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From Experimental Network to Meta-analysis Methods and Applications with R for Agronomic and Environmental Sciences
Author
Title
From Experimental Network to Meta-analysis [electronic resource] : Methods and Applications with R for Agronomic and Environmental Sciences / by David Makowski, François Piraux, François Brun.
ISBN
9789402416961
Publication
Dordrecht : Springer Netherlands : Imprint: Springer, 2019.
Physical Description
1 online resource (X, 155 p.) 69 illus., 43 illus. in color.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
Data analysis plays an increasing role in research, scientific expertise and prospective studies. Multiple data sources are often available to estimate a key parameter or to test a hypothesis of scientific or societal interest. These data, obtained under different environmental conditions or based on different experimental protocols, are generally heterogeneous. Sometimes they are not even directly accessible and should be extracted from scientific articles or reports. However, a comprehensive analysis of the available data is essential to increase the accuracy of estimates, assess the validity of research conclusions and understand the origin of the variability of the experimental results. A quantitative synthesis of the data set available allows for a better understanding of the effects of explanatory factors and for evidence-based recommendations. Designed as a methodological guide, this book shows the interests and limitations of different statistical methods to analyze data from experimental networks and to perform meta-analyses. It is intended for engineers, students and researchers involved in data analysis in agronomy and environmental science. Our objective is to present the main statistical methods to analyze data from experimental networks and scientific publications. Each chapter exposes one or more methods and illustrates them with examples processed with the R software. Data and R codes are provided and commented in order to facilitate their adaptation to other situations. The codes can be reused from the KenSyn R package associated with this book.
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Springer ENIN.
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Format
Books / Online
Language
English
Added to Catalog
May 17, 2019
Contents
Chapter 1. Introduction and examples
Part I. Analysis of experimental networks
Chapter 2. Basic Concepts
Chapter 3. Analysis of network of experiments in blocks of complete randomness as a studied factor
Chapter 4. Advanced Methods for Network Analysis
Chapter 5. Planning an Experimental Network
Part II. The meta-analysis
Chapter 6. Basics for meta-analysis
Chapter 7. Specific statistical problems for the meta-analysis
Annex. R resources to implement the methods of analysis networks and meta-analysis
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