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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data.
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.
- Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest
- Written in a step-by-step approach that allows for eased understanding by non-statisticians
- Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data
- All example data as well as additional functions are provided in the R-package blmeco
Graduate students and professionals in ecology, biogeography, and biology
- Digital Assets
- Chapter 1. Why do we Need Statistical Models and What is this Book About?
- 1.1. Why We Need Statistical Models
- 1.2. What This Book is About
- Chapter 2. Prerequisites and Vocabulary
- 2.1. Software
- 2.2. Important Statistical Terms and How to Handle Them in R
- Chapter 3. The Bayesian and the Frequentist Ways of Analyzing Data
- 3.1. Short Historical Overview
- 3.2. The Bayesian Way
- 3.3. The Frequentist Way
- 3.4. Comparison of the Bayesian and the Frequentist Ways
- Chapter 4. Normal Linear Models
- 4.1. Linear Regression
- 4.2. Regression Variants: ANOVA, ANCOVA, and Multiple Regression
- Chapter 5. Likelihood
- 5.1. Theory
- 5.2. The Maximum Likelihood Method
- 5.3. The Log Pointwise Predictive Density
- Chapter 6. Assessing Model Assumptions: Residual Analysis
- 6.1. Model Assumptions
- 6.2. Independent and Identically Distributed
- 6.3. The QQ Plot
- 6.4. Temporal Autocorrelation
- 6.5. Spatial Autocorrelation
- 6.6. Heteroscedasticity
- Chapter 7. Linear Mixed Effects Models
- 7.1. Background
- 7.2. Fitting a Linear Mixed Model in R
- 7.3. Restricted Maximum Likelihood Estimation
- 7.4. Assessing Model Assumptions
- 7.5. Drawing Conclusions
- 7.6. Frequentist Results
- 7.7. Random Intercept and Random Slope
- 7.8. Nested and Crossed Random Effects
- 7.9. Model Selection in Mixed Models
- Chapter 8. Generalized Linear Models
- 8.1. Background
- 8.2. Binomial Model
- 8.3. Fitting a Binary Logistic Regression in R
- 8.4. Poisson Model
- Chapter 9. Generalized Linear Mixed Models
- 9.1. Binomial Mixed Model
- 9.2. Poisson Mixed Model
- Chapter 10. Posterior Predictive Model Checking and Proportion of Explained Variance
- 10.1. Posterior Predictive Model Checking
- 10.2. Measures of Explained Variance
- Chapter 11. Model Selection and Multimodel Inference
- 11.1. When and Why We Select Models and Why This is Difficult
- 11.2. Methods for Model Selection and Model Comparisons
- 11.3. Multimodel Inference
- 11.4. Which Method to Choose and Which Strategy to Follow
- Chapter 12. Markov Chain Monte Carlo Simulation
- 12.1. Background
- 12.2. MCMC Using BUGS
- 12.3. MCMC Using Stan
- 12.4. Sim, BUGS, and Stan
- Chapter 13. Modeling Spatial Data Using GLMM
- 13.1. Background
- 13.2. Modeling Assumptions
- 13.3. Explicit Modeling of Spatial Autocorrelation
- Chapter 14. Advanced Ecological Models
- 14.1. Hierarchical Multinomial Model to Analyze Habitat Selection Using BUGS
- 14.2. Zero-Inflated Poisson Mixed Model for Analyzing Breeding Success Using Stan
- 14.3. Occupancy Model to Measure Species Distribution Using Stan
- 14.4. Territory Occupancy Model to Estimate Survival Using BUGS
- 14.5. Analyzing Survival Based on Mark-Recapture Data Using Stan
- Chapter 15. Prior Influence and Parameter Estimability
- 15.1. How to Specify Prior Distributions
- 15.2. Prior Sensitivity Analysis
- 15.3. Parameter Estimability
- Chapter 16. Checklist
- 16.1. Data Analysis Step by Step
- Chapter 17. What Should I Report in a Paper
- 17.1. How to Present the Results
- 17.2. How to Write Up the Statistical Methods
- No. of pages:
- © Academic Press 2015
- 4th April 2015
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Fränzi Korner-Nievergelt has been working as a statistical consultant since 2003. Dr. Korner-Nievergelt conducts research in ecology and ecological statistics at the Swiss Ornithological Institute and oikostat GmbH. Additionally, she provides data analyses for scientific projects in the public and private sector. A large part of her work involves teaching courses for scientists at scientific institutions and private organizations.
Ecological Statistician, oikostat GmbH and Swiss Ornithological Institute, Switzerland
Tobias Roth is a postdoc at the University of Basel where he teaches masters level courses in statistics for ecology and biology students. In addition, Dr. Tobias Roth is co-owner and project manager at Hintermann & Weber AG, where he is responsible for data analyses and develops analytical methods for biodiversity monitoring programs.
Ecological Statistician, University of Basel and Hintermann & Weber AG, Switzerland
Stefanie von Felten has a PhD in Plant Ecology and a diploma of advanced studies in statistics. Since 2010 she works as statistician at the University Hospital Basel where she is involved in planning, analysis and publication of clinical studies. In addition, Dr. von Felten is a statistical consultant for oikostat GmbH. She has been teaching statistics in several courses for Master and PhD students at various academic institutions and for doctors and other health personnel at the Hospital.
Statistician, oikostat GmbH and Clinical Trial Unit, University Hospital Basel, Switzerland
Jérôme Guélat has been leading the GIS team at the Swiss Ornithological Institute for more than 6 years. He uses spatial statistics to provide guidance to applied conservation problems. He also teaches a short course on spatial and Bayesian statistics.
Geographic Information Systems, Swiss Ornithological Institute
Bettina Almasi has a PhD in eco-physiology and ecology from the University of Zurich and a post-diploma course in applied statistics from the ETH Zurich. Dr. Almasi conducts research in stress physiology and behavioural ecology at the Swiss Ornithological Institute and works part-time as a statistical consultant at oikostat GmbH
Research Ecologist and Statistical Consultant, Swiss Ornithological Institute and oikostat GmbH, Switzerland
Pius Korner-Nievergelt has a PhD in ecology, conservation biology and a post-diploma course in applied statistics both from ETH Zurich. Dr. Korner-Nievergelt works as a statistician at oikostat GmbH as well as at the Swiss Ornithological Institute for data analyses, mainly regarding ecological questions.
Biologist and Statistical Consultant, oikostat GmbH and Swiss Ornithological Institute, Switzerland
"...an excellent statistical toolbox book that provides examples of ecological analyses that increase in complexity using frequentist and Bayesian methods...it will have a permanent place on many bookshelves, including mine..." --The Journal of Wildlife Management
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