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Ecological Models And Data In R

Author : Benjamin M. Bolker
ISBN : 9781400840908
Genre : Nature
File Size : 52.48 MB
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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
Category: Nature

Ecological Models And Data In R

Author : Benjamin M. Bolker
ISBN : 9780691125220
Genre : Computers
File Size : 44.90 MB
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Introduction and background; Exploratory data analysis and graphics; Deterministic functions for ecological modeling; Probability and stochastic distributions for ecological modeling; Stochatsic simulation and power analysis; Likelihood and all that; Optimization and all that; Likelihood examples; Standar statistics revisited; Modeling variance; Dynamic models.
Category: Computers

Environmental And Ecological Statistics With R

Author : Song S. Qian
ISBN : 1420062085
Genre : Mathematics
File Size : 20.41 MB
Format : PDF, Docs
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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.
Category: Mathematics

A Practical Guide To Ecological Modelling

Author : Karline Soetaert
ISBN : 9781402086236
Genre : Science
File Size : 90.32 MB
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Mathematical modelling is an essential tool in present-day ecological research. Yet for many ecologists it is still problematic to apply modelling in their research. In our experience, the major problem is at the conceptual level: proper understanding of what a model is, how ecological relations can be translated consistently into mathematical equations, how models are solved, steady states calculated and interpreted. Many textbooks jump over these conceptual hurdles to dive into detailed formulations or the mathematics of solution. This book attempts to fill that gap. It introduces essential concepts for mathematical modelling, explains the mathematics behind the methods, and helps readers to implement models and obtain hands-on experience. Throughout the book, emphasis is laid on how to translate ecological questions into interpretable models in a practical way. The book aims to be an introductory textbook at the undergraduate-graduate level, but will also be useful to seduce experienced ecologists into the world of modelling. The range of ecological models treated is wide, from Lotka-Volterra type of principle-seeking models to environmental or ecosystem models, and including matrix models, lattice models and sequential decision models. All chapters contain a concise introduction into the theory, worked-out examples and exercises. All examples are implemented in the open-source package R, thus taking away problems of software availability for use of the book. All code used in the book is available on a dedicated website.
Category: Science

Bayesian Data Analysis In Ecology Using Linear Models With R Bugs And Stan

Author : Franzi Korner-Nievergelt
ISBN : 9780128016787
Genre : Science
File Size : 66.97 MB
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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
Category: Science

Introduction To Hierarchical Bayesian Modeling For Ecological Data

Author : Eric Parent
ISBN : 9781584889199
Genre : Mathematics
File Size : 64.36 MB
Format : PDF
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Making statistical modeling and inference more accessible to ecologists and related scientists, Introduction to Hierarchical Bayesian Modeling for Ecological Data gives readers a flexible and effective framework to learn about complex ecological processes from various sources of data. It also helps readers get started on building their own statistical models. The text begins with simple models that progressively become more complex and realistic through explanatory covariates and intermediate hidden states variables. When fitting the models to data, the authors gradually present the concepts and techniques of the Bayesian paradigm from a practical point of view using real case studies. They emphasize how hierarchical Bayesian modeling supports multidimensional models involving complex interactions between parameters and latent variables. Data sets, exercises, and R and WinBUGS codes are available on the authors’ website. This book shows how Bayesian statistical modeling provides an intuitive way to organize data, test ideas, investigate competing hypotheses, and assess degrees of confidence of predictions. It also illustrates how conditional reasoning can dismantle a complex reality into more understandable pieces. As conditional reasoning is intimately linked with Bayesian thinking, considering hierarchical models within the Bayesian setting offers a unified and coherent framework for modeling, estimation, and prediction.
Category: Mathematics

A Biologist S Guide To Mathematical Modeling In Ecology And Evolution

Author : Sarah P. Otto
ISBN : 9781400840915
Genre : Science
File Size : 71.37 MB
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Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own. The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction. Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists. A how-to guide for developing new mathematical models in biology Provides step-by-step recipes for constructing and analyzing models Interesting biological applications Explores classical models in ecology and evolution Questions at the end of every chapter Primers cover important mathematical topics Exercises with answers Appendixes summarize useful rules Labs and advanced material available
Category: Science

Models For Ecological Data

Author : James Samuel Clark
ISBN : 0691121788
Genre : Science
File Size : 40.17 MB
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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
Category: Science

Bayesian Methods For Ecology

Author : Michael A. McCarthy
ISBN : 9781139463874
Genre : Science
File Size : 43.67 MB
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The interest in using Bayesian methods in ecology is increasing, however many ecologists have difficulty with conducting the required analyses. McCarthy bridges that gap, using a clear and accessible style. The text also incorporates case studies to demonstrate mark-recapture analysis, development of population models and the use of subjective judgement. The advantages of Bayesian methods, are also described here, for example, the incorporation of any relevant prior information and the ability to assess the evidence in favour of competing hypotheses. Free software is available as well as an accompanying web-site containing the data files and WinBUGS codes. Bayesian Methods for Ecology will appeal to academic researchers, upper undergraduate and graduate students of Ecology.
Category: Science

Mixed Effects Models And Extensions In Ecology With R

Author : Alain Zuur
ISBN : 0387874585
Genre : Science
File Size : 57.12 MB
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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.
Category: Science