60 2.7.3 Initial values. 62 2.7.4 Parameters to save. 62
viii 2.7.5 Executing JAGS and obtaining results. 62 2.7.6 Mixing of chains. 63 2.7.7 Model validation. 63 2.7.8 Model interpretation. 64 2.8 MCMC AND GAMMA GAMM. 67 2.8.1 Data forJAGS. 67 2.8.2 JAGS modelling code. 68 2.8.3 Initial values. 69 2.8.4 Parameters to save. 70 2.8.5 Executing JAGS and obtaining results. 70 2.8.6 Mixing of Chains. 70 2.8.7 Model validation. 70 2.8.8 Model interpretation. 71 2.9 DISCUSSION. 71 2.10 What to present in a paper. 72 3 ADDITIVE MIXED EFFECTS MODELS APPLIED ON CORAL REEF
DATA. 73 3.1 Introduction. 73 3.1.1 Coral reefs.73 3.1.2 Aim of this chapter. 73 3.2 The variables. 74 3.3 . Housekeeping. 74 3.4 Data exploration. 75 3.4.1 Checking for outliers. 75 3.4.2 Relationships. 76 3.4.3 Collinearity. 78 3.5 Model formulation. 79 3.6 Frequentist approach. 80 3.6.1 Linear mixed effects model using Ime. 80 3.6.2 Additive mixed effects model usinggamm. 82 3.6.3 Model validation of the additive mixed effects model. 86 3.6.4 Model interpretation. 86
3.7 MCMC and Gaussian additive mixed effects models.88 3.7.1 Data forJAGS. 89 3.7.2 JAGS modelling code. 90 3.7.3 Initial values. 92 3.7.4 Parameters to save. 92 3.7.5 Executing JAGS and obtaining results. 92 3.7.6 Mixing of Chains. . 93 3.7.7 Model validation. 93 3.7.8 Model interpretation.93 3.8 Discussion. 97 3.9 What to present in a paper.98 4 POISSON GAMM APPLIED ON RUDDY TURNSTONE DATA. 99 4.1 Group size effect on vigilance in ruddy turnstones. 99
ix 4.2 THE VARIABLES. 100 4.3 . Housekeeping. 100 4.4 Data exploration. 101 4.5 Poisson GAMM. 104 4.5.1 GLMM or GAMM?. 105 4.5.2 GAMMformulation. 105 4.5.3 Fitting GAMM using дапип4. 106 4.5.4 Estimated smoothers. 109 4.5.5 Model validation. 110 4.6 Using flock size as an offset?. 110 4.7 Unbalanced random effects; simulation study. Ill 4.8 Discussion. 114 5 GAMM APPLIED ON PARASITE DATA.115 5.1 Introduction. 115 5.2 The variables. 116 5.3 . Housekeeping. 116 5.4 Data
exploration. 117 5.4.1 Checking for missing values. 117 5.4.2 Checking for outliers. 117 5.4.3 Relationships. 118 5.4.4 Collinearity. 119 5.4.5 Zero inflation. 119 5.5 Model formulation.119 5.6 Poisson and negative binomial GLMM for total abundance. 121 5.6.1 Poisson GLMM using lme4. 121 5.6.2 Negative binomial GAMM using JAGS. 125 5.6.3 Negative binomial-P GLMs and GAMMs. 136 5.7 Generalised Poisson GAMM for underdispersed species richness 142 5.8 Binomial GAMM forabsence/presence data. 144 6 ZERO-INFLATED SEA BIRD DATA SAMPLED AT OFFSHORE WINDFARMS. 145 6.1 Common guillemots. 145 6.2 The data. . 146 6.2.1 Importing the data. 146 6.2.2 Recoding of
variables. 146 6.3 Loading the required packages. 148 6.4 Data exploration. 148 6.5 Building towards a model. 153 6.5.1 Poisson GAM with a bivariate smoother. 153 6.5.2 Applying the Poisson GAM with a bivariate smoother.155 6.5.3 Poisson GAM with multiple bivariate smoothers.157 6.5.4 Poisson GAMM with multiple bivariate smoothers. 159 6.6 Zero-inflated Poisson GAMM with bivariate smoothers. 161
X 6.7 Zero-inflated negative binomial GAMM with bivariate smoothers 163 6.8 Technical details of fitting the two-way nested ZIP GAMM. 163 6.8.1 Underlying mixed effects model. 163 6.8.2 MCMC code for a two-dimensional smootherfor data from one survey. 166 6.9 Model selection using data from survey 1. 176 6.10 A model for all 13 surveys. 177 6.11 MCMC RESULTS FOR ALL 13 SURVEYS. 179 6.12 Adding indicator functions. 182 6.13 Model validation. 182 6.14 Discussion. 185 6.15 What to present in a paper. 186 7 ZERO-INFLATED GAMM APPLIED ON HARBOUR PORPOISE.187 7.1 Harbour porpoise. 187 7.2 Importing the data and housekeeping. 188 7.3 Data exploration. 189 7.3.1 Spatial and temporal sampling positions. 189 7.3.2 Outliers.192 7.3.3
Collinearity. 193 7.3.4 Relationships. 194 7.4 Brainstorming.196 7.4.1 Adding correlation to the model. 196 7.4.2 Specifying the fixed part of the models. 197 7.4.3 Mathematical formulation of the models. 198 7.5 ZIP GAMM using univariate smoothers. 199 7.5.1 Implementation of ZIP GAMM with univariate smoothers. 199 7.5.2 ZIP GAMM results.208 7.5.3 ZIP GLMM results.210 7.6 ZIP GAMM USING TWO-DIMENSIONAL SPATIAL SMOOTHERS. 210 7.6.1 Implementation ofZIP GAMM with multivariate smoother. 210 7.6.2 ZIP GAMM results. 211 Ί.Ί Discussion. 214 7.8 What to present in a paper. 214 8 GAMMA GAMM APPLIED ON TREE GROWTH DATA. 217 8.1 Introduction. 217 8.2 The variables. 218 8.3 .
Housekeeping. 218 8.4 Data exploration. 219 8.5 Model specification. 221 8.6 FREQUENTIST APPROACH.222 8.7 Bayesian approach.226 8.7.1 Schematic overview. 226 8.7.2 Data forJAGS. 226
XI 8.7.3 JAGS modelling code. 228 8.7.4 Initial values and parameters to save. 231 8.7.5 Running JAGS. 232 8.7.6 Assess mixing of chains and model fit. 232 8.7.7 JAGS results. 233 8.7.8 Model validation. 234 8.7.9 Heterogeneous gamma GAMM.237 8.8 Discussion. 238 8.9 What to present in a paper. 239 9 BERNOULLI GAMM APPLIED ON COWBIRD BROOD PARASITISM 241 9.1 Introduction. 241 9.2 The variables. 242 9.3 . Housekeeping. 243 9.4 Data exploration. 243 9.5 Model formulation. 245 9.6 Frequentist approach. 245 9.7 Bayesian approach. 247 9.7.1 Preparing the data
forJAGS.248 9.7.2 JAGS modelling code. 249 9.7.3 Running JAGS and mixing of chains. 251 9.7.4 Estimated smoothers and fitted values obtained by JAGS. 251 9.7.5 Differences between fitted values. 253 9.8 Discussion. 254 10 GAMM APPLIED ON MAXIMUM COD LENGTH USING INLA .255 10.1 Introduction. 255 10.2 The variables. 256 10.3 Data exploration.256 10.4 GAM IN MGCV. 259 10.5 Adding spatial correlation to the GAM. 261 10.6 Fitting a GAM using inla. 263 10.6.1 What is INLA?. 263 10.6.2 Installing INLA.265 10.6.3 Applying regression models in INLA.265 10.6.4 GAM in inla. 268 10.7 GAM WITH SPATIAL CORRELATION IN INLA.270 10.7.1 Defining a
mesh. 270 10.7.2 Projector matrix. 272 10.7.3 Setting up the model. 272 10.7.4 Executing inla. 273 10.7.5 Plotting the spatial random field. 274 10.7.6 Theoretical and estimated spatial correlation. 275 10.8 GAM WITH EXTREME VALUE DISTRIBUTION IN INLA. 278 10.8.1 Generalised extreme value distribution. 278 10.8.2 Applying gevmodels using the ismevpackage in R. 279
xii 10.8.3 Applying gevmodels using inla in R. 282 10.9 GAM WITH EXTREME VALUE DISTRIBUTION AND SPATIAL CORRELATION IN INLA. 283 10.10 Discussion. 284 10.11 What to present in a paper.284 11 ZERO-INFLATED AND SPATIAL CORRELATED COMMON SCOTER DATA. 285 11.1 Introduction.285 11.2 The variables.286 11.3 Data exploration. 286 11.3.1 Outliers. 286 11.3.2 Collinearity. 287 11.3.3 Relationships between response and covariates. 288 11.4 Poisson GLM. 291 11.5 Zero-inflated Poisson GLM using pscl and inla. 295 11.6 Zero-inflated Poisson GAM using inla. 298 11.7 ZIP GLM with spatial correlation using inla. 303 11.8 ZIP GAM with spatial correlation using
inla. 306 11.9 NB GLM WITH SPATIAL CORRELATION USING INLA. 308 11.10 THE EFFECT OF A NEW EXCLUSION ZONE. 311 11.11 Discussion. 317 11.12 What to present in a paper. 318 REFERENCES. 319 INDEX. 325 BOOKS BY HIGHLAND STATISTICS. 329 Upcoming books in 2014. 331 |
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| spelling | Zuur, Alain F. Verfasser (DE-588)1068021438 aut A beginner's guide to generalised additive mixed models with R Alain F. Zuur, Anatoly A Saveliev ; Elena N Ieno Newburgh, United Kingdom Highland Statistics Ltd 2014 xvi, 332 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Aus dem Vorwort: All datasets used are downloadable from www.highstat.com/BGGAM.htm. All R code can also be downloaded from the website for this book. Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Multi-level-Verfahren (DE-588)4344428-3 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 gnd rswk-swf R Programm (DE-588)4705956-4 s Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 s Multi-level-Verfahren (DE-588)4344428-3 s Regressionsanalyse (DE-588)4129903-6 s DE-604 Savelʹev, Anatolij A. Verfasser (DE-588)1026882206 aut Ieno, Elena N. Verfasser (DE-588)1068021616 aut V:DE-605 application/pdf http://digitale-objekte.hbz-nrw.de/storage2/2015/03/12/file_3/6047894.pdf Inhaltsverzeichnis Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030482307&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Zuur, Alain F. Savelʹev, Anatolij A. Ieno, Elena N. A beginner's guide to generalised additive mixed models with R Regressionsanalyse (DE-588)4129903-6 gnd Multi-level-Verfahren (DE-588)4344428-3 gnd R Programm (DE-588)4705956-4 gnd Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 gnd |
| subject_GND | (DE-588)4129903-6 (DE-588)4344428-3 (DE-588)4705956-4 (DE-588)4508520-1 |
| title | A beginner's guide to generalised additive mixed models with R |
| title_auth | A beginner's guide to generalised additive mixed models with R |
| title_exact_search | A beginner's guide to generalised additive mixed models with R |
| title_full | A beginner's guide to generalised additive mixed models with R Alain F. Zuur, Anatoly A Saveliev ; Elena N Ieno |
| title_fullStr | A beginner's guide to generalised additive mixed models with R Alain F. Zuur, Anatoly A Saveliev ; Elena N Ieno |
| title_full_unstemmed | A beginner's guide to generalised additive mixed models with R Alain F. Zuur, Anatoly A Saveliev ; Elena N Ieno |
| title_short | A beginner's guide to generalised additive mixed models with R |
| title_sort | a beginner s guide to generalised additive mixed models with r |
| topic | Regressionsanalyse (DE-588)4129903-6 gnd Multi-level-Verfahren (DE-588)4344428-3 gnd R Programm (DE-588)4705956-4 gnd Markov-Ketten-Monte-Carlo-Verfahren (DE-588)4508520-1 gnd |
| topic_facet | Regressionsanalyse Multi-level-Verfahren R Programm Markov-Ketten-Monte-Carlo-Verfahren |
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