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Format: 1 Online-Ressource.

ISBN: 978-3-031-32800-8

Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-32799-5

Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-32802-2

Language: English

Subjects: Biology

DOI: 10.1007/978-3-031-32800-8

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource.

ISBN: 978-3-031-32800-8

Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-32799-5

Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-32802-2

Language: English

Subjects: Biology

DOI: 10.1007/978-3-031-32800-8

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource.

ISBN: 978-3-031-32800-8

Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-32799-5

Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-32802-2

Language: English

Subjects: Biology

DOI: 10.1007/978-3-031-32800-8

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Format: 1 Online-Ressource

ISBN: 9783031328008

Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-3-031-32799-5

Additional Edition: Erscheint auch als Druck-Ausgabe, Paperback ISBN 978-3-031-32802-2

Language: English

Subjects: Biology

DOI: 10.1007/978-3-031-32800-8

URL: Volltext  (kostenfrei)

URL: Volltext  (kostenfrei)

UID:

Format: 1 online resource (434 pages)

Edition: 1st ed. 2023.

ISBN: 3-031-32800-0 , 9783031328008

Content: This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.

Note: Chapter 1) Elements of the Generalized Linear Mixed Models -- Chapter 2) Generalized Linear Models -- Chapter 3) Objectives in Model Inference -- Chapter 4) Generalized Linear Mixed Models for non-normal responses -- Chapter 5) Generalized Linear Mixed Models for Count response -- Chapter 6) Generalized Linear Mixed Models for Proportions and Percentages response -- Chapter 7) Times of occurrence of an event of interest -- Chapter 8) Generalized Linear Mixed Models for Categorial and Ordinal responses -- Chapter 9) Generalized Linear Mixed Models for Repeated Measurements.

Additional Edition: ISBN 3-031-32799-3

Additional Edition: ISBN 9783031327995

Language: English

Keywords: Llibres electrònics ; Llibres electrònics

DOI: 10.1007/978-3-031-32800-8

URL: kostenfrei

UID:

Format: 1 Online-Ressource (427 p.)

ISBN: 9783031328008 , 9783031327995

Content: This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables

Note: English

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URL: lizenzpflichtig

UID:

Format: 1 online resource (xi, 427 pages) : , illustrations (some color)

ISBN: 9783031328008 , 3031328000

Content: This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later -- through examples -- its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.

Note: Chapter 1) Elements of the Generalized Linear Mixed Models -- Chapter 2) Generalized Linear Models -- Chapter 3) Objectives in Model Inference -- Chapter 4) Generalized Linear Mixed Models for non-normal responses -- Chapter 5) Generalized Linear Mixed Models for Count response -- Chapter 6) Generalized Linear Mixed Models for Proportions and Percentages response -- Chapter 7) Times of occurrence of an event of interest -- Chapter 8) Generalized Linear Mixed Models for Categorial and Ordinal responses -- Chapter 9) Generalized Linear Mixed Models for Repeated Measurements.

Additional Edition: ISBN 3-031-32799-3

Language: English

Keywords: Electronic books.

DOI: 10.1007/978-3-031-32800-8

URL: SpringerLink

URL: Click here to view book

UID:

Format: xi, 427 Seiten : , Diagramme.

ISBN: 978-3-031-32802-2

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-031-32800-8

Language: English

Subjects: Biology

UID:

Format: 1 online resource (436 pages)

Edition: 1st ed.

ISBN: 9783031328008

Note: Intro -- Foreword -- Acknowledgments -- Contents -- Chapter 1: Elements of Generalized Linear Mixed Models -- 1.1 Introduction to Linear Models -- 1.2 Regression Models -- 1.2.1 Simple Linear Regression -- 1.2.2 Multiple Linear Regression -- 1.3 Analysis of Variance Models -- 1.3.1 One-Way Analysis of Variance -- 1.3.2 Two-Way Nested Analysis of Variance -- 1.3.3 Two-Way Analysis of Variance with Interaction -- 1.4 Analysis of Covariance (ANCOVA) -- 1.5 Mixed Models -- 1.5.1 Introduction -- 1.5.2 Mixed Models -- 1.5.3 Distribution of the Response Variable Conditional on Random Effects (y|b) -- 1.5.4 Types of Factors and Their Related Effects on LMMs -- 1.5.4.1 Fixed Factors -- 1.5.4.2 Random Factors -- 1.5.4.3 Fixed Versus Random Factors -- 1.5.5 Nested Versus Crossed Factors and Their Corresponding Effects -- 1.5.6 Estimation Methods -- 1.5.6.1 Maximum Likelihood -- 1.5.6.2 Restricted Maximum Likelihood Estimation -- 1.5.7 One-Way Random Effects Model -- 1.5.8 Analysis of Variance Model of a Randomized Block Design -- 1.6 Exercises -- Appendix -- Chapter 2: Generalized Linear Models -- 2.1 Introduction -- 2.2 Components of a GLM -- 2.2.1 The Random Component -- 2.2.2 The Systematic Component -- 2.2.3 Predictorś Link Function η -- 2.3 Assumptions of a GLM -- 2.4 Estimation and Inference of a GLM -- 2.5 Specification of a GLM -- 2.5.1 Continuous Normal Response Variable -- 2.5.2 Binary Logistic Regression -- 2.5.2.1 Model Diagnosis -- 2.5.3 Poisson Regression -- 2.5.4 Gamma Regression -- 2.5.4.1 Model Selection -- 2.5.5 Beta Regression -- 2.6 Exercises -- Appendix -- Chapter 3: Objectives of Inference for Stochastic Models -- 3.1 Three Aspects to Consider for an Inference -- 3.1.1 Data Scale in the Modeling Process Versus Original Data -- 3.1.2 Inference Space -- 3.1.3 Inference Based on Marginal and Conditional Models. , 3.2 Illustrative Examples of the Data Scale and the Model Scale -- 3.3 Fixed and Random Effects in the Inference Space -- 3.3.1 A Broad Inference Space or a Population Inference -- 3.3.2 Mixed Models with a Normal Response -- 3.4 Marginal and Conditional Models -- 3.4.1 Marginal Versus Conditional Models -- 3.4.2 Normal Distribution -- 3.4.3 Non-normal Distribution -- 3.5 Exercises -- Chapter 4: Generalized Linear Mixed Models for Non-normal Responses -- 4.1 Introduction -- 4.2 A Brief Description of Linear Mixed Models (LMMs) -- 4.3 Generalized Linear Mixed Models -- 4.4 The Inverse Link Function -- 4.5 The Variance Function -- 4.6 Specification of a GLMM -- 4.7 Estimation of the Dispersion Parameter -- 4.8 Estimation and Inference in Generalized Linear Mixed Models -- 4.8.1 Estimation -- 4.8.2 Inference -- 4.9 Fitting the Model -- 4.10 Exercises -- Chapter 5: Generalized Linear Mixed Models for Counts -- 5.1 Introduction -- 5.2 The Poisson Model -- 5.2.1 CRD with a Poisson Response -- 5.2.2 Example 2: CRDs with Poisson Response -- 5.2.3 Example 3: Control of Weeds in Cereal Crops in an RCBD -- 5.2.4 Overdispersion in Poisson Data -- 5.2.4.1 Using the Scale Parameter -- 5.2.4.2 Linear Predictor Review -- 5.2.4.3 Using a Different Distribution -- 5.2.5 Factorial Designs -- 5.2.5.1 Example: A 2 x 4 Factorial with a Poisson Response -- 5.2.6 Latin Square (LS) Design -- 5.2.6.1 Latin Square Design with a Poisson Response -- 5.2.6.2 Randomized Complete Block Design in a Split Plot -- 5.3 Exercises -- Appendix 1 -- Chapter 6: Generalized Linear Mixed Models for Proportions and Percentages -- 6.1 Response Variables as Ratios and Percentages -- 6.2 Analysis of Discrete Proportions: Binary and Binomial Responses -- 6.2.1 Completely Randomized Design (CRD): Methylation Experiment. , 6.3 Factorial Design in a Randomized Complete Block Design (RCBD) with Binomial Data: Toxic Effect of Different Treatments on ... -- 6.4 A Split-Plot Design in an RCBD with a Normal Response -- 6.4.1 An RCBD Split Plot with Binomial Data: Carrot Fly Larval Infestation of Carrots -- 6.4.1.1 Linear Predictor Review (ηijk) -- 6.4.1.2 Scale Parameter -- 6.4.1.3 Alternative Distribution -- 6.5 A Split-Split Plot in an RCBD:- In Vitro Germination of Seeds -- 6.6 Alternative Link Functions for Binomial Data -- 6.6.1 Probit Link: A Split-Split Plot in an RCBD with a Binomial Response -- 6.6.2 Complementary Log-Log Link Function: A Split Plot in an RCBD with a Binomial Response -- 6.7 Percentages -- 6.7.1 RCBD: Dead Aphid Rate -- 6.7.2 RCBD: Percentage of Quality Malt -- 6.7.3 A Split Plot in an RCBD: Cockroach Mortality (Blattella germanica) -- 6.7.4 A Split-Plot Design in an RCBD: Percentage Disease Inhibition -- 6.7.5 Randomized Complete Block Design with a Binomial Response with Multiple Variance Components -- 6.8 Exercises -- Appendix -- Chapter 7: Time of Occurrence of an Event of Interest -- 7.1 Introduction -- 7.2 Generalized Linear Mixed Models with a Gamma Response -- 7.2.1 CRD: Estrus Induction in Pelibuey Ewes -- 7.2.2 Randomized Complete Block Design (RCBD): Itch Relief Drugs -- 7.2.3 Factorial Design: Insect Survival Time -- 7.2.4 A Split Plot with a Factorial Structure on a Large Plot in a Completely Randomized Design (CRD) -- 7.3 Survival Analysis -- 7.3.1 Concepts and Definitions -- 7.3.2 CRD: Aedes aegypti -- 7.3.3 RCBD: Aedes aegypti -- 7.4 Exercises -- Appendix 1 -- Chapter 8: Generalized Linear Mixed Models for Categorical and Ordinal Responses -- 8.1 Introduction -- 8.2 Concepts and Definitions -- 8.3 Cumulative Logit Models (Proportional Odds Models) -- 8.3.1 Complete Randomize Design (CRD) with a Multinomial Response: Ordinal. , 8.3.2 Randomized Complete Block Design (RCBD) with a Multinomial Response: Ordinal -- 8.4 Cumulative Probit Models -- 8.5 Effect of Judges ́Experience on Canned Bean Quality Ratings -- 8.6 Generalized Logit Models: Nominal Response Variables -- 8.6.1 CRDs with a Nominal Multinomial Response -- 8.6.2 CRD: Cheese Tasting -- 8.7 Exercises -- Appendix -- Chapter 9: Generalized Linear Mixed Models for Repeated Measurements -- 9.1 Introduction -- 9.2 Example of Turf Quality -- 9.3 Effect of Insecticides on Aphid Growth -- 9.4 Manufacture of Livestock Feed -- 9.5 Characterization of Spatial and Temporal Variations in Fecal Coliform Density -- 9.6 Log-Normal Distribution -- 9.6.1 Emission of Nitrous Oxide (N2O) in Beef Cattle Manure with Different Percentages of Crude Protein in the Diet -- 9.7 Effect of a Chemical Salt on the Percentage Inhibition of the Fusarium sp. -- 9.8 Carbon Dioxide (CO2) Emission as a Function of Soil Moisture and Microbial Activity -- 9.9 Effect of Soil Compaction and Soil Moisture on Microbial Activity -- 9.10 Joint Model for Binary and Poisson Data -- 9.11 Exercises -- Appendix -- References.

Additional Edition: Print version: Salinas Ruíz, Josafhat Generalized Linear Mixed Models with Applications in Agriculture and Biology Cham : Springer International Publishing AG,c2023 ISBN 9783031327995

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 online resource (434 pages)

Edition: 1st ed. 2023.

ISBN: 3-031-32800-0 , 9783031328008

Content: This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.

Note: Chapter 1) Elements of the Generalized Linear Mixed Models -- Chapter 2) Generalized Linear Models -- Chapter 3) Objectives in Model Inference -- Chapter 4) Generalized Linear Mixed Models for non-normal responses -- Chapter 5) Generalized Linear Mixed Models for Count response -- Chapter 6) Generalized Linear Mixed Models for Proportions and Percentages response -- Chapter 7) Times of occurrence of an event of interest -- Chapter 8) Generalized Linear Mixed Models for Categorial and Ordinal responses -- Chapter 9) Generalized Linear Mixed Models for Repeated Measurements.

Additional Edition: ISBN 3-031-32799-3

Additional Edition: ISBN 9783031327995

Language: English

Keywords: Llibres electrònics

DOI: 10.1007/978-3-031-32800-8