LEADER 04602nam 22006375 450 001 9910337956203321 005 20220627194559.0 010 $a94-024-1696-X 024 7 $a10.1007/978-94-024-1696-1 035 $a(CKB)4100000008153843 035 $a(MiAaPQ)EBC5770980 035 $a(DE-He213)978-94-024-1696-1 035 $a(PPN)236519794 035 $a(EXLCZ)994100000008153843 100 $a20190507d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFrom Experimental Network to Meta-analysis $eMethods and Applications with R for Agronomic and Environmental Sciences /$fby David Makowski, François Piraux, François Brun 205 $a1st ed. 2019. 210 1$aDordrecht :$cSpringer Netherlands :$cImprint: Springer,$d2019. 215 $a1 online resource (160 pages) $cillustrations 311 $a94-024-1695-1 320 $aIncludes bibliographical references and index. 327 $aChapter 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. 330 $aData 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. 606 $aAgriculture 606 $aPlant science 606 $aBotany 606 $aStatistics  606 $aEnvironmental sciences 606 $aR (Computer program language) 606 $aAgriculture$3https://scigraph.springernature.com/ontologies/product-market-codes/L11006 606 $aPlant Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/L24000 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aEnvironmental Science and Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/G37000 615 0$aAgriculture. 615 0$aPlant science. 615 0$aBotany. 615 0$aStatistics . 615 0$aEnvironmental sciences. 615 0$aR (Computer program language). 615 14$aAgriculture. 615 24$aPlant Sciences. 615 24$aStatistical Theory and Methods. 615 24$aEnvironmental Science and Engineering. 676 $a003 700 $aMakowski$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut$0926057 702 $aPiraux$b François$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBrun$b François$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910337956203321 996 $aFrom Experimental Network to Meta-analysis$92079143 997 $aUNINA