From Experimental Network to Meta-analysis: Methods and Applications with R for Agronomic and Environmental Sciences

Makowski, David

ISBN 10: 9402416986 / ISBN 13: 9789402416985

Published by Springer, 2020

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Seller Inventory # ABLIING23Apr0412070061908

Bibliographic Details

• Title: From Experimental Network to Meta-analysis: Methods and Applications with R for Agronomic and Environmental Sciences
• Publisher: Springer
• Publication Date: 2020
• Language: English
• ISBN 10: 9402416986
• ISBN 13: 9789402416985
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Chapter 1. Introduction and examples Objectives of the analysis of experimental networks and meta-analysis Data The type of data The data collection Data validation Analysis Main steps Presentation of the tested hypotheses Collection of data Data validation Data analysis Validation of the analysis Communication of results Objective of the book A simple example of a mixed model Definition Data Model definition Estimate Comparison with the model without random effect References Part I. Analysis of experimental networks Chapter 2. Basic Concepts Agronomic experimentation Experimental network Definition Example of experiment network Environmental concept Objectives of network of experiments Concept of population of environments Interaction concept References Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor Objective of the chapter Example "wheat" Modelization Model with a random experiment effect Model with a fixed experimental effect Example How to choose between a model with a fixed experimental effect and a model with a random experiment effect? Model evaluation Normality Homoscedasticity Independence Suspicious data Average comparisons Hypothesis equality tests Confidence intervals Hypothesis equivalence tests Example Example "wheat": R script and commented analysis References Chapter 4. Advanced Methods for Network Analysis Analysis of average data Step 1: Analysis of individual experiments to estimate treatment averages Step 2: Analysis of the average data Example A analysis of average data with a fixed model Estimation of the interaction variance treatment-experimentation R script Experiments with heterogeneous variances Introduction Example "wheat" For further Missing data Origin of missing data Adjusted averages The factors place and year Goal Example "wheat_pluri" Model for analyzing average data Variance estimation of the treatment-year-place interaction Variance of the difference between two treatments Analysis of the example "wheat_pluri" and script R References Chapter 5. Planning an Experimental Network Goal Comparison of two treatments Case of a multilocal network Case of a multi-local and multi-year network Other contrasts Average comparison of several witnesses Comparison to the overall average References Part II. The meta-analysis Chapter 6. Basics for meta-analysis Definition, origin and main stages of the meta-analysis Estimated average effect size Goal Systematic search of studies, selection of references and data extraction Estimation of the average effect size with a model without random effect Estimation of the average effect size with a random effects model Meta-regression Goal Example Regression models with and without random effect Example (continued) Critical analysis of results References Chapter 7. Specific statistical problems for the meta-analysis Setting the effect size Correction of the bias related to the use of ratios Difference between observation means Effect sizes for binary data Correlation coefficient Effect sizes based on variance Generalized linear models for discrete data analysis Binomial logit model with random effects to analyze the effect of a treatment Example Mixed nonlinear models Interest and definition Example Bayesian models Definition meta-analysis with MCMCglmm References <