Introduction To Mixed Modelling

Author : N. W. Galwey
Publisher : John Wiley & Sons
Page : 379 pages
File Size : 52,8 Mb
Release : 2007-04-04
Category : Mathematics
ISBN : 9780470035962

Get Book

Introduction to Mixed Modelling by N. W. Galwey Pdf

Mixed modelling is one of the most promising and exciting areas ofstatistical analysis, enabling more powerful interpretation of datathrough the recognition of random effects. However, many perceivemixed modelling as an intimidating and specialized technique. Thisbook introduces mixed modelling analysis in a simple andstraightforward way, allowing the reader to apply the techniqueconfidently in a wide range of situations. Introduction to Mixed Modelling shows that mixedmodelling is a natural extension of the more familiar statisticalmethods of regression analysis and analysis of variance. In doingso, it provides the ideal introduction to this importantstatistical technique for those engaged in the statistical analysisof data. This essential book: Demonstrates the power of mixed modelling in a wide range ofdisciplines, including industrial research, social sciences,genetics, clinical research, ecology and agriculturalresearch. Illustrates how the capabilities of regression analysis can becombined with those of ANOVA by the specification of a mixedmodel. Introduces the criterion of Restricted Maximum Likelihood(REML) for the fitting of a mixed model to data. Presents the application of mixed model analysis to a widerange of situations and explains how to obtain and interpret BestLinear Unbiased Predictors (BLUPs). Features a supplementary website containing solutions toexercises, further examples, and links to the computer softwaresystems GenStat and R. This book provides a comprehensive introduction to mixedmodelling, ideal for final year undergraduate students,postgraduate students and professional researchers alike. Readerswill come from a wide range of scientific disciplines includingstatistics, biology, bioinformatics, medicine, agriculture,engineering, economics, and social sciences.

Author : Walter W. Stroup,George A. Milliken,Elizabeth A. Claassen,Russell D. Wolfinger
Publisher : SAS Institute
Page : 608 pages
File Size : 51,8 Mb
Release : 2018-12-12
Category : Computers
ISBN : 9781635261523

Get Book

SAS for Mixed Models by Walter W. Stroup,George A. Milliken,Elizabeth A. Claassen,Russell D. Wolfinger Pdf

Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 52,9 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

Get Book

Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Author : Eugene Demidenko
Publisher : John Wiley & Sons
Page : 768 pages
File Size : 41,9 Mb
Release : 2013-08-05
Category : Mathematics
ISBN : 9781118091579

Get Book

Mixed Models by Eugene Demidenko Pdf

Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.

Author : Daniel Spieler,Eric Schumacher
Publisher : Routledge
Page : 292 pages
File Size : 44,9 Mb
Release : 2019-10-28
Category : Psychology
ISBN : 9781000617467

Get Book

New Methods in Cognitive Psychology by Daniel Spieler,Eric Schumacher Pdf

This book provides an overview of cutting-edge methods currently being used in cognitive psychology, which are likely to appear with increasing frequency in coming years. Once built around univariate parametric statistics, cognitive psychology courses now seem deficient without some contact with methods for signal processing, spatial statistics, and machine learning. There are also important changes in analyses of behavioral data (e.g., hierarchical modeling and Bayesian inference) and there is the obvious change wrought by the advancement of functional imaging. This book begins by discussing the evidence of this rapid change, for example the movement between using traditional analyses of variance to multi-level mixed models, in psycholinguistics. It then goes on to discuss the methods for analyses of physiological measurements, and how these methods provide insights into cognitive processing. New Methods in Cognitive Psychology provides senior undergraduates, graduates and researchers with cutting-edge overviews of new and emerging topics, and the very latest in theory and research for the more established topics.

Author : José C. Pinheiro,Douglas Bates
Publisher : Springer Science & Business Media
Page : 538 pages
File Size : 52,9 Mb
Release : 2009-04-15
Category : Computers
ISBN : 9781441903174

Get Book

Mixed-Effects Models in S and S-PLUS by José C. Pinheiro,Douglas Bates Pdf

R, linear models, random, fixed, data, analysis, fit.

Author : Andrzej Gałecki,Tomasz Burzykowski
Publisher : Springer Science & Business Media
Page : 542 pages
File Size : 52,5 Mb
Release : 2013-02-05
Category : Mathematics
ISBN : 9781461439004

Get Book

Linear Mixed-Effects Models Using R by Andrzej Gałecki,Tomasz Burzykowski Pdf

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.

Author : Brady T. West,Kathleen B. Welch,Andrzej T Galecki
Publisher : CRC Press
Page : 376 pages
File Size : 41,8 Mb
Release : 2006-11-22
Category : Mathematics
ISBN : 9781420010435

Get Book

Linear Mixed Models by Brady T. West,Kathleen B. Welch,Andrzej T Galecki Pdf

Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-nav

Author : Duchateau, L.,Janssen, P.,Rowlands, J.
Publisher : ILRI (aka ILCA and ILRAD)
Page : 172 pages
File Size : 43,9 Mb
Release : 1998-01-01
Category : Electronic
ISBN : 9291460389

Get Book

Linear mixed models. An introduction with applications in veterinary research by Duchateau, L.,Janssen, P.,Rowlands, J. Pdf

Author : Eugene Demidenko
Publisher : John Wiley & Sons
Page : 732 pages
File Size : 40,6 Mb
Release : 2005-01-28
Category : Mathematics
ISBN : 9780471726135

Get Book

Mixed Models by Eugene Demidenko Pdf

A rigorous, self-contained examination of mixed model theory and application Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image. Paying special attention to algorithms and their implementations, the book discusses: Modeling of complex clustered or longitudinal data Modeling data with multiple sources of variation Modeling biological variety and heterogeneity Mixed model as a compromise between the frequentist and Bayesian approaches Mixed model for the penalized log-likelihood Healthy Akaike Information Criterion (HAIC) How to cope with parameter multidimensionality How to solve ill-posed problems including image reconstruction problems Modeling of ensemble shapes and images Statistics of image processing Major results and points of discussion at the end of each chapter along with "Summary Points" sections make this reference not only comprehensive but also highly accessible for professionals and students alike in a broad range of fields such as cancer research, computer science, engineering, and industry.

Author : Helen Brown,Robin Prescott
Publisher : John Wiley & Sons
Page : 476 pages
File Size : 50,5 Mb
Release : 2006-05-01
Category : Mathematics
ISBN : 9780470023570

Get Book

Applied Mixed Models in Medicine by Helen Brown,Robin Prescott Pdf

A mixed model allows the incorporation of both fixed and random variables within a statistical analysis. This enables efficient inferences and more information to be gained from the data. The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This new edition of a groundbreaking text discusses the latest developments, from updated SAS techniques to the increasingly wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on bioequivalence, cluster randomised trials and missing data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features new version of SAS, including the procedure PROC GLIMMIX and an introduction to other available software. Supported by a website featuring computer code, data sets, and further material, available at: http://www.chs.med.ed.ac.uk/phs/mixed/. This much-anticipated second edition is ideal for applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The text will also be of great value to a broad range of scientists, particularly those working the medical and pharmaceutical areas.

Author : John Fox,Sanford Weisberg
Publisher : SAGE Publications
Page : 505 pages
File Size : 55,9 Mb
Release : 2018-09-27
Category : Social Science
ISBN : 9781544336459

Get Book

An R Companion to Applied Regression by John Fox,Sanford Weisberg Pdf

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition has been reorganized and includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text. "An R Companion to Applied Regression continues to provide the most comprehensive and user-friendly guide to estimating, interpreting, and presenting results from regression models in R." –Christopher Hare, University of California, Davis

Author : Walter W. Stroup
Publisher : CRC Press
Page : 547 pages
File Size : 53,8 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781439815137

Get Book

Generalized Linear Mixed Models by Walter W. Stroup Pdf

With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.

Author : Jos W. R. Twisk
Publisher : Cambridge University Press
Page : 249 pages
File Size : 42,8 Mb
Release : 2019-04-18
Category : Mathematics
ISBN : 9781108480574

Get Book

Applied Mixed Model Analysis by Jos W. R. Twisk Pdf

Emphasizing interpretation of results, this hands-on guide explains why, when, and how to use mixed models with your data.

Author : Alain Zuur,Elena N. Ieno,Neil Walker,Anatoly A. Saveliev,Graham M. Smith
Publisher : Springer Science & Business Media
Page : 579 pages
File Size : 53,6 Mb
Release : 2009-03-05
Category : Science
ISBN : 9780387874586

Get Book

Mixed Effects Models and Extensions in Ecology with R by Alain Zuur,Elena N. Ieno,Neil Walker,Anatoly A. Saveliev,Graham M. Smith Pdf

This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.