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Título : Forest Analytics with R : An Introduction Tipo de documento: documento electrónico Autores: Robinson, Andrew P ; SpringerLink (Online service) ; Hamann, Jeff D Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Use R Número de páginas: XIV, 354 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-7762-5 Idioma : Inglés (eng) Palabras clave: Statistics Forestry management Environmental sciences for Life Sciences, Medicine, Health Sciences Management Math. Appl. in Science Clasificación: 51 Matemáticas Resumen: Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others Nota de contenido: Introduction -- Forest data management -- Data analysis for common inventory methods -- Imputation and Interpolation -- Fitting dimensional distributions -- Linear and non-linear models -- Fitting linear hierarchical models -- Simulations -- Forest estate planning and optimization En línea: http://dx.doi.org/10.1007/978-1-4419-7762-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33168 Forest Analytics with R : An Introduction [documento electrónico] / Robinson, Andrew P ; SpringerLink (Online service) ; Hamann, Jeff D . - New York, NY : Springer New York, 2011 . - XIV, 354 p : online resource. - (Use R) .
ISBN : 978-1-4419-7762-5
Idioma : Inglés (eng)
Palabras clave: Statistics Forestry management Environmental sciences for Life Sciences, Medicine, Health Sciences Management Math. Appl. in Science Clasificación: 51 Matemáticas Resumen: Forest Analytics with R combines practical, down-to-earth forestry data analysis and solutions to real forest management challenges with state-of-the-art statistical and data-handling functionality. The authors adopt a problem-driven approach, in which statistical and mathematical tools are introduced in the context of the forestry problem that they can help to resolve. All the tools are introduced in the context of real forestry datasets, which provide compelling examples of practical applications. The modeling challenges covered within the book include imputation and interpolation for spatial data, fitting probability density functions to tree measurement data using maximum likelihood, fitting allometric functions using both linear and non-linear least-squares regression, and fitting growth models using both linear and non-linear mixed-effects modeling. The coverage also includes deploying and using forest growth models written in compiled languages, analysis of natural resources and forestry inventory data, and forest estate planning and optimization using linear programming. The book would be ideal for a one-semester class in forest biometrics or applied statistics for natural resources management. The text assumes no programming background, some introductory statistics, and very basic applied mathematics. Andrew Robinson has been associate professor of forest mensuration and forest biometrics at the University of Idaho, and is currently senior lecturer in applied statistics at the University of Melbourne. He received his PhD in forestry from the University of Minnesota. Robinson is author of the popular and freely-available "icebreakeR" document. Jeff Hamann has been a software developer, forester, and financial analyst. He is currently a consultant specializing in forestry, operations research, and geographic information sciences. He received his PhD in forestry from Oregon State University. Both authors have presented numerous R workshops to forestry professionals and scientists, and others Nota de contenido: Introduction -- Forest data management -- Data analysis for common inventory methods -- Imputation and Interpolation -- Fitting dimensional distributions -- Linear and non-linear models -- Fitting linear hierarchical models -- Simulations -- Forest estate planning and optimization En línea: http://dx.doi.org/10.1007/978-1-4419-7762-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33168 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar Nonlinear Regression with R / SpringerLink (Online service) ; Ritz, Christian ; Streibig, Jens Carl (2008)
Título : Nonlinear Regression with R Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Ritz, Christian ; Streibig, Jens Carl Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Use R Número de páginas: XII, 148 p Il.: online resource ISBN/ISSN/DL: 978-0-387-09616-2 Idioma : Inglés (eng) Materias: Análisis de regresión
Lenguajes de programaciónPalabras clave: Mathematics Pharmacology Epidemiology Forestry Probabilities Statistics Computational intelligence Probability Theory and Stochastic Processes Statistical Methods Pharmacology/Toxicology Intelligence Clasificación: 004R R (Lenguaje de programación) Resumen: R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology. The book begins with an introduction on how to fit nonlinear regression models in R. Subsequent chapters explain in more depth the salient features of the fitting function nls(), the use of model diagnostics, the remedies for various model departures, and how to do hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered. Christian Ritz has a PhD in biostatistics from the Royal Veterinary and Agricultural University. For the last 5 years he has been working extensively with various applications of nonlinear regression in the life sciences and related disciplines, authoring several R packages and papers on this topic. He is currently doing postdoctoral research at the University of Copenhagen. Jens C. Streibig is a professor in Weed Science at the University of Copenhagen. He has for more than 25 years worked on selectivity of herbicides and more recently on the ecotoxicology of pesticides and has extensive experience in applying nonlinear regression models. Together with the first author he has developed short courses on the subject of this book for students in the life sciences Nota de contenido: Getting Started -- Starting Values and Self-starters -- More on nls() -- Model Diagnostics -- Remedies for Model Violations -- Uncertainty, Hypothesis Testing, and Model Selection -- Grouped Data En línea: http://dx.doi.org/10.1007/978-0-387-09616-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34144 Nonlinear Regression with R [documento electrónico] / SpringerLink (Online service) ; Ritz, Christian ; Streibig, Jens Carl . - New York, NY : Springer New York, 2008 . - XII, 148 p : online resource. - (Use R) .
ISBN : 978-0-387-09616-2
Idioma : Inglés (eng)
Materias: Análisis de regresión
Lenguajes de programaciónPalabras clave: Mathematics Pharmacology Epidemiology Forestry Probabilities Statistics Computational intelligence Probability Theory and Stochastic Processes Statistical Methods Pharmacology/Toxicology Intelligence Clasificación: 004R R (Lenguaje de programación) Resumen: R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. Currently, R offers a wide range of functionality for nonlinear regression analysis, but the relevant functions, packages and documentation are scattered across the R environment. This book provides a coherent and unified treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology. The book begins with an introduction on how to fit nonlinear regression models in R. Subsequent chapters explain in more depth the salient features of the fitting function nls(), the use of model diagnostics, the remedies for various model departures, and how to do hypothesis testing. In the final chapter grouped-data structures, including an example of a nonlinear mixed-effects regression model, are considered. Christian Ritz has a PhD in biostatistics from the Royal Veterinary and Agricultural University. For the last 5 years he has been working extensively with various applications of nonlinear regression in the life sciences and related disciplines, authoring several R packages and papers on this topic. He is currently doing postdoctoral research at the University of Copenhagen. Jens C. Streibig is a professor in Weed Science at the University of Copenhagen. He has for more than 25 years worked on selectivity of herbicides and more recently on the ecotoxicology of pesticides and has extensive experience in applying nonlinear regression models. Together with the first author he has developed short courses on the subject of this book for students in the life sciences Nota de contenido: Getting Started -- Starting Values and Self-starters -- More on nls() -- Model Diagnostics -- Remedies for Model Violations -- Uncertainty, Hypothesis Testing, and Model Selection -- Grouped Data En línea: http://dx.doi.org/10.1007/978-0-387-09616-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34144 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
Título : Numerical Ecology with R Tipo de documento: documento electrónico Autores: Borcard, Daniel ; SpringerLink (Online service) ; Gillet, Francois ; Legendre, Pierre Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Use R Número de páginas: XII, 306 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-7976-6 Idioma : Inglés (eng) Palabras clave: Statistics Epidemiology Biostatistics Ecology Forestry for Life Sciences, Medicine, Health Sciences Environmental Monitoring/Analysis Theoretical Ecology/Statistics Clasificación: 51 Matemáticas Resumen: Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. 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/). 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. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment Nota de contenido: Introduction -- Exploratory data analysis -- Association measures and matrices -- Cluster analysis -- Unconstrained ordination -- Canonical ordination -- Spatial analysis of ecological data. En línea: http://dx.doi.org/10.1007/978-1-4419-7976-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33178 Numerical Ecology with R [documento electrónico] / Borcard, Daniel ; SpringerLink (Online service) ; Gillet, Francois ; Legendre, Pierre . - New York, NY : Springer New York, 2011 . - XII, 306 p : online resource. - (Use R) .
ISBN : 978-1-4419-7976-6
Idioma : Inglés (eng)
Palabras clave: Statistics Epidemiology Biostatistics Ecology Forestry for Life Sciences, Medicine, Health Sciences Environmental Monitoring/Analysis Theoretical Ecology/Statistics Clasificación: 51 Matemáticas Resumen: Numerical Ecology with R provides a long-awaited bridge between a textbook in Numerical Ecology and the implementation of this discipline in the R language. After short theoretical overviews, the authors accompany the users through the exploration of the methods by means of applied and extensively commented examples. Users are invited to use this book as a teaching companion at the computer. The travel starts with exploratory approaches, proceeds with the construction of association matrices, then addresses three families of methods: clustering, unconstrained and canonical ordination, and spatial analysis. 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/). 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. The three authors teach numerical ecology, both theoretical and practical, to a wide array of audiences, in regular courses in their Universities and in short courses given around the world. Daniel Borcard is lecturer of Biostatistics and Ecology and researcher in Numerical Ecology at Université de Montréal, Québec, Canada. François Gillet is professor of Community Ecology and Ecological Modelling at Université de Franche-Comté, Besançon, France. Pierre Legendre is professor of Quantitative Biology and Ecology at Université de Montréal, Fellow of the Royal Society of Canada, and ISI Highly Cited Researcher in Ecology/Environment Nota de contenido: Introduction -- Exploratory data analysis -- Association measures and matrices -- Cluster analysis -- Unconstrained ordination -- Canonical ordination -- Spatial analysis of ecological data. En línea: http://dx.doi.org/10.1007/978-1-4419-7976-6 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33178 Ejemplares
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar
