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Titre : Numerical ecology with R Type de document : livre Auteurs : Daniel Borcard, Auteur ; François Gillet, Auteur ; Pierre Legendre, Auteur Mention d'édition : 2nd ed. Editeur : New York : Springer Année de publication : 2018 Collection : Use R! Importance : 435 p. ISBN/ISSN/EAN : 978-3-319-71403-5 Note générale : Voir aussi la 1ère ed. de 2011 aux cotes 69116/11 et 69319/11; All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site (http://adn.biol.umontreal.ca/~numericalecology/numecolR/). Langues : Anglais (eng) Mots-clés : Data processing Ecology Mathematics Multivariate analysis R (Computer program language) Statistical methods Résumé : Le site éditeur indique : This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches. It proceeds logically with the construction of the key building blocks of most methods, i.e. association measures and matrices, and then submits example data to three families of approaches: clustering, ordination and canonical ordination. The last two chapters make use of these methods to explore important and contemporary issues in ecology: the analysis of spatial structures and of community diversity. The aims of methods thus range from descriptive to explanatory and predictive and encompass a wide variety of approaches that should provide readers with an extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis. The second edition of this book features a complete revision to the R code and offers improved procedures and more diverse applications of the major methods. It also highlights important changes in the methods and expands upon topics such as multiple correspondence analysis, principal response curves and co-correspondence analysis. New features include the study of relationships between species traits and the environment, and community diversity analysis.
This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. Users are invited to use this book as a teaching companion at the computer. All the necessary data files, the scripts used in the chapters, as well as extra R functions and packages written by the authors of the book, are available online (URL: http://adn.biol.umontreal.ca/~numericalecology/numecolR/).En ligne : https://doi.org/10.1007/978-3-319-71404-2 Numerical ecology with R [livre] / Daniel Borcard, Auteur ; François Gillet, Auteur ; Pierre Legendre, Auteur . - 2nd ed. . - New York : Springer, 2018 . - 435 p.. - (Use R!) .
ISBN : 978-3-319-71403-5
Voir aussi la 1ère ed. de 2011 aux cotes 69116/11 et 69319/11; All the necessary data files, the scripts used in the chapters, as well as the extra R functions and packages written by the authors, can be downloaded from a web page accessible through the Springer web site (http://adn.biol.umontreal.ca/~numericalecology/numecolR/).
Langues : Anglais (eng)
Mots-clés : Data processing Ecology Mathematics Multivariate analysis R (Computer program language) Statistical methods Résumé : Le site éditeur indique : This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches. It proceeds logically with the construction of the key building blocks of most methods, i.e. association measures and matrices, and then submits example data to three families of approaches: clustering, ordination and canonical ordination. The last two chapters make use of these methods to explore important and contemporary issues in ecology: the analysis of spatial structures and of community diversity. The aims of methods thus range from descriptive to explanatory and predictive and encompass a wide variety of approaches that should provide readers with an extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis. The second edition of this book features a complete revision to the R code and offers improved procedures and more diverse applications of the major methods. It also highlights important changes in the methods and expands upon topics such as multiple correspondence analysis, principal response curves and co-correspondence analysis. New features include the study of relationships between species traits and the environment, and community diversity analysis.
This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. Users are invited to use this book as a teaching companion at the computer. All the necessary data files, the scripts used in the chapters, as well as extra R functions and packages written by the authors of the book, are available online (URL: http://adn.biol.umontreal.ca/~numericalecology/numecolR/).En ligne : https://doi.org/10.1007/978-3-319-71404-2 Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 69587 BOR_11_69587 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043 69588 BOR_11_69588 Livre Salle des ouvrages 11_Mathématiques Disponible
Titre : Functional data analysis with R and MATLAB Type de document : livre Auteurs : J. O. Ramsay, Auteur ; Gilles Hooker, Auteur ; Spencer Graves, Auteur Editeur : Dordrecht : Springer Année de publication : 2009 Collection : Use R! Importance : 207 p. ISBN/ISSN/EAN : 978-0-387-98184-0 Prix : 69.99 USD Langues : Anglais (eng) Mots-clés : Computer programs Data processing R (Computer program language) MATLAB (Computer program language) Case studies Résumé : Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures. En ligne : https://doi.org/10.1007/978-0-387-98185-7 Functional data analysis with R and MATLAB [livre] / J. O. Ramsay, Auteur ; Gilles Hooker, Auteur ; Spencer Graves, Auteur . - Dordrecht : Springer, 2009 . - 207 p.. - (Use R!) .
ISBN : 978-0-387-98184-0 : 69.99 USD
Langues : Anglais (eng)
Mots-clés : Computer programs Data processing R (Computer program language) MATLAB (Computer program language) Case studies Résumé : Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems. It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures. En ligne : https://doi.org/10.1007/978-0-387-98185-7 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 69536 RAM_11_69536 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043
Titre : Statistical computing with R Type de document : livre Auteurs : Rizzo, M.L. Editeur : Boca Raton : Chapman & Hall/CRC Année de publication : 2008 Collection : Computer science and data analysis series Importance : 399 p. ISBN/ISSN/EAN : 978-1-58488-545-0 Langues : Anglais (eng) Mots-clés : R (Computer program language) Statistical methods Résumé : Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions. Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing. [Résumé éditeur] Statistical computing with R [livre] / Rizzo, M.L. . - Boca Raton : Chapman & Hall/CRC, 2008 . - 399 p.. - (Computer science and data analysis series) .
ISBN : 978-1-58488-545-0
Langues : Anglais (eng)
Mots-clés : R (Computer program language) Statistical methods Résumé : Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions. Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing. [Résumé éditeur] Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 68239 RIZ_11_68239 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043
Titre : Analysis of phylogenetics and evolution with R Type de document : livre Auteurs : Paradis, E., Auteur Editeur : New York : Springer Année de publication : 2006 Collection : Use R! Importance : 211 p. ISBN/ISSN/EAN : 978-0-387-32914-7 Langues : Anglais (eng) Mots-clés : Evolution Phylogenetics R (Computer program language) Résumé : The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters. Analysis of phylogenetics and evolution with R [livre] / Paradis, E., Auteur . - New York : Springer, 2006 . - 211 p.. - (Use R!) .
ISBN : 978-0-387-32914-7
Langues : Anglais (eng)
Mots-clés : Evolution Phylogenetics R (Computer program language) Résumé : The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification. This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments. Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters. Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 68108 PAR_11_68108 Livre Salle des ouvrages 11_Mathématiques Disponible
Titre : The R book Type de document : livre Auteurs : Michael J. Crawley, Auteur Mention d'édition : 2nd ed. Editeur : Chichester, U.K. : Wiley Année de publication : 2013 Importance : 1051 p. ISBN/ISSN/EAN : 978-0-470-97392-9 Prix : 68.00 GBP Note générale : 2 ex.; Voir aussi la 1ère éd. de 2007 aux cotes 68177, 68339, 68367, 68469 et 68557/11 Langues : Anglais (eng) Mots-clés : Analysis of variance Data analysis Non-linear regression R (Computer program language) Spatial statistics Statistical methods Résumé : La 4ème de couv. indique : Hugely successful and popular text presenting an extensive and comprehensive guide for all R users
The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.
This edition:
*Features full colour text and extensive graphics throughout.
*Introduces a clear structure with numbered section headings to help readers locate information more efficiently.
*Looks at the evolution of R over the past five years.
*Features a new chapter on Bayesian Analysis and Meta-Analysis.
*Presents a fully revised and updated bibliography and reference section.
*Is supported by an accompanying website allowing examples from the text to be run by the user.Note de contenu : Download all the Executable text files (a bundle of 268 small text files) : http://www.bio.ic.ac.uk/research/mjcraw/therbook/index.htm En ligne : https://www.wiley.com/en-gb/The+R+Book%2C+2nd+Edition-p-9780470973929 The R book [livre] / Michael J. Crawley, Auteur . - 2nd ed. . - Chichester, U.K. : Wiley, 2013 . - 1051 p.
ISBN : 978-0-470-97392-9 : 68.00 GBP
2 ex.; Voir aussi la 1ère éd. de 2007 aux cotes 68177, 68339, 68367, 68469 et 68557/11
Langues : Anglais (eng)
Mots-clés : Analysis of variance Data analysis Non-linear regression R (Computer program language) Spatial statistics Statistical methods Résumé : La 4ème de couv. indique : Hugely successful and popular text presenting an extensive and comprehensive guide for all R users
The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.
This edition:
*Features full colour text and extensive graphics throughout.
*Introduces a clear structure with numbered section headings to help readers locate information more efficiently.
*Looks at the evolution of R over the past five years.
*Features a new chapter on Bayesian Analysis and Meta-Analysis.
*Presents a fully revised and updated bibliography and reference section.
*Is supported by an accompanying website allowing examples from the text to be run by the user.Note de contenu : Download all the Executable text files (a bundle of 268 small text files) : http://www.bio.ic.ac.uk/research/mjcraw/therbook/index.htm En ligne : https://www.wiley.com/en-gb/The+R+Book%2C+2nd+Edition-p-9780470973929 Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 69720 CRA_11_69720 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043 69746 CRA_11_69746 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 13/08/2049 PermalinkPermalinkPermalinkIntegrated population models: theory and ecological applications with R and JAGS / Michael Schaub (2022)
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