Linear Mixed Effects Models Using by Andrzej Ecki (10 results)
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Published by Springer, 2015
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Softcover
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Taschenbuch. Condition: Neu. Linear Mixed-Effects Models Using R | A Step-by-Step Approach | Andrzej Ga¿ecki (u. a.) | Taschenbuch | Springer Texts in Statistics | xxxii | Englisch | 2015 | Springer | EAN 9781489996671 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]har…tmann[at]springer[dot]com | Anbieter: preigu.
Language: English
Published by Springer, Humana, 2015
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 measure…ment, 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.
Language: English
Published by Springer, 2013
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Hardcover
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Condition: New. pp. 576.
Language: English
Published by Springer, Humana, 2013
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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, a…nd 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.
Language: English
Published by Springer, Humana Mär 2015, 2015
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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, edu…cational 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. 576 pp. Englisch.
Language: English
Published by Springer, Humana Mär 2015, 2015
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Softcover
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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, educati…onal 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 576 pp. Englisch.
Language: English
Published by Springer, Humana Feb 2013, 2013
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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, educationa…l 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. 576 pp. Englisch.
Language: English
Published by Springer, Humana Feb 2013, 2013
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Hardcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 me…asurement, 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 576 pp. Englisch.
Language: English
Published by Springer, 2013
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Hardcover
- Print on Demand
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Condition: New. Print on Demand pp. 576 67 Illus.
Language: English
Published by Springer, 2013
Series: Springer Texts in Statistics, Book 66 of 111. Book 66 of 111 - Springer Texts in Statistics
- Hardcover
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Condition: New. PRINT ON DEMAND pp. 576.
