Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan.

Front Cover -- Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan -- Copyright -- Contents -- Digital Assets -- Acknowledgments -- 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 -- FURTHER READING -- Chapter 2 - Prerequisites and Vocabulary -- 2.1 SOFTWARE -- 2.2 IMPORTANT STATISTICAL TERMS AND HOW TO HANDLE THEM IN R -- FURTHER READING -- 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 -- FURTHER READING -- Chapter 4 - Normal Linear Models -- 4.1 LINEAR REGRESSION -- 4.2 REGRESSION VARIANTS: ANOVA, ANCOVA, AND MULTIPLE REGRESSION -- FURTHER READING -- Chapter 5 - Likelihood -- 5.1 THEORY -- 5.2 THE MAXIMUM LIKELIHOOD METHOD -- 5.3 THE LOG POINTWISE PREDICTIVE DENSITY -- FURTHER READING -- 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 -- FURTHER READING -- 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 -- FURTHER READING -- 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 -- FURTHER READING -- Chapter 9 - Generalized Linear Mixed Models -- 9.1 BINOMIAL MIXED MODEL.

9.2 POISSON MIXED MODEL -- FURTHER READING -- Chapter 10 - Posterior Predictive Model Checking and Proportion of Explained Variance -- 10.1 POSTERIOR PREDICTIVE MODEL CHECKING -- 10.2 MEASURES OF EXPLAINED VARIANCE -- FURTHER READING -- 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 -- FURTHER READING -- 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 -- FURTHER READING -- Chapter 13 - Modeling Spatial Data Using GLMM -- 13.1 BACKGROUND -- 13.2 MODELING ASSUMPTIONS -- 13.3 EXPLICIT MODELING OF SPATIAL AUTOCORRELATION -- FURTHER READING -- 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 -- FURTHER READING -- Chapter 15 - Prior Influence and Parameter Estimability -- 15.1 HOW TO SPECIFY PRIOR DISTRIBUTIONS -- 15.2 PRIOR SENSITIVITY ANALYSIS -- 15.3 PARAMETER ESTIMABILITY -- FURTHER READING -- Chapter 16 - Checklist -- 16.1 DATA ANALYSIS STEP BY STEP -- FURTHER READING -- 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 -- FURTHER READING -- References -- Index.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.