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Titlebook: Linear Mixed-Effects Models Using R; A Step-by-Step Appro Andrzej Ga?ecki,Tomasz Burzykowski Textbook 2013 Springer Science+Business Media

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書目名稱Linear Mixed-Effects Models Using R
副標(biāo)題A Step-by-Step Appro
編輯Andrzej Ga?ecki,Tomasz Burzykowski
視頻videohttp://file.papertrans.cn/587/586338/586338.mp4
概述This book provides a description of the most important theoretical concepts and features of linear mixed models (LMMs) and their implementation in R.All the classes of linear models presented in the b
叢書名稱Springer Texts in Statistics
圖書封面Titlebook: Linear Mixed-Effects Models Using R; A Step-by-Step Appro Andrzej Ga?ecki,Tomasz Burzykowski Textbook 2013 Springer Science+Business Media
描述.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, metho
出版日期Textbook 2013
關(guān)鍵詞Correlated data; Linear mixed-effects models; Linear models; Mixed-effects models; R
版次1
doihttps://doi.org/10.1007/978-1-4614-3900-4
isbn_softcover978-1-4899-9667-1
isbn_ebook978-1-4614-3900-4Series ISSN 1431-875X Series E-ISSN 2197-4136
issn_series 1431-875X
copyrightSpringer Science+Business Media New York 2013
The information of publication is updating

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Fitting Linear Models with Homogeneous Variance: The , and , FunctionsIn ., we outlined several concepts related to the classical LM. In the current chapter, we review the tools available in . for fitting the model.
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Fitting Linear Models with Fixed Effects and Correlated Errors: The ,FunctionIn ., we summarized the main concepts underlying the construction of the LM with fixed effects and correlated residual errors for normally distributed, grouped data. An important component of the model is the correlation function, which is used to take into account the correlation between the observations belonging to the same group.
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Fitting Linear Mixed-Effects Models: The ,FunctionIn Chap. 13 we summarized the main theoretical concepts underlying the construction of LMMs. Compared to the LMs introduced in Chaps. 4, 7, and 10, LMMs allow taking the hierarchical structure of data into account in the analysis. This is achieved by introducing, in addition to the mean (fixed-effects) structure, a randomeffects structure.
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https://doi.org/10.1007/978-1-4614-3900-4Correlated data; Linear mixed-effects models; Linear models; Mixed-effects models; R
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