Generalized Linear Models With Examples In R

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Generalized Linear Models With Examples in R

Peter K. Dunn,Gordon K. Smyth
Generalized Linear Models With Examples in R Book PDF

Author : Peter K. Dunn,Gordon K. Smyth
Publisher : Springer
Page : 562 pages
File Size : 53,8 Mb
Release : 2018-11-10
Category : Mathematics
ISBN : 9781441901187

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Generalized Linear Models With Examples in R by Peter K. Dunn,Gordon K. Smyth Pdf

This textbook presents an introduction to generalized linear models, complete with real-world data sets and practice problems, making it applicable for both beginning and advanced students of applied statistics. Generalized linear models (GLMs) are powerful tools in applied statistics that extend the ideas of multiple linear regression and analysis of variance to include response variables that are not normally distributed. As such, GLMs can model a wide variety of data types including counts, proportions, and binary outcomes or positive quantities. The book is designed with the student in mind, making it suitable for self-study or a structured course. Beginning with an introduction to linear regression, the book also devotes time to advanced topics not typically included in introductory textbooks. It features chapter introductions and summaries, clear examples, and many practice problems, all carefully designed to balance theory and practice. The text also provides a working knowledge of applied statistical practice through the extensive use of R, which is integrated into the text. Other features include: • Advanced topics such as power variance functions, saddlepoint approximations, likelihood score tests, modified profile likelihood, small-dispersion asymptotics, and randomized quantile residuals • Nearly 100 data sets in the companion R package GLMsData • Examples that are cross-referenced to the companion data set, allowing readers to load the data and follow the analysis in their own R session

Generalized Linear Models

Peter Dunn,Gordon Smyth
Generalized Linear Models Book PDF

Author : Peter Dunn,Gordon Smyth
Publisher : Springer
Page : 10 pages
File Size : 42,9 Mb
Release : 2014-03-14
Category : Mathematics
ISBN : 1441901175

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Generalized Linear Models by Peter Dunn,Gordon Smyth Pdf

This book is designed with teaching and learning in mind. It uses introductions, chapter summaries, exercises, short answers, simple, clear examples, examples of R code, and the minimum necessary theory.

Linear Models with R

Julian J. Faraway
Linear Models with R Book PDF

Author : Julian J. Faraway
Publisher : CRC Press
Page : 284 pages
File Size : 50,9 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781439887349

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Linear Models with R by Julian J. Faraway Pdf

A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models

Applying Generalized Linear Models

James K. Lindsey
Applying Generalized Linear Models Book PDF

Author : James K. Lindsey
Publisher : Springer Science & Business Media
Page : 256 pages
File Size : 40,6 Mb
Release : 2008-01-15
Category : Mathematics
ISBN : 9780387227306

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Applying Generalized Linear Models by James K. Lindsey Pdf

This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

Generalized Linear Models

P. McCullagh
Generalized Linear Models Book PDF

Author : P. McCullagh
Publisher : Routledge
Page : 361 pages
File Size : 52,8 Mb
Release : 2019-01-22
Category : Mathematics
ISBN : 9781351445849

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Generalized Linear Models by P. McCullagh Pdf

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and ot

Data Analysis Using Hierarchical Generalized Linear Models with R

Youngjo Lee,Lars Ronnegard,Maengseok Noh
Data Analysis Using Hierarchical Generalized Linear Models with R Book PDF

Author : Youngjo Lee,Lars Ronnegard,Maengseok Noh
Publisher : CRC Press
Page : 250 pages
File Size : 40,7 Mb
Release : 2017-07-06
Category : Mathematics
ISBN : 9781351811552

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Data Analysis Using Hierarchical Generalized Linear Models with R by Youngjo Lee,Lars Ronnegard,Maengseok Noh Pdf

Since their introduction, hierarchical generalized linear models (HGLMs) have proven useful in various fields by allowing random effects in regression models. Interest in the topic has grown, and various practical analytical tools have been developed. This book summarizes developments within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to advanced statistical modelling including generalized linear models with random effects, survival analysis and frailty models, multivariate HGLMs, factor and structural equation models, robust modelling of random effects, models including penalty and variable selection and hypothesis testing. This example-driven book is aimed primarily at researchers and graduate students, who wish to perform data modelling beyond the frequentist framework, and especially for those searching for a bridge between Bayesian and frequentist statistics.

Introduction to General and Generalized Linear Models

Henrik Madsen,Poul Thyregod
Introduction to General and Generalized Linear Models Book PDF

Author : Henrik Madsen,Poul Thyregod
Publisher : CRC Press
Page : 307 pages
File Size : 52,7 Mb
Release : 2010-11-09
Category : Mathematics
ISBN : 9781439891148

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Introduction to General and Generalized Linear Models by Henrik Madsen,Poul Thyregod Pdf

Bridging the gap between theory and practice for modern statistical model building, Introduction to General and Generalized Linear Models presents likelihood-based techniques for statistical modelling using various types of data. Implementations using R are provided throughout the text, although other software packages are also discussed. Numerous

Extending the Linear Model with R

Julian J. Faraway
Extending the Linear Model with R Book PDF

Author : Julian J. Faraway
Publisher : CRC Press
Page : 307 pages
File Size : 51,9 Mb
Release : 2016-02-10
Category : Mathematics
ISBN : 9780203492284

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Extending the Linear Model with R by Julian J. Faraway Pdf

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the data described in the book is available at http://people.bath.ac.uk/jjf23/ELM/ Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught.

An Introduction to Generalized Linear Models

Annette J. Dobson,Adrian G. Barnett
An Introduction to Generalized Linear Models Book PDF

Author : Annette J. Dobson,Adrian G. Barnett
Publisher : CRC Press
Page : 376 pages
File Size : 52,7 Mb
Release : 2018-04-17
Category : Mathematics
ISBN : 9781351726221

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An Introduction to Generalized Linear Models by Annette J. Dobson,Adrian G. Barnett Pdf

An Introduction to Generalized Linear Models, Fourth Edition provides a cohesive framework for statistical modelling, with an emphasis on numerical and graphical methods. This new edition of a bestseller has been updated with new sections on non-linear associations, strategies for model selection, and a Postface on good statistical practice. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers Normal, Poisson, and Binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, non-linear associations and longitudinal analysis Connects Bayesian analysis and MCMC methods to fit GLMs Contains numerous examples from business, medicine, engineering, and the social sciences Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods Offers the data sets and solutions to the exercises online Describes the components of good statistical practice to improve scientific validity and reproducibility of results. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons.

Beyond Multiple Linear Regression

Paul Roback,Julie Legler
Beyond Multiple Linear Regression Book PDF

Author : Paul Roback,Julie Legler
Publisher : CRC Press
Page : 436 pages
File Size : 51,6 Mb
Release : 2021-01-14
Category : Mathematics
ISBN : 9781439885406

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Beyond Multiple Linear Regression by Paul Roback,Julie Legler Pdf

Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)

Linear Models And Regression With R: An Integrated Approach

Debasis Sengupta,S Rao Jammalamadaka
Linear Models And Regression With R An Integrated Approach Book PDF

Author : Debasis Sengupta,S Rao Jammalamadaka
Publisher : World Scientific
Page : 773 pages
File Size : 52,8 Mb
Release : 2019-07-30
Category : Mathematics
ISBN : 9789811200427

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Linear Models And Regression With R: An Integrated Approach by Debasis Sengupta,S Rao Jammalamadaka Pdf

Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed.The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references.This completely updated and new edition of Linear Models: An Integrated Approach includes the following features:

Generalized Linear Models

Raymond H. Myers,Douglas C. Montgomery,G. Geoffrey Vining,Timothy J. Robinson
Generalized Linear Models Book PDF

Author : Raymond H. Myers,Douglas C. Montgomery,G. Geoffrey Vining,Timothy J. Robinson
Publisher : John Wiley & Sons
Page : 521 pages
File Size : 51,9 Mb
Release : 2012-01-20
Category : Mathematics
ISBN : 9780470556979

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Generalized Linear Models by Raymond H. Myers,Douglas C. Montgomery,G. Geoffrey Vining,Timothy J. Robinson Pdf

Praise for the First Edition "The obvious enthusiasm of Myers, Montgomery, and Vining and their reliance on their many examples as a major focus of their pedagogy make Generalized Linear Models a joy to read. Every statistician working in any area of applied science should buy it and experience the excitement of these new approaches to familiar activities." —Technometrics Generalized Linear Models: With Applications in Engineering and the Sciences, Second Edition continues to provide a clear introduction to the theoretical foundations and key applications of generalized linear models (GLMs). Maintaining the same nontechnical approach as its predecessor, this update has been thoroughly extended to include the latest developments, relevant computational approaches, and modern examples from the fields of engineering and physical sciences. This new edition maintains its accessible approach to the topic by reviewing the various types of problems that support the use of GLMs and providing an overview of the basic, related concepts such as multiple linear regression, nonlinear regression, least squares, and the maximum likelihood estimation procedure. Incorporating the latest developments, new features of this Second Edition include: A new chapter on random effects and designs for GLMs A thoroughly revised chapter on logistic and Poisson regression, now with additional results on goodness of fit testing, nominal and ordinal responses, and overdispersion A new emphasis on GLM design, with added sections on designs for regression models and optimal designs for nonlinear regression models Expanded discussion of weighted least squares, including examples that illustrate how to estimate the weights Illustrations of R code to perform GLM analysis The authors demonstrate the diverse applications of GLMs through numerous examples, from classical applications in the fields of biology and biopharmaceuticals to more modern examples related to engineering and quality assurance. The Second Edition has been designed to demonstrate the growing computational nature of GLMs, as SAS®, Minitab®, JMP®, and R software packages are used throughout the book to demonstrate fitting and analysis of generalized linear models, perform inference, and conduct diagnostic checking. Numerous figures and screen shots illustrating computer output are provided, and a related FTP site houses supplementary material, including computer commands and additional data sets. Generalized Linear Models, Second Edition is an excellent book for courses on regression analysis and regression modeling at the upper-undergraduate and graduate level. It also serves as a valuable reference for engineers, scientists, and statisticians who must understand and apply GLMs in their work.

R for Data Science

Hadley Wickham,Garrett Grolemund
R for Data Science Book PDF

Author : Hadley Wickham,Garrett Grolemund
Publisher : "O'Reilly Media, Inc."
Page : 521 pages
File Size : 48,9 Mb
Release : 2016-12-12
Category : Computers
ISBN : 9781491910368

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R for Data Science by Hadley Wickham,Garrett Grolemund Pdf

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Design of Experiments for Generalized Linear Models

Kenneth G. Russell
Design of Experiments for Generalized Linear Models Book PDF

Author : Kenneth G. Russell
Publisher : CRC Press
Page : 208 pages
File Size : 48,6 Mb
Release : 2018-12-14
Category : Mathematics
ISBN : 9780429614415

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Design of Experiments for Generalized Linear Models by Kenneth G. Russell Pdf

Generalized Linear Models (GLMs) allow many statistical analyses to be extended to important statistical distributions other than the Normal distribution. While numerous books exist on how to analyse data using a GLM, little information is available on how to collect the data that are to be analysed in this way. This is the first book focusing specifically on the design of experiments for GLMs. Much of the research literature on this topic is at a high mathematical level, and without any information on computation. This book explains the motivation behind various techniques, reduces the difficulty of the mathematics, or moves it to one side if it cannot be avoided, and gives examples of how to write and run computer programs using R. Features The generalisation of the linear model to GLMs Background mathematics, and the use of constrained optimisation in R Coverage of the theory behind the optimality of a design Individual chapters on designs for data that have Binomial or Poisson distributions Bayesian experimental design An online resource contains R programs used in the book This book is aimed at readers who have done elementary differentiation and understand minimal matrix algebra, and have familiarity with R. It equips professional statisticians to read the research literature. Nonstatisticians will be able to design their own experiments by following the examples and using the programs provided.

Foundations of Linear and Generalized Linear Models

Alan Agresti
Foundations of Linear and Generalized Linear Models Book PDF

Author : Alan Agresti
Publisher : John Wiley & Sons
Page : 471 pages
File Size : 55,6 Mb
Release : 2015-02-23
Category : Mathematics
ISBN : 9781118730034

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Foundations of Linear and Generalized Linear Models by Alan Agresti Pdf

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.