Linear Mixed Effects Models Using R
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Linear Mixed Effects Models Using R
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Author : Andrzej Gałecki
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-05
Linear Mixed Effects Models Using R written by Andrzej Gałecki and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-05 with Mathematics categories.
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.
Multilevel Modeling Using R
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Author : W. Holmes Finch
language : en
Publisher: CRC Press
Release Date : 2019-07-16
Multilevel Modeling Using R written by W. Holmes Finch and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-16 with Mathematics categories.
Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT, Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.
Mixed Effects Models And Extensions In Ecology With R
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Author : Alain Zuur
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-03-05
Mixed Effects Models And Extensions In Ecology With R written by Alain Zuur and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-03-05 with Science categories.
Building on the successful Analysing Ecological Data (2007) by Zuur, Ieno and Smith, the authors now provide an expanded introduction to using regression and its extensions in analysing ecological data. As with the earlier book, real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. The second part provides ten case studies that range from koalas to deep sea research. These chapters provide an invaluable insight into analysing complex ecological datasets, including comparisons of different approaches to the same problem. By matching ecological questions and data structure to a case study, these chapters provide an excellent starting point to analysing your own data. Data and R code from all chapters are available from www.highstat.com.
Research Methods Using R
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Author : Daniel H. Baker
language : en
Publisher: Oxford University Press
Release Date : 2022
Research Methods Using R written by Daniel H. Baker and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Psychology categories.
Providing complete coverage of advanced research methods for undergraduates, Daniel H. Baker supports students in their mastery of more advanced research methods and their application in R. This brand new title brings together coverage of a variety of topics for readers with basic statistical knowledge. It begins with material on the fundamental tools - nonlinear curve fitting and function optimization, stochastic methods, and Fourier (frequency) analysis - before leading readers on to more specialist content - bivariate and multivariate statistics, Bayesian statistics, and machine learning methods. Several chapters also discuss methods that can be used to improve research practises, including power analysis, meta-analysis, reproducible data analysis. Written to build a student's confidence with using R in a step-by-step way, early chapters present the essentials, ensuring that the content is accessible to those that have never programmed before. By giving them a feel for how the software works in practice, students are gradually introduced to simple examples of techniques before building up to more detailed implementations demonstrated in worked examples. Readers are also presented with opportunities to try analysis techniques for themselves. Practice questions are presented at the end of each chapter with answer guidance supplied in the book, while multiple-choice-questions with instant feedback can be accessed online. The author also provides datasets online which students can use to practise their new skills. Digital formats and resources This book is available for students and institutions to purchase in a variety of formats, and is supported by online resources. - The e-book offers a mobile experience and convenient access along with functionality, navigation features, and links that offer extra learning support. This book is accompanied by online resources including multiple-choice-questions with instant feedback, example code, and data files allowing students to run examples independently.
Mixed Effects Regression Models In Linguistics
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Author : Dirk Speelman
language : en
Publisher: Springer
Release Date : 2018-02-07
Mixed Effects Regression Models In Linguistics written by Dirk Speelman and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-07 with Social Science categories.
When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.
Mixed Effects Models For The Population Approach
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Author : Marc Lavielle
language : en
Publisher: CRC Press
Release Date : 2014-07-14
Mixed Effects Models For The Population Approach written by Marc Lavielle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-14 with Mathematics categories.
Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data Modeling The book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical Implementation This book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.
Multilevel Modeling
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Author : Douglas A. Luke
language : en
Publisher: SAGE Publications
Release Date : 2019-12-13
Multilevel Modeling written by Douglas A. Luke and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-13 with Social Science categories.
Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.
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.
New Methods In Cognitive Psychology
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Author : Daniel Spieler
language : en
Publisher: Routledge
Release Date : 2019-10-28
New Methods In Cognitive Psychology written by Daniel Spieler and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-28 with Psychology categories.
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.
Symmetry In Vision
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Author : Marco Bertamini
language : en
Publisher: MDPI
Release Date : 2018-07-09
Symmetry In Vision written by Marco Bertamini and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-09 with Computers categories.
This book is a printed edition of the Special Issue "Symmetry in Vision" that was published in Symmetry