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Título : An Introduction to Applied Multivariate Analysis with R Tipo de documento: documento electrónico Autores: Brian S. Everitt ; SpringerLink (Online service) ; Hothorn, Torsten Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Use R Número de páginas: XIV, 274 p. 92 illus Il.: online resource ISBN/ISSN/DL: 9781441996503 Idioma : Inglés (eng) Palabras clave: Statistics Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data Nota de contenido: Multivariate data and multivariate analysis  Looking at multivariate data: visualization  Principal components analysis  Multidimensional scaling. Exploratory factor analysis  Cluster analysis  Confirmatory factor analysis and structural equation models  The analysis of repeated measures data. En línea: http://dx.doi.org/10.1007/9781441996503 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33195 An Introduction to Applied Multivariate Analysis with R [documento electrónico] / Brian S. Everitt ; SpringerLink (Online service) ; Hothorn, Torsten .  New York, NY : Springer New York, 2011 .  XIV, 274 p. 92 illus : online resource.  (Use R) .
ISBN : 9781441996503
Idioma : Inglés (eng)
Palabras clave: Statistics Statistical Theory and Methods Clasificación: 51 Matemáticas Resumen: The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data Nota de contenido: Multivariate data and multivariate analysis  Looking at multivariate data: visualization  Principal components analysis  Multidimensional scaling. Exploratory factor analysis  Cluster analysis  Confirmatory factor analysis and structural equation models  The analysis of repeated measures data. En línea: http://dx.doi.org/10.1007/9781441996503 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33195 Ejemplares
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Título : Analysis of Phylogenetics and Evolution with R Tipo de documento: documento electrónico Autores: Emmanuel Paradis ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Colección: Use R Número de páginas: XII, 211 p. 50 illus Il.: online resource ISBN/ISSN/DL: 9780387351001 Idioma : Inglés (eng) Palabras clave: Life sciences Biochemistry Bioinformatics Evolutionary biology Statistics Sciences Biochemistry, general Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: 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 reassess 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. Emmanuel Paradis is an evolutionary biologist at the Centre National de la Recherche Scientifique (CNRS) and the Institut de Recherche pour le Développement (IRD) in Montpellier. He received his Doctorate Diploma in population biology and ecology in 1993 at the University of Montpellier II. He has conducted empirical and theoretical research on birds, mammals, and fish. He worked at the British Trust for Ornithology for three years and at the Institut des Sciences de l'Évolution in Montpellier for seven years where he developed most of the ideas presented in this book. He is the main author and maintainer of the R package APE (Analysis of Phylogenetics and Evolution) Nota de contenido: First Steps in R for Phylogeneticists  Phylogenetic Data in R  Plotting Phylogenies  Phylogeny Estimation  Analysis of Macroevolution with Phylogenies  Developing and Implementing Phylogenetic Methods in R En línea: http://dx.doi.org/10.1007/9780387351001 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34814 Analysis of Phylogenetics and Evolution with R [documento electrónico] / Emmanuel Paradis ; SpringerLink (Online service) .  New York, NY : Springer New York, 2006 .  XII, 211 p. 50 illus : online resource.  (Use R) .
ISBN : 9780387351001
Idioma : Inglés (eng)
Palabras clave: Life sciences Biochemistry Bioinformatics Evolutionary biology Statistics Sciences Biochemistry, general Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: 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 reassess 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. Emmanuel Paradis is an evolutionary biologist at the Centre National de la Recherche Scientifique (CNRS) and the Institut de Recherche pour le Développement (IRD) in Montpellier. He received his Doctorate Diploma in population biology and ecology in 1993 at the University of Montpellier II. He has conducted empirical and theoretical research on birds, mammals, and fish. He worked at the British Trust for Ornithology for three years and at the Institut des Sciences de l'Évolution in Montpellier for seven years where he developed most of the ideas presented in this book. He is the main author and maintainer of the R package APE (Analysis of Phylogenetics and Evolution) Nota de contenido: First Steps in R for Phylogeneticists  Phylogenetic Data in R  Plotting Phylogenies  Phylogeny Estimation  Analysis of Macroevolution with Phylogenies  Developing and Implementing Phylogenetic Methods in R En línea: http://dx.doi.org/10.1007/9780387351001 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34814 Ejemplares
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Título : Applied Econometrics with R Tipo de documento: documento electrónico Autores: Kleiber, Christian ; SpringerLink (Online service) ; Zeileis, Achim Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Use R Número de páginas: X, 222 p Il.: online resource ISBN/ISSN/DL: 9780387773186 Idioma : Inglés (eng) Palabras clave: Game theory Economics, Mathematical Statistics Economic Econometrics Economics for Business/Economics/Mathematical Finance/Insurance Theory/Quantitative Economics/Mathematical Methods Quantitative Finance Theory, Social and Behav. Sciences Clasificación: 51 Matemáticas Resumen: This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents handson examples for a wide range of econometric models, from classical linear regression models for crosssection, time series or panel data and the common nonlinear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research. An R package accompanying this book, AER, is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.Rproject.org/package=AER. It contains some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.g., from popular textbooks. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care. The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike. Christian Kleiber is Professor of Econometrics and Statistics at Universität Basel, Switzerland. Achim Zeileis is Assistant Professor in the Dept. of Statistics and Mathematics at Wirtschaftsuniversität Wien, Austria. R users since version 0.64.0, they have been collaborating on econometric methodology in R, including several R packages, for the past eight years. Nota de contenido: Basics  Linear Regression  Diagnostics and Alternative Methods of Regression  Models of Microeconometrics  Time Series  Programming Your Own Analysis En línea: http://dx.doi.org/10.1007/9780387773186 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34232 Applied Econometrics with R [documento electrónico] / Kleiber, Christian ; SpringerLink (Online service) ; Zeileis, Achim .  New York, NY : Springer New York, 2008 .  X, 222 p : online resource.  (Use R) .
ISBN : 9780387773186
Idioma : Inglés (eng)
Palabras clave: Game theory Economics, Mathematical Statistics Economic Econometrics Economics for Business/Economics/Mathematical Finance/Insurance Theory/Quantitative Economics/Mathematical Methods Quantitative Finance Theory, Social and Behav. Sciences Clasificación: 51 Matemáticas Resumen: This is the first book on applied econometrics using the R system for statistical computing and graphics. It presents handson examples for a wide range of econometric models, from classical linear regression models for crosssection, time series or panel data and the common nonlinear models of microeconometrics such as logit, probit and tobit models, to recent semiparametric extensions. In addition, it provides a chapter on programming, including simulations, optimization, and an introduction to R tools enabling reproducible econometric research. An R package accompanying this book, AER, is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.Rproject.org/package=AER. It contains some 100 data sets taken from a wide variety of sources, the full source code for all examples used in the text plus further worked examples, e.g., from popular textbooks. The data sets are suitable for illustrating, among other things, the fitting of wage equations, growth regressions, hedonic regressions, dynamic regressions and time series models as well as models of labor force participation or the demand for health care. The goal of this book is to provide a guide to R for users with a background in economics or the social sciences. Readers are assumed to have a background in basic statistics and econometrics at the undergraduate level. A large number of examples should make the book of interest to graduate students, researchers and practitioners alike. Christian Kleiber is Professor of Econometrics and Statistics at Universität Basel, Switzerland. Achim Zeileis is Assistant Professor in the Dept. of Statistics and Mathematics at Wirtschaftsuniversität Wien, Austria. R users since version 0.64.0, they have been collaborating on econometric methodology in R, including several R packages, for the past eight years. Nota de contenido: Basics  Linear Regression  Diagnostics and Alternative Methods of Regression  Models of Microeconometrics  Time Series  Programming Your Own Analysis En línea: http://dx.doi.org/10.1007/9780387773186 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34232 Ejemplares
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Título : A Beginner's Guide to R Tipo de documento: documento electrónico Autores: Zuur, Alain F ; SpringerLink (Online service) ; Ieno, Elena N ; Meesters, Erik Editorial: New York, NY : Springer New York Fecha de publicación: 2009 Colección: Use R Número de páginas: XV, 220 p Il.: online resource ISBN/ISSN/DL: 9780387938370 Idioma : Inglés (eng) Palabras clave: Statistics Ecology and Computing/Statistics Programs Theoretical Ecology/Statistics for Life Sciences, Medicine, Health Sciences Clasificación: 51 Matemáticas Resumen: Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R. "Its biggest advantage is that it aims only to teach R...It organizes R commands very efficiently, with much teaching guidance included. I would describe this book as being handyit's the kind of book that you want to keep in your jacket pocket or backpack all the time, ready for use, like a Swiss Army knife." (Loveday Conquest, University of Washington) "Whilst several books focus on learning statistics in R..., the authors of this book fill a gap in the market by focusing on learning R whilst almost completely avoiding any statistical jargon...The fact that the authors have very extensive experience of teaching R to absolute beginners shines throughout." (Mark Mainwaring, Lancaster University) "Exactly what is needed...This is great, nice work. I love the ecological/biological examples; they will be an enormous help." (Andrew J. Tyne, University of NebraskaLincoln) Alain F. Zuur is senior statistician and director of Highland Statistics Ltd., a statistical consultancy company based in the UK. He has taught statistics to more than 5000 ecologists. He is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Elena N. Ieno is senior marine biologist and codirector at Highland Statistics Ltd. She has been involved in guiding PhD students on the design and analysis of ecological data. She is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Erik H.W.G. Meesters is a researcher at the Dutch Institute for Marine Resources and Ecosystem Studies (IMARES). He specializes in coral reef ecology and applied statistics and conducts research on North Sea benthos and seal ecology Nota de contenido: Getting Data into R  Accessing Variables and Managing Subsets of Data  Simple Functions  An Introduction to Basic Plotting Tools  Loops and Functions  Graphing Tools  An Introduction to the Lattice Package  Common R Mistakes En línea: http://dx.doi.org/10.1007/9780387938370 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33925 A Beginner's Guide to R [documento electrónico] / Zuur, Alain F ; SpringerLink (Online service) ; Ieno, Elena N ; Meesters, Erik .  New York, NY : Springer New York, 2009 .  XV, 220 p : online resource.  (Use R) .
ISBN : 9780387938370
Idioma : Inglés (eng)
Palabras clave: Statistics Ecology and Computing/Statistics Programs Theoretical Ecology/Statistics for Life Sciences, Medicine, Health Sciences Clasificación: 51 Matemáticas Resumen: Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R. "Its biggest advantage is that it aims only to teach R...It organizes R commands very efficiently, with much teaching guidance included. I would describe this book as being handyit's the kind of book that you want to keep in your jacket pocket or backpack all the time, ready for use, like a Swiss Army knife." (Loveday Conquest, University of Washington) "Whilst several books focus on learning statistics in R..., the authors of this book fill a gap in the market by focusing on learning R whilst almost completely avoiding any statistical jargon...The fact that the authors have very extensive experience of teaching R to absolute beginners shines throughout." (Mark Mainwaring, Lancaster University) "Exactly what is needed...This is great, nice work. I love the ecological/biological examples; they will be an enormous help." (Andrew J. Tyne, University of NebraskaLincoln) Alain F. Zuur is senior statistician and director of Highland Statistics Ltd., a statistical consultancy company based in the UK. He has taught statistics to more than 5000 ecologists. He is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Elena N. Ieno is senior marine biologist and codirector at Highland Statistics Ltd. She has been involved in guiding PhD students on the design and analysis of ecological data. She is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Erik H.W.G. Meesters is a researcher at the Dutch Institute for Marine Resources and Ecosystem Studies (IMARES). He specializes in coral reef ecology and applied statistics and conducts research on North Sea benthos and seal ecology Nota de contenido: Getting Data into R  Accessing Variables and Managing Subsets of Data  Simple Functions  An Introduction to Basic Plotting Tools  Loops and Functions  Graphing Tools  An Introduction to the Lattice Package  Common R Mistakes En línea: http://dx.doi.org/10.1007/9780387938370 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33925 Ejemplares
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Título : Business Analytics for Managers Tipo de documento: documento electrónico Autores: Jank, Wolfgang ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Use R Número de páginas: XI, 189 p. 100 illus., 63 illus. in color Il.: online resource ISBN/ISSN/DL: 9781461404064 Idioma : Inglés (eng) Palabras clave: Statistics for Business/Economics/Mathematical Finance/Insurance Clasificación: 51 Matemáticas Resumen: The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of datadriven decision making are plentiful and increasing on a daily basis. This book will motivate the need for data and datadriven solutions, using real data from real business scenarios. It will allow managers to better interact with personnel specializing in analytics by exposing managers and decision makers to the key ideas and concepts of datadriven decision making. Business Analytics for Managers conveys ideas and concepts from both statistics and data mining with the goal of extracting knowledge from real business data and actionable insight for managers. Throughout, emphasis placed on conveying datadriven thinking. While the ideas discussed in this book can be implemented using many different software solutions from many different vendors, it also provides a quickstart to one of the most powerful software solutions available. The main goals of this book are as follows: · To excite managers and decision makers about the potential that resides in data and the value that data analytics can add to business processes. · To provide managers with a basic understanding of the main concepts of data analytics and a common language to convey datadriven decision problems so they can better communicate with personnel specializing in data mining or statistics Nota de contenido: Introduction  Exploring & Discovering Data  Data Modeling I  Basics  Data Modeling II  Making Models More Flexible  Data Modeling III  Making Models More Selective  Data Modeling IV  Fine Tuning Your Model  Introduction to the Statistical Software R En línea: http://dx.doi.org/10.1007/9781461404064 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33223 Business Analytics for Managers [documento electrónico] / Jank, Wolfgang ; SpringerLink (Online service) .  New York, NY : Springer New York, 2011 .  XI, 189 p. 100 illus., 63 illus. in color : online resource.  (Use R) .
ISBN : 9781461404064
Idioma : Inglés (eng)
Palabras clave: Statistics for Business/Economics/Mathematical Finance/Insurance Clasificación: 51 Matemáticas Resumen: The practice of business is changing. More and more companies are amassing larger and larger amounts of data, and storing them in bigger and bigger data bases. Consequently, successful applications of datadriven decision making are plentiful and increasing on a daily basis. This book will motivate the need for data and datadriven solutions, using real data from real business scenarios. It will allow managers to better interact with personnel specializing in analytics by exposing managers and decision makers to the key ideas and concepts of datadriven decision making. Business Analytics for Managers conveys ideas and concepts from both statistics and data mining with the goal of extracting knowledge from real business data and actionable insight for managers. Throughout, emphasis placed on conveying datadriven thinking. While the ideas discussed in this book can be implemented using many different software solutions from many different vendors, it also provides a quickstart to one of the most powerful software solutions available. The main goals of this book are as follows: · To excite managers and decision makers about the potential that resides in data and the value that data analytics can add to business processes. · To provide managers with a basic understanding of the main concepts of data analytics and a common language to convey datadriven decision problems so they can better communicate with personnel specializing in data mining or statistics Nota de contenido: Introduction  Exploring & Discovering Data  Data Modeling I  Basics  Data Modeling II  Making Models More Flexible  Data Modeling III  Making Models More Selective  Data Modeling IV  Fine Tuning Your Model  Introduction to the Statistical Software R En línea: http://dx.doi.org/10.1007/9781461404064 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33223 Ejemplares
Signatura Medio Ubicación Sublocalización Sección Estado ningún ejemplar PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkNonlinear Regression with R / SpringerLink (Online service) ; Ritz, Christian ; Streibig, Jens Carl (2008)
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