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Beginner S Guide To Spatial Temporal And Spatial Temporal Ecological Data Analysis With R Inla Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Beginner S Guide To Spatial Temporal And Spatial Temporal Ecological Data Analysis With R Inla book. This book definitely worth reading, it is an incredibly well-written.
Author : Alain F. Zuur,Elena N. Ieno,Anatoly A. Saveliev
Publisher : Unknown
Page : 362 pages
File Size : 41,5 Mb
Release : 2017
Category : Ecology
ISBN : 0957174195
Author : Elias T. Krainski,Virgilio Gómez-Rubio,Haakon Bakka,Amanda Lenzi,Daniela Castro-Camilo,Daniel Simpson,Finn Lindgren,Håvard Rue
Publisher : CRC Press
Page : 224 pages
File Size : 54,9 Mb
Release : 2018-12-07
Category : Mathematics
ISBN : 9780429628214
Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
Author : Marta Blangiardo,Michela Cameletti
Publisher : John Wiley & Sons
Page : 321 pages
File Size : 45,6 Mb
Release : 2015-06-02
Category : Mathematics
ISBN : 9781118326558
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented to real data problems. The numerous examples from the fields of epidemiology, biostatistics and social science all are coded in the R package R-INLA, which has proven to be a valid alternative to the commonly used Markov Chain Monte Carlo simulations
Author : Delphis F. Levia,Darryl E. Carlyle-Moses,Shin'ichi Iida,Beate Michalzik,Kazuki Nanko,Alexander Tischer
Publisher : Springer Nature
Page : 629 pages
File Size : 42,8 Mb
Release : 2020-02-05
Category : Science
ISBN : 9783030260866
The United Nations has declared 2018-2028 as the International Decade for Action on Water for Sustainable Development. This is a timely designation. In an increasingly thirsty world, the subject of forest-water interactions is of critical importance to the achievement of sustainability goals. The central underlying tenet of this book is that the hydrologic community can conduct better science and make a more meaningful impact to the world’s water crisis if scientists are: (1) better equipped to utilize new methods and harness big data from either or both high-frequency sensors and long-term research watersheds; and (2) aware of new developments in our process-based understanding of the hydrological cycle in both natural and urban settings. Accordingly, this forward-looking book delves into forest-water interactions from multiple methodological, statistical, and process-based perspectives (with some chapters featuring data sets and open-source R code), concluding with a chapter on future forest hydrology under global change. Thus, this book describes the opportunities of convergence in high-frequency sensing, big data, and open source software to catalyze more comprehensive understanding of forest-water interactions. The book will be of interest to researchers, graduate students, and advanced undergraduates in an array of disciplines, including hydrology, forestry, ecology, botany, and environmental engineering.
Author : Sujit Sahu
Publisher : CRC Press
Page : 385 pages
File Size : 48,8 Mb
Release : 2022-02-23
Category : Mathematics
ISBN : 9781000543698
Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. Key features of the book: • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc • Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.
Author : Jason Newton,Gabriele Stowasser,Rona A. R. McGill
Publisher : Frontiers Media SA
Page : 212 pages
File Size : 52,8 Mb
Release : 2023-09-29
Category : Science
ISBN : 9782832533925
Author : Robin Lovelace,Jakub Nowosad,Jannes Muenchow
Publisher : CRC Press
Page : 335 pages
File Size : 44,9 Mb
Release : 2019-03-22
Category : Mathematics
ISBN : 9781351396905
Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.
Author : Jesús Ernesto Arias González,Anastazia T. Banaszak,Iliana B. Baums,Edwin A. Hernandez-Delgado,Carlos Prada,Baruch Rinkevich,Sergio Rossi
Publisher : Frontiers Media SA
Page : 241 pages
File Size : 40,5 Mb
Release : 2022-06-14
Category : Science
ISBN : 9782889763498
Author : Marie-Josée Fortin,Mark R. T. Dale
Publisher : Cambridge University Press
Page : 386 pages
File Size : 52,6 Mb
Release : 2005-04-21
Category : Mathematics
ISBN : 0521804345
An overview of the wide range of spatial statistics available to analyse ecological data.
Author : Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio
Publisher : Springer Science & Business Media
Page : 405 pages
File Size : 48,5 Mb
Release : 2013-06-21
Category : Medical
ISBN : 9781461476184
Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.
Author : Mary C. Fabrizio,Mark J. Henderson,Kenneth Alan Rose,Pierre Petitgas
Publisher : Frontiers Media SA
Page : 268 pages
File Size : 40,8 Mb
Release : 2022-11-23
Category : Science
ISBN : 9782832506929
Author : Iftekhar Khan,Ralph Crott,Zahid Bashir
Publisher : CRC Press
Page : 416 pages
File Size : 54,8 Mb
Release : 2019-06-14
Category : Mathematics
ISBN : 9781498761314
Cancer is a major healthcare burden across the world and impacts not only the people diagnosed with various cancers but also their families, carers, and healthcare systems. With advances in the diagnosis and treatment, more people are diagnosed early and receive treatments for a disease where few treatments options were previously available. As a result, the survival of patients with cancer has steadily improved and, in most cases, patients who are not cured may receive multiple lines of treatment, often with financial consequences for the patients, insurers and healthcare systems. Although many books exist that address economic evaluation, Economic Evaluation of Cancer Drugs using Clinical Trial and Real World Data is the first unified text that specifically addresses the economic evaluation of cancer drugs. The authors discuss how to perform cost-effectiveness analyses while emphasising the strategic importance of designing cost-effectiveness into cancer trials and building robust economic evaluation models that have a higher chance of reimbursement if truly cost-effective. They cover the use of real-world data using cancer registries and discuss how such data can support or complement clinical trials with limited follow up. Lessons learned from failed reimbursement attempts, factors predictive of successful reimbursement and the different payer requirements across major countries including US, Australia, Canada, UK, Germany, France and Italy are also discussed. The book includes many detailed practical examples, case studies and thought-provoking exercises for use in classroom and seminar discussions. Iftekhar Khan is a medical statistician and health economist and a lead statistician at Oxford Unviersity’s Center for Statistics in Medicine. Professor Khan is also a Senior Research Fellow in Health Economics at University of Warwick and is a Senior Statistical Assessor within the Licensing Division of the UK Medicine and Health Regulation Agency. Ralph Crott is a former professor in Pharmacoeconomics at the University of Montreal in Quebec, Canada and former head of the EORTC Health Economics Unit and former senior health economist at the Belgian HTA organization. Zahid Bashir has over twelve years experience working in the pharmaceutical industry in medical affairs and oncology drug development where he is involved in the design and execution of oncology clinical trials and development of reimbursement dossiers for HTA submission.
Author : Stephanie Brodie,Christopher Cvitanovic,Maria Grazia Pennino,Jon Lopez,André Frainer,Kelly Ortega-Cisneros,Natasa Maria Vaidianu,Samiya Ahmed Selim,Sabine Mathesius
Publisher : Frontiers Media SA
Page : 506 pages
File Size : 45,9 Mb
Release : 2022-06-01
Category : Science
ISBN : 9782889763016
The Topic Editors Stephanie Brodie, Christopher Cvitanovic, Maria Grazia Pennino, Jon Lopez and André Frainer declare that they are members of the IMBeR (Integrated Marine Biosphere Research) network and IMECaN (Interdisciplinary Marine Early Career Network) and are collaborating with the IMBeR research community.
Author : Alain F. Zuur,Joseph M. Hilbe,Elena N. Ieno
Publisher : Unknown
Page : 256 pages
File Size : 49,7 Mb
Release : 2013
Category : Ecology
ISBN : 0957174136
This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts.
Author : Paula Moraga
Publisher : CRC Press
Page : 298 pages
File Size : 54,5 Mb
Release : 2023-12-08
Category : Mathematics
ISBN : 9781003832300
Describes R packages for retrieval, manipulation, and visualization of spatial data Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches