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From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	From experimental network to meta-analysis/ by David Makowski, Francois Piraux, Francois Brun.
Reminder of title:	methods and applications with R for agronomic and environmental sciences /
Author:	Makowski, David.
other author:	Piraux, Francois.
Published:	Dordrecht :Springer Netherlands : : 2019.,
Description:	x, 155 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	System analysis. -
Online resource:	https://doi.org/10.1007/978-94-024-1696-1
ISBN:	9789402416961

From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
Makowski, David.

From experimental network to meta-analysismethods and applications with R for agronomic and environmental sciences /[electronic resource] :by David Makowski, Francois Piraux, Francois Brun. - Dordrecht :Springer Netherlands :2019. - x, 155 p. :ill., digital ;24 cm.

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 -- Package Codes.

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.

ISBN: 9789402416961

Standard No.: 10.1007/978-94-024-1696-1doiSubjects--Topical Terms:

528439
System analysis.

LC Class. No.: QA402 / .M356 2019

Dewey Class. No.: 003

From experimental network to meta-analysis = methods and applications with R for agronomic and environmental sciences /
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520 $a 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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