07100cam a2200757Ii 4500 ocn906699032 OCoLC 20190328114810.0 m o d cr cnu|||unuuu 150407s2015 ne a ob 001 0 eng d N$T eng rda pn N$T N$T IDEBK OPELS E7B YDXCP COO CDX CHVBK EBLCP DEBSZ FEM OCLCO IDB Z5A OCLCQ MERUC OCLCQ WRM U3W D6H OCLCF RRP OCLCQ WYU OCLCA MERER OCLCQ 908100768 926105501 968096815 1066538951 9780128016787 (electronic bk.) 0128016787 (electronic bk.) 0128013702 9780128013700 9780128013700 (OCoLC)906699032 (OCoLC)908100768 (OCoLC)926105501 (OCoLC)968096815 (OCoLC)1066538951 QH541.15.S72 QH541.15.S72 K67 2015 NAT 010000 bisacsh NAT 045040 bisacsh SCI 026000 bisacsh SCI 020000 bisacsh 577.01/5195 23 Korner-Nievergelt, Fr�anzi, author. Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / [electronic resource] Fr�anzi Korner-Nievergelt [and five others]. Amsterdam ; Boston : Academic Press, an imprint of Elsevier, [2015] 1 online resource : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier text file rda Online resource; title from PDF title page (Ebsco, viewed April 9, 2015). Includes bibliographical references and index. 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. 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 WAYSFURTHER 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 Models7.1 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 MODELFURTHER 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 STAN12.4 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. BUGS (Information storage and retrieval system) BUGS (Information storage and retrieval system) fast (OCoLC)fst01897696 Ecology Research Statistical methods. Bayesian statistical decision theory. R (Computer program language) NATURE Ecology. bisacsh NATURE Ecosystems & Habitats Wilderness. bisacsh SCIENCE Environmental Science. bisacsh SCIENCE Life Sciences Ecology. bisacsh Bayesian statistical decision theory. fast (OCoLC)fst00829019 R (Computer program language) fast (OCoLC)fst01086207 �Okologie gnd (DE-588)4043207-5 Datenverarbeitung gnd (DE-588)4011152-0 Bayes-Verfahren gnd (DE-588)4204326-8 Biostatistik gnd (DE-588)4729990-3 R Programm gnd (DE-588)4705956-4 Gibbs-sampling gnd (DE-588)4352359-6 Bayes Theorem. Linear Models. Electronic books. Electronic book. Print version: Bayesian data analysis in ecology using linear models with R, BUGS, and Stan. Amsterdam, [Netherlands] : Academic Press, �2015 xii, 316 pages 9780128013700 ScienceDirect http://www.sciencedirect.com/science/book/9780128013700 247071 247071