Linear Mixed-Effects Models Using R - gebunden oder broschiert

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ISBN/EAN: 9781461438991

ISBN - alternative Schreibweisen:
1-4614-3899-3, 978-1-4614-3899-1
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: tomasz, andrzej just, tomas gale
Titel des Buches: mix, mixe, mixed mode, linear models with, statistics, linear mixed effects models using step step approach, springer texts

Daten vom Verlag:

Autor/in: Andrzej Gałecki; Tomasz Burzykowski
Titel: Springer Texts in Statistics; Linear Mixed-Effects Models Using R - A Step-by-Step Approach
Verlag: Springer; Springer US
542 Seiten
Erscheinungsjahr: 2013-02-05
New York; NY; US
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
139,09 € (DE)
142,99 € (AT)
153,50 CHF (CH)
POD
XXXII, 542 p. 64 illus.

BB; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Correlated data; Linear mixed-effects models; Linear models; Mixed-effects models; R; Statistical Theory and Methods; Statistics; Statistics and Computing; Mathematische und statistische Software; EA; BC

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 linearmodels 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.

Introduction.- Linear Models for Independent Observations.- Linear Fixed-effects Models for Correlated Data.- Linear Mixed-effects Models.

From the reviews:

Techonometrics, 56:1 2014

“This textbook is built as a step by step incremental description of a modelling tool used extensively in the analysis of hierarchical structured data sets. It is a balanced collection of concepts and examples from various research areas … . In addition to a great collection of theory and examples, a state of the art description of LMMs in R, the authors developed the R package nlmeU which contains the data sets and presented R code, making this book a milestone in its field.” (Irina Ioana Mohorianu, zbMATH, Vol. 1275, 2014)

. Dr. Burzykowski published methodological work on survival analysis, meta-analyses of clinical trials, validation of surrogate endpoints, analysis of gene expression data, and modelling of peptide-centric mass-spectrometry data. He is also a co-author of numerous papers applying statistical methods to clinical data in different disease areas.

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 linearmodels 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.

. Dr. Burzykowski published methodological work on survival analysis, meta-analyses of clinical trials, validation of surrogate endpoints, analysis of gene expression data, and modelling of peptide-centric mass-spectrometry data. He is also a co-author of numerous papers applying statistical methods to clinical data in different disease areas.