| 书目名称 | Linear Mixed-Effects Models Using R | | 副标题 | A Step-by-Step Appro | | 编辑 | Andrzej Gałecki,Tomasz Burzykowski | | 视频video | | | 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 | | 丛书名称 | Springer Texts in Statistics | | 图书封面 |  | | 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 | | 关键词 | Correlated data; Linear mixed-effects models; Linear models; Mixed-effects models; R | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4614-3900-4 | | isbn_softcover | 978-1-4899-9667-1 | | isbn_ebook | 978-1-4614-3900-4Series ISSN 1431-875X Series E-ISSN 2197-4136 | | issn_series | 1431-875X | | copyright | Springer Science+Business Media New York 2013 |
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发表于 2025-3-21 17:01:13
