| Titre : |
Introduction to WinBUGS for ecologists : a Bayesian approach to regression, ANOVA, mixed models and related analyses |
| Type de document : |
Ouvrage |
| Auteurs : |
Marc Kéry, Auteur |
| Editeur : |
London ; New York : Elsevier |
| Année de publication : |
2010 |
| Importance : |
302 p |
| ISBN/ISSN/EAN : |
978-0-12-378605-0 |
| Langues : |
Anglais (eng) |
| Catégories : |
[Motbis 2025] analyse statistique
[Motbis 2025] écologie
[Motbis 2025] statistique
|
| Index. décimale : |
DC-0 Généralités |
| Note de contenu : |
1. Introduction
1.1 Advantages of the Bayesian Approach to Statistics
1.2 So Why Then Isn’t Everyone a Bayesian?
1.3 WinBUGS
1.4 Why This Book?
1.5 What This Book Is Not About: Theory of Bayesian Statistics and Computation
1.6 Further Reading
1.7 Summary
2. Introduction to the Bayesian Analysis of a Statistical Model
2.1 Probability Theory and Statistics
2.2 Two Views of Statistics: Classical and Bayesian
2.3 The Importance of Modern Algorithms and Computers for Bayesian Statistics
2.4 Markov chain Monte Carlo (MCMC) and Gibbs Sampling
2.5 What Comes after MCMC?
2.6 Some Shared Challenges in the Bayesian and the Classical Analysis of a Statistical Model
2.7 Pointer to Special Topics in This Book
2.8 Summary
3. WinBUGS
3.1 What Is WinBUGS?
3.2 Running WinBUGS from R
3.3 WinBUGS Frees the Modeler in You
3.4 Some Technicalities and Conventions
4. A First Session in WinBUGS: The “Model of the Mean”
4.1 Introduction
4.2 Setting Up the Analysis
4.3 Starting the MCMC blackbox
4.4 Summarizing the Results
4.5 Summary
5. Running WinBUGS from R via R2WinBUGS
5.1 Introduction
5.2 Data Generation
5.3 Analysis Using R
5.4 Analysis Using WinBUGS
5.5 Summary
6. Key Components of (Generalized) Linear Models: Statistical Distributions and the Linear Predictor
6.1 Introduction
6.2 Stochastic Part of Linear Models: Statistical Distributions
6.3 Deterministic Part of Linear Models: Linear Predictor and Design Matrices
6.4 Summary
7. t-Test: Equal and Unequal Variances
7.1 t-Test with Equal Variances
7.2 t-Test with Unequal Variances
7.3 Summary and a Comment on the Modeling of Variances
8. Normal Linear Regression
8.1 Introduction
8.2 Data Generation
8.3 Analysis Using R
8.4 Analysis Using WinBUGS
8.5 Summary
9. Normal One-Way ANOVA
9.1 Introduction: Fixed and Random Effects
9.2 Fixed-Effects ANOVA
9.3 Random-Effects ANOVA
9.4 Summary
10. Normal Two-Way ANOVA
10.1 Introduction: Main and Interaction Effects
10.2 Data Generation
10.3 Aside: Using Simulation to Assess Bias and Precision of an Estimator
10.4 Analysis Using R
10.5 Analysis Using WinBUGS
10.6 Summary
11. General Linear Model (ANCOVA)
11.1 Introduction
11.2 Data Generation
11.3 Analysis Using R
11.4 Analysis Using WinBUGS (and a Cautionary Tale About the Importance of Covariate Standardization)
11.5 Summary
12. Linear Mixed-Effects Model
12.1 Introduction
12.2 Data Generation
12.3 Analysis Under a Random-Intercepts Model
12.4 Analysis Under a Random-Coefficients Model without Correlation between Intercept and Slope
12.5 The Random-Coefficients Model with Correlation between Intercept and Slope
12.6 Summary
13. Introduction to the Generalized Linear Model: Poisson “t-test”
13.1 Introduction
13.2 An Important but Often Forgotten Issue with Count Data
13.3 Data Generation
13.4 Analysis Using R
13.5 Analysis Using WinBUGS
13.6 Summary
14. Overdispersion, Zero-Inflation, and Offsets in the GLM
14.1 Overdispersion
14.2 Zero-Inflation
14.3 Offsets
14.4 Summary
15. Poisson ANCOVA
15.1 Introduction
15.2 Data Generation
15.3 Analysis Using R
15.4 Analysis Using WinBUGS
15.5 Summary
16. Poisson Mixed-Effects Model (Poisson GLMM)
16.1 Introduction
16.2 Data Generation
16.3 Analysis Under a Random-Coefficients Model
16.4 Summary
17. Binomial “t-Test”
17.1 Introduction
17.2 Data Generation
17.3 Analysis Using R
17.4 Analysis Using WinBUGS
17.5 Summary
18. Binomial Analysis of Covariance
18.1 Introduction
18.2 Data Generation
18.3 Analysis Using R
18.4 Analysis Using WinBUGS
18.5 Summary
19. Binomial Mixed-Effects Model (Binomial GLMM)
19.1 Introduction
19.2 Data Generation
19.3 Analysis Under a Random-Coefficients Model
19.4 Summary
20. Nonstandard GLMMs 1: Site-Occupancy Species Distribution Model
20.1 Introduction
20.2 Data Generation
20.3 Analysis Using WinBUGS
20.4 Summary
21. Nonstandard GLMMs 2: Binomial Mixture Model to Model Abundance
21.1 Introduction
21.2 Data Generation
21.3 Analysis Using WinBUGS
21.4 Summary
|
Introduction to WinBUGS for ecologists : a Bayesian approach to regression, ANOVA, mixed models and related analyses [Ouvrage] / Marc Kéry, Auteur . - London ; New York : Elsevier, 2010 . - 302 p. ISBN : 978-0-12-378605-0 Langues : Anglais ( eng)
| Catégories : |
[Motbis 2025] analyse statistique
[Motbis 2025] écologie
[Motbis 2025] statistique
|
| Index. décimale : |
DC-0 Généralités |
| Note de contenu : |
1. Introduction
1.1 Advantages of the Bayesian Approach to Statistics
1.2 So Why Then Isn’t Everyone a Bayesian?
1.3 WinBUGS
1.4 Why This Book?
1.5 What This Book Is Not About: Theory of Bayesian Statistics and Computation
1.6 Further Reading
1.7 Summary
2. Introduction to the Bayesian Analysis of a Statistical Model
2.1 Probability Theory and Statistics
2.2 Two Views of Statistics: Classical and Bayesian
2.3 The Importance of Modern Algorithms and Computers for Bayesian Statistics
2.4 Markov chain Monte Carlo (MCMC) and Gibbs Sampling
2.5 What Comes after MCMC?
2.6 Some Shared Challenges in the Bayesian and the Classical Analysis of a Statistical Model
2.7 Pointer to Special Topics in This Book
2.8 Summary
3. WinBUGS
3.1 What Is WinBUGS?
3.2 Running WinBUGS from R
3.3 WinBUGS Frees the Modeler in You
3.4 Some Technicalities and Conventions
4. A First Session in WinBUGS: The “Model of the Mean”
4.1 Introduction
4.2 Setting Up the Analysis
4.3 Starting the MCMC blackbox
4.4 Summarizing the Results
4.5 Summary
5. Running WinBUGS from R via R2WinBUGS
5.1 Introduction
5.2 Data Generation
5.3 Analysis Using R
5.4 Analysis Using WinBUGS
5.5 Summary
6. Key Components of (Generalized) Linear Models: Statistical Distributions and the Linear Predictor
6.1 Introduction
6.2 Stochastic Part of Linear Models: Statistical Distributions
6.3 Deterministic Part of Linear Models: Linear Predictor and Design Matrices
6.4 Summary
7. t-Test: Equal and Unequal Variances
7.1 t-Test with Equal Variances
7.2 t-Test with Unequal Variances
7.3 Summary and a Comment on the Modeling of Variances
8. Normal Linear Regression
8.1 Introduction
8.2 Data Generation
8.3 Analysis Using R
8.4 Analysis Using WinBUGS
8.5 Summary
9. Normal One-Way ANOVA
9.1 Introduction: Fixed and Random Effects
9.2 Fixed-Effects ANOVA
9.3 Random-Effects ANOVA
9.4 Summary
10. Normal Two-Way ANOVA
10.1 Introduction: Main and Interaction Effects
10.2 Data Generation
10.3 Aside: Using Simulation to Assess Bias and Precision of an Estimator
10.4 Analysis Using R
10.5 Analysis Using WinBUGS
10.6 Summary
11. General Linear Model (ANCOVA)
11.1 Introduction
11.2 Data Generation
11.3 Analysis Using R
11.4 Analysis Using WinBUGS (and a Cautionary Tale About the Importance of Covariate Standardization)
11.5 Summary
12. Linear Mixed-Effects Model
12.1 Introduction
12.2 Data Generation
12.3 Analysis Under a Random-Intercepts Model
12.4 Analysis Under a Random-Coefficients Model without Correlation between Intercept and Slope
12.5 The Random-Coefficients Model with Correlation between Intercept and Slope
12.6 Summary
13. Introduction to the Generalized Linear Model: Poisson “t-test”
13.1 Introduction
13.2 An Important but Often Forgotten Issue with Count Data
13.3 Data Generation
13.4 Analysis Using R
13.5 Analysis Using WinBUGS
13.6 Summary
14. Overdispersion, Zero-Inflation, and Offsets in the GLM
14.1 Overdispersion
14.2 Zero-Inflation
14.3 Offsets
14.4 Summary
15. Poisson ANCOVA
15.1 Introduction
15.2 Data Generation
15.3 Analysis Using R
15.4 Analysis Using WinBUGS
15.5 Summary
16. Poisson Mixed-Effects Model (Poisson GLMM)
16.1 Introduction
16.2 Data Generation
16.3 Analysis Under a Random-Coefficients Model
16.4 Summary
17. Binomial “t-Test”
17.1 Introduction
17.2 Data Generation
17.3 Analysis Using R
17.4 Analysis Using WinBUGS
17.5 Summary
18. Binomial Analysis of Covariance
18.1 Introduction
18.2 Data Generation
18.3 Analysis Using R
18.4 Analysis Using WinBUGS
18.5 Summary
19. Binomial Mixed-Effects Model (Binomial GLMM)
19.1 Introduction
19.2 Data Generation
19.3 Analysis Under a Random-Coefficients Model
19.4 Summary
20. Nonstandard GLMMs 1: Site-Occupancy Species Distribution Model
20.1 Introduction
20.2 Data Generation
20.3 Analysis Using WinBUGS
20.4 Summary
21. Nonstandard GLMMs 2: Binomial Mixture Model to Model Abundance
21.1 Introduction
21.2 Data Generation
21.3 Analysis Using WinBUGS
21.4 Summary
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