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  • 1
    Online Resource
    Online Resource
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / (2015)
    Amsterdam, [Netherlands] :Academic Press,
    Details
    UID:
    almafu_9960073527302883
    Format: 1 online resource (329 p.)
    Edition: 1st ed.
    Content: 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.--
    Note: Description based upon print version of record. , 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 , 14.5 ANALYZING SURVIVAL BASED ON MARK-RECAPTURE DATA USING STAN , English
    Additional Edition: ISBN 0-12-801370-2
    Additional Edition: ISBN 0-12-801678-7
    Language: English
    Keywords: Electronic books.
    URL: Click to View
    Library Location Call Number Volume/Issue/Year Availability
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    Online Resource
    HU Berlin
    FU Berlin
    Charité
  • 2
    Book
    Book
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan (2015)
    Amsterdam [u.a.]. : Acad. Press, an imprint of Elsevier
    Details
    UID:
    gbv_823963055
    Format: XII, 316 S. , graph. Darst.
    ISBN: 0128013702 , 9780128013700
    Content: 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.--
    Note: Literaturverz. S. 297 - 307
    Additional Edition: Erscheint auch als Online-Ausgabe Bayesian data analysis in ecology using linear models with R, BUGS, and Stan Amsterdam : Academic Press, an imprint of Elsevier, 2015 ISBN 9780128016787
    Additional Edition: ISBN 0128016787
    Additional Edition: ISBN 0128013702
    Additional Edition: ISBN 9780128013700
    Language: English
    Subjects: Computer Science , Geography , Biology
    RVK:
    RB 10480
    RVK:
    ST 250
    RVK:
    WI 1500
    Keywords: Ökologie ; Datenanalyse
    URL: Verlagsangaben
    URL: Inhaltsverzeichnis
    URL: http://www.sciencedirect.com/science/book/9780128013700
    Library Location Call Number Volume/Issue/Year Availability
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    Book
    UB Potsdam get availability status
    Müncheberg ZALF get availability status
  • 3
    Online Resource
    Online Resource
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / (2015)
    Amsterdam :Academic Press,
    Details
    UID:
    almafu_BV043479733
    Format: 1 Online-Ressource (xii, 316 Seiten) : , Diagramme.
    ISBN: 978-0-12-801678-7
    Note: Literaturverzeichnis Seite 297-307
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-801370-0
    Language: English
    Subjects: Computer Science , Geography , Biology
    RVK:
    RB 10480
    RVK:
    ST 250
    RVK:
    WI 1500
    Keywords: Ökologie ; Datenanalyse ; Ökologie ; Datenanalyse ; R
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    FU Berlin
    Charité
  • 4
    Online Resource
    Online Resource
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / (2015)
    Amsterdam :Academic Press,
    Details
    UID:
    edocfu_BV043479733
    Format: 1 Online-Ressource (xii, 316 Seiten) : , Diagramme.
    ISBN: 978-0-12-801678-7
    Note: Literaturverzeichnis Seite 297-307
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-0-12-801370-0
    Language: English
    Subjects: Computer Science , Geography , Biology
    RVK:
    RB 10480
    RVK:
    ST 250
    RVK:
    WI 1500
    Keywords: Ökologie ; Datenanalyse ; Ökologie ; Datenanalyse ; R
    URL: Volltext  (URL des Erstveröffentlichers)
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Online Access
    FU Berlin
  • 5
    Online Resource
    Online Resource
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / (2015)
    Amsterdam, [Netherlands] :Academic Press,
    Details
    UID:
    edocfu_9960073527302883
    Format: 1 online resource (329 p.)
    Edition: 1st ed.
    Content: 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.--
    Note: Description based upon print version of record. , 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 , 14.5 ANALYZING SURVIVAL BASED ON MARK-RECAPTURE DATA USING STAN , English
    Additional Edition: ISBN 0-12-801370-2
    Additional Edition: ISBN 0-12-801678-7
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    FU Berlin
  • 6
    Online Resource
    Online Resource
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / (2015)
    Amsterdam, [Netherlands] :Academic Press,
    Details
    UID:
    almahu_9948026402002882
    Format: 1 online resource (329 p.)
    Edition: 1st ed.
    Content: 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.--
    Note: Description based upon print version of record. , 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 , 14.5 ANALYZING SURVIVAL BASED ON MARK-RECAPTURE DATA USING STAN , English
    Additional Edition: ISBN 0-12-801370-2
    Additional Edition: ISBN 0-12-801678-7
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    HU Berlin
  • 7
    Online Resource
    Online Resource
    Bayesian data analysis in ecology using linear models with R, BUGS, and Stan / (2015)
    Amsterdam, [Netherlands] :Academic Press,
    Details
    UID:
    edoccha_9960073527302883
    Format: 1 online resource (329 p.)
    Edition: 1st ed.
    Content: 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.--
    Note: Description based upon print version of record. , 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 , 14.5 ANALYZING SURVIVAL BASED ON MARK-RECAPTURE DATA USING STAN , English
    Additional Edition: ISBN 0-12-801370-2
    Additional Edition: ISBN 0-12-801678-7
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Charité
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