Linear Mixed-Effects Models Using R

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a comprehensive and accessible text that provides a unified approac

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

This book presents wide-ranging coverage of parametric modeling in linear and nonlinear mixed-effects models. Using these models, readers can perform parameter estimation across a whole population of

This second edition updates the case studies using the latest versions of the software procedures, covers additional topics in the application of linear mixed models, and provides up-to-date informati

Linear models are widely used in applications in physical science, engineering, social science and business. The world of R has expanded in the ten years that have passed since this text’s first editi

"This book covers a wide range of statistical models, including hierarchical, hierarchical generalized linear, linear mixed, dynamic linear, smoothing, spatial, and longitudinal. It presents a framewo

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

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, p

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual fram

"Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: Those who are familiar with the theory and applications of linear statistical models and wish

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

This set features:Variance Components by Shayle R. Searle, George Casella, Charles E. McCulloch (978-0-470-00959-8)Matrix Algebra for Useful Statistics by Shayle R. Searle (978-0-470-00961-1)Linear Mo

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 o

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 pr

A precise and accessible presentation of linear model theory, illustrated with data examplesStatisticians often use linear models for data analysis and for developing new statistical methods. Most boo

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 reg

This textbook focuses on the practice of regression and analysis of variance. Readers will learn which methods are available and the various situations in which they can be applied. Numerous examples

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all a

An overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data. The authors p

An advanced discussion of linear models with mixed or random effects.In recent years a breakthrough has occurred in our ability to draw inferences from exact and optimum tests of variance component mo

This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman

The goal of this text for students and professionals is to help readers who have worked with linear mixed models make the transition to generalized linear mixed models. Benefits and challenges of gene

Mixed effects models, including linear mixed models, generalized linear mixed models, nonlinear mixed effects models, and non-parametric mixed effects models, are complex models by nature; yet, these

This book provides the application of generalized linear mixed-effects models and its related models in the statistical design and analysis of repeated measures adopted in randomized controlled trials

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 a

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

This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement

"This useful guide helps pharmaceutical scientists and students learn the requisite information needed to perform mixed effect modeling of pharmacologic data using the NONMEM software package"--Provid