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: 3845
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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

Amerika!

Amerika!

Auf der Suche nach dem Land der unbegrenzten Möglichkeiten

  • Author: Geert Mak
  • Publisher: Siedler Verlag
  • ISBN: 3641102766
  • Category: History
  • Page: 640
  • View: 5614
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Eine Reise zu den Wurzeln des großen amerikanischen Traums Geert Mak hat sich wieder auf den Weg gemacht. Der Bestsellerautor von »In Europa« ist quer durch die Landschaft, durch Geschichte und Gegenwart, ja durch die Köpfe und Herzen der USA gereist. Dabei trifft er unterschiedlichste Menschen, macht außergewöhnliche Beobachtungen und erzählt hinreißende Geschichten. Mit diesem Buch begibt sich Mak auf die Suche nach den Wurzeln des großen amerikanischen Traums und beschreibt die Mythen und das Selbstverständnis jenes Landes, das uns immer noch am meisten beschäftigt. Was ist aus dem amerikanischen Traum geworden, seit John Steinbeck 1960 die USA gemeinsam mit seinem berühmten Pudel Charley durchquert hat? Dieser Frage folgt der international bekannte Publizist Geert Mak und macht sich dafür selbst auf den Weg durch die Vereinigten Staaten, fernab ausgetrampelter Pfade, quer durch ein Land, das er liebt und zugleich kritisch betrachtet. Meile um Meile dringt er tiefer in das Land und seine Mythen, sein Selbstverständnis, seine Großartigkeit und Zerrissenheit vor. Seine Reise führt ihn von den großen Ostküstenstädten über die Kartoffelacker des Hinterlandes und die Prärie des mittleren Westens bis zum Pazifik. Er trifft Menschen – setzt sich an einen Tisch mit dem Farmer, dem Fabrikarbeiter, dem Fischer, dem Lehrer. Er streift durch die riesigen Malls und die Vororte, und er sucht nach den Wurzeln des Landes, das sich radikal verändert und doch den Glauben an den amerikanischen Traum bewahrt hat.

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: 8664
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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.

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: 3247
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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.

Dental Management of the Medically Compromised Patient

Dental Management of the Medically Compromised Patient

  • Author: James W. Little,Donald A. Falace
  • Publisher: N.A
  • ISBN: 9783769140309
  • Category: Sick
  • Page: 446
  • View: 7228
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Ökologie - Individuen, Populationen und Lebensgemeinschaften

Ökologie - Individuen, Populationen und Lebensgemeinschaften

  • Author: BEGON,TOWNSEND,HARPER
  • Publisher: Springer-Verlag
  • ISBN: 3034861567
  • Category: Juvenile Nonfiction
  • Page: 1032
  • View: 5688
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Environmental and Ecological Statistics with R

Environmental and Ecological Statistics with R

  • Author: Song S. Qian
  • Publisher: CRC Press
  • ISBN: 9781420062083
  • Category: Mathematics
  • Page: 440
  • View: 4514
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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many examples to illustrate the statistical models and presents R implementations of the models. The book first builds a foundation for conducting a simple data analysis task, such as exploratory data analysis and fitting linear regression models. It then focuses on statistical modeling, including linear and nonlinear models, classification and regression tree, and the generalized linear model. The text also discusses the use of simulation for model checking, provides tools for a critical assessment of the developed model, and explores multilevel regression models, which are a class of models that can have a broad impact in environmental and ecological data analysis. Based on courses taught by the author at Duke University, this book focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the processes of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

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: 1540
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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.

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: 4681
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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

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: 5358
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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.

R in a Nutshell

R in a Nutshell

  • Author: Joseph Adler
  • Publisher: O'Reilly Germany
  • ISBN: 3897216507
  • Category: Computers
  • Page: 768
  • View: 4445
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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.

Functional and Phylogenetic Ecology in R

Functional and Phylogenetic Ecology in R

  • Author: Nathan G. Swenson
  • Publisher: Springer Science & Business Media
  • ISBN: 1461495423
  • Category: Computers
  • Page: 212
  • View: 6910
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Functional and Phylogenetic Ecology in R is designed to teach readers to use R for phylogenetic and functional trait analyses. Over the past decade, a dizzying array of tools and methods were generated to incorporate phylogenetic and functional information into traditional ecological analyses. Increasingly these tools are implemented in R, thus greatly expanding their impact. Researchers getting started in R can use this volume as a step-by-step entryway into phylogenetic and functional analyses for ecology in R. More advanced users will be able to use this volume as a quick reference to understand particular analyses. The volume begins with an introduction to the R environment and handling relevant data in R. Chapters then cover phylogenetic and functional metrics of biodiversity; null modeling and randomizations for phylogenetic and functional trait analyses; integrating phylogenetic and functional trait information; and interfacing the R environment with a popular C-based program. This book presents a unique approach through its focus on ecological analyses and not macroevolutionary analyses. The author provides his own code, so that the reader is guided through the computational steps to calculate the desired metrics. This guided approach simplifies the work of determining which package to use for any given analysis. Example datasets are shared to help readers practice, and readers can then quickly turn to their own datasets.

Spatial Data Analysis in Ecology and Agriculture Using R

Spatial Data Analysis in Ecology and Agriculture Using R

  • Author: Richard E. Plant
  • Publisher: CRC Press
  • ISBN: 1439819149
  • Category: Mathematics
  • Page: 648
  • View: 2216
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Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. Written in terms of four data sets easily accessible online, this 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. Based on the author’s spatial data analysis course at the University of California, Davis, the book is intended for classroom use or self-study by graduate students and researchers in ecology, geography, and agricultural science with an interest in the analysis of spatial data.

Modeling and Simulation

Modeling and Simulation

  • Author: Hartmut Bossel
  • Publisher: Springer-Verlag
  • ISBN: 3663108228
  • Category: Technology & Engineering
  • Page: 484
  • View: 5519
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This book is the the English Language Version of the very successful German textbook, "Modellbildung und Simulation". It provides a self-contained and complete guide to the methods and mathematical background of modeling and simulation software of dynamic systems. Furthermore, an appropriate simulation software and a collection of dynamic system models (on the accompanying disk) are highlights of the book/software-Package.Dies ist die englischsprachige Ausgabe des sehr erfolgreichen Lehrbuches "Modellbildung und Simulation". Geboten wird eine vollständige Einführung in die Methoden der Simulation dynamischer Systeme, wobei auch der notwendige mathematische Hintergrund vermittelt wird. Außerdem ist eine Simulationssoftware Bestandteil des Werkes; auf der beiliegenden Diskette befinden sich ferner 50 Beispielsysteme ("Systemzoo"), die zur spielerischen Einübung der verwendeten Verfahren hilfreich sind.

Ecological Modeling for Resource Management

Ecological Modeling for Resource Management

  • Author: Virginia H. Dale
  • Publisher: Springer Science & Business Media
  • ISBN: 0387954937
  • Category: Computers
  • Page: 328
  • View: 6262
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This book will serve as a readable introduction to ecological modeling for people involved in resource management and will also review models for specific applications of interest to more experienced modelers. Successful uses of ecological models as well as discussions of important issues in modeling are addressed. The authors of this volume hope to close the gap between the state of the art in ecological modeling and the state of the practice in the use of models in management decision making.

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: 7088
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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

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: 6207
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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.

Datenanalyse mit Python

Datenanalyse mit Python

Auswertung von Daten mit Pandas, NumPy und IPython

  • Author: Wes McKinney
  • Publisher: O'Reilly
  • ISBN: 3960102143
  • Category: Computers
  • Page: 542
  • View: 1525
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Erfahren Sie alles über das Manipulieren, Bereinigen, Verarbeiten und Aufbereiten von Datensätzen mit Python: Aktualisiert auf Python 3.6, zeigt Ihnen dieses konsequent praxisbezogene Buch anhand konkreter Fallbeispiele, wie Sie eine Vielzahl von typischen Datenanalyse-Problemen effektiv lösen. Gleichzeitig lernen Sie die neuesten Versionen von pandas, NumPy, IPython und Jupyter kennen.Geschrieben von Wes McKinney, dem Begründer des pandas-Projekts, bietet Datenanalyse mit Python einen praktischen Einstieg in die Data-Science-Tools von Python. Das Buch eignet sich sowohl für Datenanalysten, für die Python Neuland ist, als auch für Python-Programmierer, die sich in Data Science und Scientific Computing einarbeiten wollen. Daten und zugehöriges Material des Buchs sind auf GitHub verfügbar.Aus dem Inhalt:Nutzen Sie die IPython-Shell und Jupyter Notebook für das explorative ComputingLernen Sie Grundfunktionen und fortgeschrittene Features von NumPy kennenSetzen Sie die Datenanalyse-Tools der pandasBibliothek einVerwenden Sie flexible Werkzeuge zum Laden, Bereinigen, Transformieren, Zusammenführen und Umformen von DatenErstellen Sie interformative Visualisierungen mit matplotlibWenden Sie die GroupBy-Mechanismen von pandas an, um Datensätzen zurechtzuschneiden, umzugestalten und zusammenzufassenAnalysieren und manipulieren Sie verschiedenste Zeitreihen-DatenFür diese aktualisierte 2. Auflage wurde der gesamte Code an Python 3.6 und die neuesten Versionen der pandas-Bibliothek angepasst. Neu in dieser Auflage: Informationen zu fortgeschrittenen pandas-Tools sowie eine kurze Einführung in statsmodels und scikit-learn.

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: 1094
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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