Generalized Linear Models With Examples In R
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Generalized Linear Models With Examples In R
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Author : Peter K. Dunn
language : en
Publisher: Springer
Release Date : 2018-11-10
Generalized Linear Models With Examples In R written by Peter K. Dunn and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-10 with Mathematics categories.
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
An Introduction To Generalized Linear Models
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Author : Annette J. Dobson
language : en
Publisher: CRC Press
Release Date : 2018-04-17
An Introduction To Generalized Linear Models written by Annette J. Dobson and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-17 with Mathematics categories.
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.
Data Analysis Using Hierarchical Generalized Linear Models With R
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Author : Youngjo Lee
language : en
Publisher: CRC Press
Release Date : 2017-07-06
Data Analysis Using Hierarchical Generalized Linear Models With R written by Youngjo Lee and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-06 with Mathematics categories.
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.
Generalized Linear Models
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Author : Jean-Francois Dupuy
language : en
Publisher: John Wiley & Sons
Release Date : 2025-06-17
Generalized Linear Models written by Jean-Francois Dupuy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
Since they were first formulated in 1972, generalized linear models have enjoyed a veritable boom, with numerous applications in insurance, economics and biostatistics. Today, they are still the subject of a great deal of research. This book provides an overview of the theory of generalized linear models. Particular attention is paid to the problems of censoring, missing data and excess zeros. Didactic and accessible, Generalized Linear Models is illustrated with exercises and numerous R codes. With all the necessary prerequisites introduced in a step-by-step fashion, this book is aimed at students (at master's or engineering school level), as well as teachers and practitioners of mathematics and statistical modeling.
Multivariate Generalized Linear Mixed Models Using R
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Author : Damon Mark Berridge
language : en
Publisher: CRC Press
Release Date : 2011-04-25
Multivariate Generalized Linear Mixed Models Using R written by Damon Mark Berridge and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-04-25 with Mathematics categories.
Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. Improve Your Longitudinal Study In medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.
Foundations Of Linear And Generalized Linear Models
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Author : Alan Agresti
language : en
Publisher: John Wiley & Sons
Release Date : 2015-01-15
Foundations Of Linear And Generalized Linear Models written by Alan Agresti and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-15 with Mathematics categories.
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.
Regression Analysis By Example Using R
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Author : Ali S. Hadi
language : en
Publisher: John Wiley & Sons
Release Date : 2023-10-31
Regression Analysis By Example Using R written by Ali S. Hadi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-31 with Mathematics categories.
Regression Analysis By Example Using R A STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS In the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R. Readers will also find: Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis Updated data sets and examples throughout the book Complimentary access to a companion website that provides data sets in xlsx, csv, and txt format Perfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.
Extending The Linear Model With R
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Author : Julian J. Faraway
language : en
Publisher: CRC Press
Release Date : 2016-02-10
Extending The Linear Model With R written by Julian J. Faraway and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-10 with Mathematics categories.
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.
Generalized Linear Models For Bounded And Limited Quantitative Variables
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Author : Michael Smithson
language : en
Publisher: SAGE Publications
Release Date : 2019-09-09
Generalized Linear Models For Bounded And Limited Quantitative Variables written by Michael Smithson and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-09 with Social Science categories.
This book introduces researchers and students to the concepts and generalized linear models for analyzing quantitative random variables that have one or more bounds. Examples of bounded variables include the percentage of a population eligible to vote (bounded from 0 to 100), or reaction time in milliseconds (bounded below by 0). The human sciences deal in many variables that are bounded. Ignoring bounds can result in misestimation and improper statistical inference. Michael Smithson and Yiyun Shou's book brings together material on the analysis of limited and bounded variables that is scattered across the literature in several disciplines, and presents it in a style that is both more accessible and up-to-date. The authors provide worked examples in each chapter using real datasets from a variety of disciplines. The software used for the examples include R, SAS, and Stata. The data, software code, and detailed explanations of the example models are available on an accompanying website.
Nonparametric Statistical Methods Using R
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Author : Graysen Cline
language : en
Publisher: Scientific e-Resources
Release Date : 2019-05-19
Nonparametric Statistical Methods Using R written by Graysen Cline and has been published by Scientific e-Resources this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-19 with categories.
Nonparametric Statistical Methods Using R covers customary nonparametric methods and rank-based examinations, including estimation and deduction for models running from straightforward area models to general direct and nonlinear models for uncorrelated and corresponded reactions. The creators underscore applications and measurable calculation. They represent the methods with numerous genuine and mimicked information cases utilizing R, including the bundles Rfit and npsm. The book initially gives a diagram of the R dialect and essential factual ideas previously examining nonparametrics. It presents rank-based methods for one-and two-example issues, strategies for relapse models, calculation for general settled impacts ANOVA and ANCOVA models, and time-to-occasion examinations. The last two parts cover further developed material, including high breakdown fits for general relapse models and rank-based surmising for bunch associated information. The book can be utilized as an essential content or supplement in a course on connected nonparametric or hearty strategies and as a source of perspective for scientists who need to execute nonparametric and rank-based methods by and by. Through various illustrations, it demonstrates to perusers proper methodologies to apply these methods utilizing R.