rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
  name:
    name part: Korner-Nievergelt, Fränzi
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource : illustrations
  abstract: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  table of contents: 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.
  note: Fränzi Korner-Nievergelt [and five others].
  note: Includes bibliographical references and index.
  note: English.
  subject:
    title info:
      title: BUGS (Information storage and retrieval system)
  subject:
    title info:
      title: BUGS (Information storage and retrieval system)
  subject:
    topic: Ecology
    topic: Research
    topic: Statistical methods
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: R (Computer program language)
  subject:
    topic: Linear models (Statistics)
  subject:
    topic: Bayes Theorem
  subject:
    topic: Linear Models
  subject:
    topic: Écologie
    topic: Recherche
    topic: Méthodes statistiques
  subject:
    topic: Théorie de la décision bayésienne
  subject:
    topic: R (Langage de programmation)
  subject:
    topic: Théorème de Bayes
  subject:
    topic: Modèles linéaires (Statistique)
  subject:
    topic: NATURE
    topic: Ecology
  subject:
    topic: NATURE
    topic: Ecosystems & Habitats
    topic: Wilderness
  subject:
    topic: SCIENCE
    topic: Environmental Science
  subject:
    topic: SCIENCE
    topic: Life Sciences
    topic: Ecology
  subject:
    topic: Linear models (Statistics)
  subject:
    topic: Bayesian statistical decision theory
  subject:
    topic: R (Computer program language)
  subject:
    topic: Ökologie
  subject:
    topic: Datenverarbeitung
  subject:
    topic: Bayes-Verfahren
  subject:
    topic: Biostatistik
  subject:
    topic: R
  subject:
    topic: Gibbs-sampling
  subject:
    topic: ecologie
  subject:
    topic: ecology
  subject:
    topic: gegevensanalyse
  subject:
    topic: data analysis
  subject:
    topic: bayesiaanse theorie
  subject:
    topic: bayesian theory
  subject:
    topic: modellen
  subject:
    topic: models
  subject:
    topic: computer software
  subject:
    topic: Statistical Analysis
  subject:
    topic: Ecology (General)
  subject:
    topic: Statistische analyse
  subject:
    topic: Ecologie (algemeen)
  classification: QH541.15.S72
  classification: 577.01/5195
  related item:
    title info:
      title: Bayesian data analysis in ecology using linear models with R, BUGS, and Stan
    origin info:
      publisher: Amsterdam, [Netherlands] : Academic Press, ©2015
    physical description:
      extent: xii, 316 pages
  identifier: 9780128016787
  identifier: 0128016787
  identifier: 0128013702
  identifier: 9780128013700
  identifier: 2014957273
  identifier: 9027699791612325547 TotalBoox
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: N$T
    record creation date: 150407
    record change date: 20260407210828.7
    record identifier: in00005759050
    language of cataloging:
      language term: eng