Generalized Linear Mixed Models with Applications in Agriculture and Biology.

Generalized Linear Mixed Models with Applications in Agriculture and Biology.

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Bibliographic Details
:
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
Online Access:
Physical Description:1 online resource (436 pages)
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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.

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