Search Results for "ecological-models-and-data-in-r"

Ecological Models and Data in R

Ecological Models and Data in R

  • Author: Benjamin M. Bolker
  • Publisher: Princeton University Press
  • ISBN: 1400840902
  • Category: Nature
  • Page: 408
  • View: 9184
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Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics. Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R Step-by-step instructions for fitting models to messy, real-world data Balanced view of different statistical approaches Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling) Techniques for data manipulation and graphical display Companion Web site with data and R code for all examples

e-Study Guide for: Ecological Models and Data in R by Benjamin M. Bolker, ISBN 9780691125220

e-Study Guide for: Ecological Models and Data in R by Benjamin M. Bolker, ISBN 9780691125220

  • Author: Cram101 Textbook Reviews
  • Publisher: Cram101 Textbook Reviews
  • ISBN: 1478430540
  • Category: Education
  • Page: 54
  • View: 2120
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Never Highlight a Book Again! Just the FACTS101 study guides give the student the textbook outlines, highlights, practice quizzes and optional access to the full practice tests for their textbook.

R in a Nutshell

R in a Nutshell

  • Author: Joseph Adler
  • Publisher: O'Reilly Germany
  • ISBN: 3897216507
  • Category: Computers
  • Page: 768
  • View: 2057
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Wozu sollte man R lernen? Da gibt es viele Gründe: Weil man damit natürlich ganz andere Möglichkeiten hat als mit einer Tabellenkalkulation wie Excel, aber auch mehr Spielraum als mit gängiger Statistiksoftware wie SPSS und SAS. Anders als bei diesen Programmen hat man nämlich direkten Zugriff auf dieselbe, vollwertige Programmiersprache, mit der die fertigen Analyse- und Visualisierungsmethoden realisiert sind – so lassen sich nahtlos eigene Algorithmen integrieren und komplexe Arbeitsabläufe realisieren. Und nicht zuletzt, weil R offen gegenüber beliebigen Datenquellen ist, von der einfachen Textdatei über binäre Fremdformate bis hin zu den ganz großen relationalen Datenbanken. Zudem ist R Open Source und erobert momentan von der universitären Welt aus die professionelle Statistik. R kann viel. Und Sie können viel mit R machen – wenn Sie wissen, wie es geht. Willkommen in der R-Welt: Installieren Sie R und stöbern Sie in Ihrem gut bestückten Werkzeugkasten: Sie haben eine Konsole und eine grafische Benutzeroberfläche, unzählige vordefinierte Analyse- und Visualisierungsoperationen – und Pakete, Pakete, Pakete. Für quasi jeden statistischen Anwendungsbereich können Sie sich aus dem reichen Schatz der R-Community bedienen. Sprechen Sie R! Sie müssen Syntax und Grammatik von R nicht lernen – wie im Auslandsurlaub kommen Sie auch hier gut mit ein paar aufgeschnappten Brocken aus. Aber es lohnt sich: Wenn Sie wissen, was es mit R-Objekten auf sich hat, wie Sie eigene Funktionen schreiben und Ihre eigenen Pakete schnüren, sind Sie bei der Analyse Ihrer Daten noch flexibler und effektiver. Datenanalyse und Statistik in der Praxis: Anhand unzähliger Beispiele aus Medizin, Wirtschaft, Sport und Bioinformatik lernen Sie, wie Sie Daten aufbereiten, mithilfe der Grafikfunktionen des lattice-Pakets darstellen, statistische Tests durchführen und Modelle anpassen. Danach werden Ihnen Ihre Daten nichts mehr verheimlichen.

Mixed Effects Models and Extensions in Ecology with R

Mixed Effects Models and Extensions in Ecology with R

  • Author: Alain Zuur,Elena N. Ieno,Neil Walker,Anatoly A. Saveliev,Graham M. Smith
  • Publisher: Springer Science & Business Media
  • ISBN: 9780387874586
  • Category: Science
  • Page: 574
  • View: 2752
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This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.

R für Dummies

R für Dummies

  • Author: Andrie de Vries,Robert Leidenfrost
  • Publisher: John Wiley & Sons
  • ISBN: 3527812520
  • Category: Computers
  • Page: 414
  • View: 6370
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Ecological Statistics

Ecological Statistics

Contemporary theory and application

  • Author: Gordon A. Fox,Simoneta Negrete-Yankelevich,Vinicio J. Sosa
  • Publisher: OUP Oxford
  • ISBN: 0191652881
  • Category: Science
  • Page: 400
  • View: 6858
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The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.

Ökologie

Ökologie

  • Author: Michael Begon,Robert W. Howarth,Colin R. Townsend
  • Publisher: Springer-Verlag
  • ISBN: 3662499061
  • Category: Science
  • Page: 599
  • View: 5055
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Dieses Ökologie-Lehrbuch führt in leicht verständlicher Weise in die Grundlagen – von den theoretischen Fundamenten bis zu Ihren praktischen Anwendungen - ein. Durchgehend farbige Abbildungen, einfache didaktische Elemente und eine Fülle an Beispielen machen dieses Buch zu einem idealen Einstieg in die Ökologie für Studierende aller Studienabschnitte. Neu in der 3. Auflage sind zusätzliche Erläuterungen in Form von Sprechblasen in den Abbildungen und die neue Gliederung des Textes in 5 Abschnitte. Ökosystem und Biogeochemie werden ausführlicher behandelt und hunderte neuer Studien sowohl für die grundlegenden als auch für die angewandten Aspekte der Ökologie werden einbezogen.

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

  • Author: Franzi Korner-Nievergelt,Tobias Roth,Stefanie von Felten,Jérôme Guélat,Bettina Almasi,Pius Korner-Nievergelt
  • Publisher: Academic Press
  • ISBN: 0128016787
  • Category: Science
  • Page: 328
  • View: 7056
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Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types. Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest Written in a step-by-step approach that allows for eased understanding by non-statisticians Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data All example data as well as additional functions are provided in the R-package blmeco

Environmental and Ecological Statistics with R, Second Edition

Environmental and Ecological Statistics with R, Second Edition

  • Author: Song S. Qian
  • Publisher: CRC Press
  • ISBN: 1498728731
  • Category: Mathematics
  • Page: 560
  • View: 6768
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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

A Primer of Ecology with R

A Primer of Ecology with R

  • Author: M. Henry Stevens
  • Publisher: Springer Science & Business Media
  • ISBN: 0387898824
  • Category: Science
  • Page: 388
  • View: 6671
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Provides simple explanations of the important concepts in population and community ecology. Provides R code throughout, to illustrate model development and analysis, as well as appendix introducing the R language. Interweaves ecological content and code so that either stands alone. Supplemental web site for additional code.

Environmental and Ecological Statistics with R, Second Edition

Environmental and Ecological Statistics with R, Second Edition

  • Author: Song S. Qian
  • Publisher: CRC Press
  • ISBN: 1498728758
  • Category: Mathematics
  • Page: 560
  • View: 9285
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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS

Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS

Volume 1:Prelude and Static Models

  • Author: Marc Kery,J. Andrew Royle
  • Publisher: Academic Press
  • ISBN: 0128014865
  • Category: Nature
  • Page: 808
  • View: 6184
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Applied Hierarchical Modeling in Ecology: Distribution, Abundance, Species Richness offers a new synthesis of the state-of-the-art of hierarchical models for plant and animal distribution, abundance, and community characteristics such as species richness using data collected in metapopulation designs. These types of data are extremely widespread in ecology and its applications in such areas as biodiversity monitoring and fisheries and wildlife management. This first volume explains static models/procedures in the context of hierarchical models that collectively represent a unified approach to ecological research, taking the reader from design, through data collection, and into analyses using a very powerful class of models. Applied Hierarchical Modeling in Ecology, Volume 1 serves as an indispensable manual for practicing field biologists, and as a graduate-level text for students in ecology, conservation biology, fisheries/wildlife management, and related fields. Provides a synthesis of important classes of models about distribution, abundance, and species richness while accommodating imperfect detection Presents models and methods for identifying unmarked individuals and species Written in a step-by-step approach accessible to non-statisticians and provides fully worked examples that serve as a template for readers' analyses Includes companion website containing data sets, code, solutions to exercises, and further information

Hierarchical Modeling and Inference in Ecology

Hierarchical Modeling and Inference in Ecology

The Analysis of Data from Populations, Metapopulations and Communities

  • Author: J. Andrew Royle,Robert M. Dorazio
  • Publisher: Elsevier
  • ISBN: 0080559255
  • Category: Science
  • Page: 464
  • View: 2894
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A guide to data collection, modeling and inference strategies for biological survey data using Bayesian and classical statistical methods. This book describes a general and flexible framework for modeling and inference in ecological systems based on hierarchical models, with a strict focus on the use of probability models and parametric inference. Hierarchical models represent a paradigm shift in the application of statistics to ecological inference problems because they combine explicit models of ecological system structure or dynamics with models of how ecological systems are observed. The principles of hierarchical modeling are developed and applied to problems in population, metapopulation, community, and metacommunity systems. The book provides the first synthetic treatment of many recent methodological advances in ecological modeling and unifies disparate methods and procedures. The authors apply principles of hierarchical modeling to ecological problems, including * occurrence or occupancy models for estimating species distribution * abundance models based on many sampling protocols, including distance sampling * capture-recapture models with individual effects * spatial capture-recapture models based on camera trapping and related methods * population and metapopulation dynamic models * models of biodiversity, community structure and dynamics * Wide variety of examples involving many taxa (birds, amphibians, mammals, insects, plants) * Development of classical, likelihood-based procedures for inference, as well as Bayesian methods of analysis * Detailed explanations describing the implementation of hierarchical models using freely available software such as R and WinBUGS * Computing support in technical appendices in an online companion web site

Alexander von Humboldt und die Erfindung der Natur

Alexander von Humboldt und die Erfindung der Natur

  • Author: Andrea Wulf
  • Publisher: C. Bertelsmann Verlag
  • ISBN: 3641195500
  • Category: Biography & Autobiography
  • Page: 560
  • View: 4649
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Was hat Alexander von Humboldt, der vor mehr als 150 Jahren starb, mit Klimawandel und Nachhaltigkeit zu tun? Der Naturforscher und Universalgelehrte, nach dem nicht nur unzählige Straßen, Pflanzen und sogar ein »Mare« auf dem Mond benannt sind, hat wie kein anderer Wissenschaftler unser Verständnis von Natur als lebendigem Ganzen, als Kosmos, in dem vom Winzigsten bis zum Größten alles miteinander verbunden ist und dessen untrennbarer Teil wir sind, geprägt. Die Historikerin Andrea Wulf stellt in ihrem vielfach preisgekrönten – so auch mit dem Bayerischen Buchpreis 2016 – Buch Humboldts Erfindung der Natur, die er radikal neu dachte, ins Zentrum ihrer Erkundungsreise durch sein Leben und Werk. Sie folgt den Spuren des begnadeten Netzwerkers und zeigt, dass unser heutiges Wissen um die Verwundbarkeit der Erde in Humboldts Überzeugungen verwurzelt ist. Ihm heute wieder zu begegnen, mahnt uns, seine Erkenntnisse endlich zum Maßstab unseres Handelns zu machen – um unser aller Überleben willen.

Models for Ecological Data

Models for Ecological Data

An Introduction

  • Author: James Samuel Clark
  • Publisher: N.A
  • ISBN: 9780691121789
  • Category: Science
  • Page: 617
  • View: 1427
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"Clark brings emerging statistical approaches alive by putting the ecology first. Writing from the perspective of a field ecologist who must confront complex data without suppressing important detail, Clark describes new methods that are well matched to the richness of real ecological data. At last we have a text that makes these tools accessible to ecologists."--Stephen R. Carpenter, University of Wisconsin, Madison "Jim Clark has been able to pitch his message just right; one can see the ecological forest "and" the statistical, distributional, and computational trees at the same time. By reading this book, statisticians will gain an appreciation for the complexity of models in the ecological and environmental sciences, and ecologists will see the potential for hierarchical statistical modeling in their research arenas. Clark explains his material extremely well, but he is also rigorous in his statistical developments."--Noel Cressie, Ohio State University "Clark's book is monumental--I don't think there is any other source that provides this range of sources and methods. He presents a huge amount of useful material, focusing on the development and application of Bayesian hierarchical models for the analysis of ecological and environmental models. It's hard to imagine finding such a collection of information--the results of extensive experience with recent ecological, environmental, and statistical literature--in one place. And I heartily agree with the author's philosophical stances on simplicity and complexity, statistical pragmatism, and the need for common sense."--Benjamin Bolker, University of Florida "I strongly believe that this is potentially a landmark book in ecology. Its integration of modern statistical methods and ecological theory and data is fundamentally new. The book will train ecologists and other quantitative scientists in the 'new modeling techniques' that are becoming ever more prevalent in their field. In particular, the book describes how one should deal with complicated problems in which there is uncertainty in data, model, and parameters. James Clark does a wonderful job of integrating modern likelihood-based statistical methods as well as describing and demonstrating the advantages of the Bayesian approach."--Christopher K. Wikle, University of Missouri, Columbia

Hot Zone

Hot Zone

Ebola, das tödliche Virus

  • Author: Richard Preston
  • Publisher: Knaur eBook
  • ISBN: 3426434571
  • Category: Health & Fitness
  • Page: 368
  • View: 2114
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Richard Prestons populärwissenschaftlicher Tatsachen-Thriller liest sich spannender als jeder Horror-Roman. Preston berichtet darin über die ersten Infektionen mit dem Ebola-Virus vor über vierzig Jahren. Sein Tatsachenthriller von 1998 ist immer noch hochaktuell, wie die schreckliche Ebola-Epidemie in Zentralafrika gerade zeigt. Ebola gehört zu den gefährlichsten Killerviren. Diese aus dem Afrikanischen Regenwald stammenden sogenannten „Filoviren“, können einen Menschen auf grausamste Art und Weise töten. Das Virus löst innerhalb weniger Tage die inneren Organe auf, und der Erkrankte verblutet von innen. Das Virus ist zudem extrem ansteckend, und weltweit gibt es immer noch kein wirksames Heilmittel dagegen. Deshalb ist es nicht auszuschließen, dass die Menschheit eines Tages einer Seuche wie Ebola erliegen könnte. Preston schildert, wie der Ebola-Erreger über Affen, die für medizinische Versuche importiert wurden, schließlich nach Amerika kommt. In einem kleinen Labor in Reston, USA, verbreitet er Angst und Schrecken. Als sich die Seuche unter den im Quarantänelager zusammengepferchten Affen ausbreitet, rufen die Betreiber der Anlage die Gesundheitsbehörden zu Hilfe. Bald müssen die Wissenschaftler feststellen, dass sich das Virus inzwischen nicht nur durch Kontakt, sondern auch durch die Luft verbreiten kann.

Ecological Modeling in Risk Assessment

Ecological Modeling in Risk Assessment

Chemical Effects on Populations, Ecosystems, and Landscapes

  • Author: Robert A. Pastorok,Steven M. Bartell,Scott Ferson,Lev R. Ginzburg
  • Publisher: CRC Press
  • ISBN: 1420032321
  • Category: Technology & Engineering
  • Page: 328
  • View: 6623
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Toxic chemicals can exert effects on all levels of the biological hierarchy, from cells to organs to organisms to populations to entire ecosystems. However, most risk assessment models express their results in terms of effects on individual organisms, without corresponding information on how populations, groups of species, or whole ecosystems may respond to chemical stressors. Ecological Modeling in Risk Assessment: Chemical Effects on Populations, Ecosystems, and Landscapes takes a new approach by compiling and evaluating models that can be used in assessing risk at the population, ecosystem, and landscape levels. The authors give an overview of the current process of ecological risk assessment for toxic chemicals and of how modeling of populations, ecosystems, and landscapes could improve the status quo. They present a classification of ecological models and explain the differences between population, ecosystem, landscape, and toxicity-extrapolation models. The authors describe the model evaluation process and define evaluation criteria. Finally, the results of the model evaluations are presented in a concise format with recommendations on modeling approaches to use now and develop further. The authors present and evaluate various models on the basis of their realism and complexity, prediction of relevant assessment endpoints, treatment of uncertainty, regulatory acceptance, resource efficiency, and other criteria. They provide models that will improve the ecological relevance of risk assessments and make data collection more cost-effective. Ecological Modeling in Risk Assessment serves as a reference for selecting and applying the best models when performing a risk assessment.

Spatial Data Analysis in Ecology and Agriculture Using R, Second Edition

Spatial Data Analysis in Ecology and Agriculture Using R, Second Edition

  • Author: Richard E. Plant
  • Publisher: CRC Press
  • ISBN: 9780815392750
  • Category: Business & Economics
  • Page: 705
  • View: 519
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Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https: //www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.

Habitat Suitability and Distribution Models

Habitat Suitability and Distribution Models

with Applications in R

  • Author: Antoine Guisan,Wilfried Thuiller,Niklaus E. Zimmermann
  • Publisher: Cambridge University Press
  • ISBN: 0521765137
  • Category: Nature
  • Page: 498
  • View: 5431
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This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.