Generalized Linear Mixed Models with Applications in Agriculture and Biology.

• Author: Salinas Ruíz, Josafhat.
• TeilnehmendeR: Montesinos López, Osval Antonio.; Hernández Ramírez, Gabriela.; Crossa Hiriart, Jose.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2023.; {copy}2023.
• Year of Publication: 2023
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (436 pages)

Table of Contents

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.