Using R for Modelling and Quantitative Methods in Fisheries PDF Download
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Author: Malcolm Haddon
Publisher: CRC Press
ISBN: 1000079236
Category : Technology & Engineering
Languages : en
Pages : 353
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Book Description
Using R for Modelling and Quantitative Methods in Fisheries has evolved and been adapted from an earlier book by the same author and provides a detailed introduction to analytical methods commonly used by fishery scientists, ecologists, and advanced students using the open-source software R as a programming tool. Some knowledge of R is assumed, as this is a book about using R, but an introduction to the development and working of functions, and how one can explore the contents of R functions and packages, is provided. The example analyses proceed step-by-step using code listed in the book and from the book’s companion R package, MQMF, available from GitHub and the standard archive, CRAN. The examples are designed to be simple to modify so the reader can quickly adapt the methods described to use with their own data. A primary aim of the book is to be a useful resource to natural resource practitioners and students. Featured Chapters: Model Parameter Estimation provides a detailed explanation of the requirements and steps involved in fitting models to data, using R and, mainly, maximum likelihood methods. On Uncertainty uses R to implement bootstrapping, likelihood profiles, asymptotic errors, and Bayesian posteriors to characterize any uncertainty in an analysis. The use of the Monte Carlo Markov Chain methodology is examined in some detail. Surplus Production Models applies all the methods examined in the earlier parts of the book to conducting a stock assessment. This included fitting alternative models to the available data, characterizing the uncertainty in different ways, and projecting the optimum models forward in time as the basis for providing useful management advice.
Author: Malcolm Haddon
Publisher: CRC Press
ISBN: 1000079236
Category : Technology & Engineering
Languages : en
Pages : 353
Get Book Here
Book Description
Using R for Modelling and Quantitative Methods in Fisheries has evolved and been adapted from an earlier book by the same author and provides a detailed introduction to analytical methods commonly used by fishery scientists, ecologists, and advanced students using the open-source software R as a programming tool. Some knowledge of R is assumed, as this is a book about using R, but an introduction to the development and working of functions, and how one can explore the contents of R functions and packages, is provided. The example analyses proceed step-by-step using code listed in the book and from the book’s companion R package, MQMF, available from GitHub and the standard archive, CRAN. The examples are designed to be simple to modify so the reader can quickly adapt the methods described to use with their own data. A primary aim of the book is to be a useful resource to natural resource practitioners and students. Featured Chapters: Model Parameter Estimation provides a detailed explanation of the requirements and steps involved in fitting models to data, using R and, mainly, maximum likelihood methods. On Uncertainty uses R to implement bootstrapping, likelihood profiles, asymptotic errors, and Bayesian posteriors to characterize any uncertainty in an analysis. The use of the Monte Carlo Markov Chain methodology is examined in some detail. Surplus Production Models applies all the methods examined in the earlier parts of the book to conducting a stock assessment. This included fitting alternative models to the available data, characterizing the uncertainty in different ways, and projecting the optimum models forward in time as the basis for providing useful management advice.
Author: Malcolm Haddon
Publisher: CRC Press
ISBN: 9781584881773
Category : Mathematics
Languages : en
Pages : 428
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Book Description
Quantitative methods and mathematical modelling are of critical importance to fishery science and management but, until now, there has been no book that offers the sharp focus, methodological detail, and practical examples needed by non-specialist fishery scientists and managers, and ecologists. Modelling and Quantitative Methods in Fisheries fills that void. To date, methodology books in fisheries science have been limited to cookbook approach to problems; simple compilations; or expositions in which either too much theory or insufficient methodological detail is given. The text is organized into three sections: an introduction to modelling in fisheries and ecology, a straight methodology section covering a range of methods, and a section focusing on specific fields in fisheries science. This book is timely as it addresses a topic of recent debate in fisheries and ecology, describing and comparing the uses of Least Squares, Maximum Likelihood, and Bayesian quantitative methods. Designed as stand-alone units, each chapter provides examples from both classic and recent literature and comes with dedicated Excel spreadsheets that permit you to delve into every detail of the analysis. All of these spreadsheets serve as active examples, which can easily be modified and customized and can be used as templates for analyzing your own data. The spreadsheets permit you to learn at your own speed and cover the simplest linear regression to the more complex non-linear modelling using maximum likelihood. Data analysis and modelling are best learned by doing and not just by reading. This book illustrates, step by step, the analyses it covers. More detailed in terms of introductory quantitative methods and modelling as applied to fisheries than any other book available, Modelling and Quantitative Methods in Fisheries gives you the advantage by supplying the full details of the analysis so that understanding the material is a matter of following the book.
Author: R. Hilborn
Publisher: Springer Science & Business Media
ISBN: 1461535980
Category : Science
Languages : en
Pages : 575
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Book Description
This book really began in 1980 with our first microcomputer, an Apple II +. The great value of the Apple II + was that we could take the computer programs we had been building on mainframe and mini-computers, and make them available to the many fisheries biologists who also had Apple II + 's. About 6 months after we got our first Apple, John Glaister came through Vancouver and saw what we were doing and realized that his agency (New South Wales State Fisheries) had the same equipment and could run the same programs. John organized a training course in Australia where we showed about 25 Australian fisheries biologists how to use microcomputers to do many standard fisheries analyses. In the process of organizing this and sub sequent courses we developed a series of lecture notes. Over the last 10 years these notes have evolved into the chapters of this book.
Author: Derek H. Ogle
Publisher: CRC Press
ISBN: 1482235226
Category : Mathematics
Languages : en
Pages : 327
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Book Description
A How-To Guide for Conducting Common Fisheries-Related Analyses in R Introductory Fisheries Analyses with R provides detailed instructions on performing basic fisheries stock assessment analyses in the R environment. Accessible to practicing fisheries scientists as well as advanced undergraduate and graduate students, the book demonstrates the flexibility and power of R, offers insight into the reproducibility of script-based analyses, and shows how the use of R leads to more efficient and productive work in fisheries science. The first three chapters present a minimal introduction to the R environment that builds a foundation for the fisheries-specific analyses in the remainder of the book. These chapters help you become familiar with R for basic fisheries analyses and graphics. Subsequent chapters focus on methods to analyze age comparisons, age-length keys, size structure, weight-length relationships, condition, abundance (from capture-recapture and depletion data), mortality rates, individual growth, and the stock-recruit relationship. The fundamental statistical methods of linear regression, analysis of variance (ANOVA), and nonlinear regression are demonstrated within the contexts of these common fisheries analyses. For each analysis, the author completely explains the R functions and provides sufficient background information so that you can confidently implement each method. Web Resource The author’s website at http://derekogle.com/IFAR/ includes the data files and R code for each chapter, enabling you to reproduce the results in the book as well as create your own scripts. The site also offers supplemental code for more advanced analyses and practice exercises for every chapter.
Author: Enrique Garcia Ceja
Publisher: CRC Press
ISBN: 1000484238
Category : Psychology
Languages : en
Pages : 434
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Book Description
Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as: data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial. Features: Build supervised machine learning models to predict indoor locations based on WiFi signals, recognize physical activities from smartphone sensors and 3D skeleton data, detect hand gestures from accelerometer signals, and so on. Program your own ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning algorithms to discover criminal behavioral patterns. Build deep learning neural networks with TensorFlow and Keras to classify muscle activity from electromyography signals and Convolutional Neural Networks to detect smiles in images. Evaluate the performance of your models in traditional and multi-user settings. Build anomaly detection models such as Isolation Forests and autoencoders to detect abnormal fish behaviors. This book is intended for undergraduate/graduate students and researchers from ubiquitous computing, behavioral ecology, psychology, e-health, and other disciplines who want to learn the basics of machine learning and deep learning and for the more experienced individuals who want to apply machine learning to analyze behavioral data.
Author: Malcolm Haddon
Publisher: CRC Press
ISBN: 1439894175
Category : Mathematics
Languages : en
Pages : 452
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Book Description
With numerous real-world examples, Modelling and Quantitative Methods in Fisheries, Second Edition provides an introduction to the analytical methods used by fisheries' scientists and ecologists. By following the examples using Excel, readers see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workb
Author: Malcolm Haddon
Publisher: CRC Press
ISBN: 1420010816
Category : Mathematics
Languages : en
Pages : 468
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Book Description
Revised and restructured, Modeling and Quantitative Methods in Fisheries, Second Edition provides an accessible introduction to quantitative methods in fisheries. This book features new material on tests and comparisons as well as new chapters on length-based models and estimating uncertainty using Bayesian methods. It presents a structured, step-by-step approach that introduces the material in a logical sequence. This text covers a range of topics such as simple linear regression, complex nonlinear modeling, methodology, and specific fields in fisheries. It also includes numerous real world examples implemented in Excel, with workbooks for all examples available for download on the web.
Author: Jeffrey C. Carrier
Publisher: CRC Press
ISBN: 1000542068
Category : Nature
Languages : en
Pages : 841
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Book Description
Biology of Sharks and Their Relatives is an award-winning and groundbreaking exploration of the fundamental elements of the taxonomy, systematics, physiology, and ecology of sharks, skates, rays, and chimera. This edition presents current research as well as traditional models, to provide future researchers with solid historical foundations in shark research as well as presenting current trends from which to develop new frontiers in their own work. Traditional areas of study such as age and growth, reproduction, taxonomy and systematics, sensory biology, and ecology are updated with contemporary research that incorporates emerging techniques including molecular genetics, exploratory techniques in artificial insemination, and the rapidly expanding fields of satellite tracking, remote sensing, accelerometry, and imaging. With two new editors and 90 contributors from the US, UK, South Africa, Portugal, France, Canada, New Zealand, Australia, India, Palau, United Arab Emirates, Micronesia, Sweden, Argentina, Indonesia, Cameroon, and the Netherlands, this third edition is the most global and comprehensive yet. It adds six new chapters representing extensive studies of health, stress, disease and pathology, and social structure, and continues to explore elasmobranch ecological roles and interactions with their habitats. The book concludes with a comprehensive review of conservation policies, management, and strategies, as well as consideration of the potential effects of impending climate change. Presenting cohesive and integrated coverage of key topics and discussing technological advances used in modern shark research, this revised edition offers a well-rounded picture for students and researchers.
Author: John Verzani
Publisher: CRC Press
ISBN: 1315360306
Category : Computers
Languages : en
Pages : 522
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Book Description
The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
Author: Michael J. Crawley
Publisher: John Wiley & Sons
ISBN: 9780470515068
Category : Mathematics
Languages : en
Pages : 953
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Book Description
The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences.
