Generalized Linear Mixed Models with Applications in Agriculture and Biology.
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Place / Publishing House: | Cham : : Springer International Publishing AG,, 2023. {copy}2023. |
Year of Publication: | 2023 |
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Language: | English |
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Salinas Ruíz, Josafhat. Generalized Linear Mixed Models with Applications in Agriculture and Biology. 1st ed. Cham : Springer International Publishing AG, 2023. {copy}2023. 1 online resource (436 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Montesinos López, Osval Antonio. Hernández Ramírez, Gabriela. Crossa Hiriart, Jose. Print version: Salinas Ruíz, Josafhat Generalized Linear Mixed Models with Applications in Agriculture and Biology Cham : Springer International Publishing AG,c2023 9783031327995 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30702995 Click to View |
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Salinas Ruíz, Josafhat. |
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Salinas Ruíz, Josafhat. Generalized Linear Mixed Models with Applications in Agriculture and Biology. 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. |
author_facet |
Salinas Ruíz, Josafhat. Montesinos López, Osval Antonio. Hernández Ramírez, Gabriela. Crossa Hiriart, Jose. |
author_variant |
r j s rj rjs |
author2 |
Montesinos López, Osval Antonio. Hernández Ramírez, Gabriela. Crossa Hiriart, Jose. |
author2_variant |
l o a m loa loam r g h rg rgh h j c hj hjc |
author2_role |
TeilnehmendeR TeilnehmendeR TeilnehmendeR |
author_sort |
Salinas Ruíz, Josafhat. |
title |
Generalized Linear Mixed Models with Applications in Agriculture and Biology. |
title_full |
Generalized Linear Mixed Models with Applications in Agriculture and Biology. |
title_fullStr |
Generalized Linear Mixed Models with Applications in Agriculture and Biology. |
title_full_unstemmed |
Generalized Linear Mixed Models with Applications in Agriculture and Biology. |
title_auth |
Generalized Linear Mixed Models with Applications in Agriculture and Biology. |
title_new |
Generalized Linear Mixed Models with Applications in Agriculture and Biology. |
title_sort |
generalized linear mixed models with applications in agriculture and biology. |
publisher |
Springer International Publishing AG, |
publishDate |
2023 |
physical |
1 online resource (436 pages) |
edition |
1st ed. |
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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Electronic books. |
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tag="040" ind1=" " ind2=" "><subfield code="a">MiAaPQ</subfield><subfield code="b">eng</subfield><subfield code="e">rda</subfield><subfield code="e">pn</subfield><subfield code="c">MiAaPQ</subfield><subfield code="d">MiAaPQ</subfield></datafield><datafield tag="050" ind1=" " ind2="4"><subfield code="a">QH323.5</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Salinas Ruíz, Josafhat.</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Generalized Linear Mixed Models with Applications in Agriculture and Biology.</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1st ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham :</subfield><subfield code="b">Springer International Publishing AG,</subfield><subfield code="c">2023.</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">{copy}2023.</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (436 pages)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="588" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources.</subfield></datafield><datafield tag="590" ind1=" " ind2=" "><subfield code="a">Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. </subfield></datafield><datafield tag="655" ind1=" " ind2="4"><subfield code="a">Electronic books.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Montesinos López, Osval Antonio.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hernández Ramírez, Gabriela.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Crossa Hiriart, Jose.</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Print version:</subfield><subfield code="a">Salinas Ruíz, Josafhat</subfield><subfield code="t">Generalized Linear Mixed Models with Applications in Agriculture and Biology</subfield><subfield code="d">Cham : Springer International Publishing AG,c2023</subfield><subfield code="z">9783031327995</subfield></datafield><datafield tag="797" ind1="2" ind2=" "><subfield code="a">ProQuest (Firm)</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=30702995</subfield><subfield code="z">Click to View</subfield></datafield></record></collection> |