Data Analysis Using Hierarchical Generalized Linear Models With R

Author : Youngjo Lee,Lars Ronnegard,Maengseok Noh
Publisher : CRC Press
Page : 250 pages
File Size : 41,9 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.

Author : Youngjo Lee,Lars Rönnegard,Maengseok Noh
Publisher : Unknown
Page : 0 pages
File Size : 43,8 Mb
Release : 2020
Category : Electronic
ISBN : OCLC:1420875212

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

Author : Peter K. Dunn,Gordon K. Smyth
Publisher : Springer
Page : 562 pages
File Size : 54,8 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

Author : Youngjo Lee,Lars Ronnegard,Maengseok Noh
Publisher : CRC Press
Page : 322 pages
File Size : 47,5 Mb
Release : 2017-07-06
Category : Mathematics
ISBN : 9781351811569

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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.

Author : Julian J. Faraway
Publisher : CRC Press
Page : 307 pages
File Size : 54,8 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.

Author : Stephen W. Raudenbush,Anthony S. Bryk
Publisher : SAGE
Page : 520 pages
File Size : 45,9 Mb
Release : 2002
Category : Social Science
ISBN : 076191904X

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Hierarchical Linear Models by Stephen W. Raudenbush,Anthony S. Bryk Pdf

New edition of a text in which Raudenbush (U. of Michigan) and Bryk (sociology, U. of Chicago) provide examples, explanations, and illustrations of the theory and use of hierarchical linear models (HLM). New material in Part I (Logic) includes information on multivariate growth models and other topics.

Author : César Pérez López
Publisher : Independently Published
Page : 222 pages
File Size : 41,8 Mb
Release : 2020-11-08
Category : Electronic
ISBN : 9798560999926

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Statistics and Data Analysis Through R by César Pérez López Pdf

This book focuses on the implementation of statistics and data analysis through R. It deals first with the Exploratory Data Analysis both numerically and graphically, which is always a technique prior to any other statistical analysis. Descriptive statistics and the calculation of probabilities are then developed. Subsequently, the multiple regression model is approached, focusing on the problems of its estimation and diagnosis. It also delves into the generalized linear models and the analysis of variance and covariance models. Dimension reduction techniques are also addressed with special emphasis on principal component analysis and factor analysis. Finally, the segmentation techniques related to hierarchical and non-hierarchical cluster analysis are presented.

Author : Paul Roback,Julie Legler
Publisher : CRC Press
Page : 436 pages
File Size : 51,9 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)

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

Author : Julian J. Faraway
Publisher : CRC Press
Page : 399 pages
File Size : 44,8 Mb
Release : 2016-03-23
Category : Mathematics
ISBN : 9781498720984

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

Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics. New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and Practice This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.

Author : Thomas W. Yee
Publisher : Springer
Page : 589 pages
File Size : 49,9 Mb
Release : 2015-09-11
Category : Mathematics
ISBN : 9781493928187

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Vector Generalized Linear and Additive Models by Thomas W. Yee Pdf

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be performed as a coherent whole. Since their advent in 1972, GLMs have unified important distributions under a single umbrella with enormous implications. However, GLMs are not flexible enough to cope with the demands of practical data analysis. And data-driven GLMs, in the form of generalized additive models (GAMs), are also largely confined to the exponential family. The methodology here and accompanying software (the extensive VGAM R package) are directed at these limitations and are described comprehensively for the first time in one volume. This book treats distributions and classical models as generalized regression models, and the result is a much broader application base for GLMs and GAMs. The book can be used in senior undergraduate or first-year postgraduate courses on GLMs or categorical data analysis and as a methodology resource for VGAM users. In the second part of the book, the R package VGAM allows readers to grasp immediately applications of the methodology. R code is integrated in the text, and datasets are used throughout. Potential applications include ecology, finance, biostatistics, and social sciences. The methodological contribution of this book stands alone and does not require use of the VGAM package.

Author : Youngjo Lee,John A. Nelder,Yudi Pawitan
Publisher : CRC Press
Page : 446 pages
File Size : 45,8 Mb
Release : 2018-07-11
Category : Mathematics
ISBN : 9781498720625

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Generalized Linear Models with Random Effects by Youngjo Lee,John A. Nelder,Yudi Pawitan Pdf

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and Nelder. It has been thoroughly updated, with around 80 pages added, including new material on the extended likelihood approach that strengthens the theoretical basis of the methodology, new developments in variable selection and multiple testing, and new examples and applications. It includes an R package for all the methods and examples that supplement the book.

Author : Andrew Gelman,Jennifer Hill
Publisher : Cambridge University Press
Page : 654 pages
File Size : 55,5 Mb
Release : 2007
Category : Mathematics
ISBN : 052168689X

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Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman,Jennifer Hill Pdf

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Author : Debasis Sengupta,S Rao Jammalamadaka
Publisher : World Scientific
Page : 773 pages
File Size : 41,6 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:

Author : John Fox
Publisher : SAGE Publications
Page : 612 pages
File Size : 40,7 Mb
Release : 2015-03-18
Category : Social Science
ISBN : 9781483321318

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Applied Regression Analysis and Generalized Linear Models by John Fox Pdf

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.