Linear Mixed-Effects Models Using R : A Step-by-Step Approach
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 wid...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Ausgabe: | 1st ed. 2013. |
| Schriftenreihe: | Springer Texts in Statistics
|
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Anmerkung: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Linear mixed-effects models using R, a step-by-step approach, Andrzej Gałecki, Tomasz Burzykowski, 2013, New York [etc.], Springer, 1 vol. (XXXII-542 p.), Springer texts in statistics, 978-1-461-43899-1 • Linear Mixed-Effects Models Using R, Texte imprimé, 9781489996671 • Linear mixed-effects models using R, a step-by-step approach, Andrzej Gałecki, Tomasz Burzykowski, 2013, New York [etc.], Springer, 1 vol. (XXXII-542 p.), Springer texts in statistics, 978-1-461-43899-1 • Linear Mixed-Effects Models Using R, Texte imprimé, 9781461439011 |
Inhaltsangabe:
- Introduction Linear Models for Independent Observations Linear Fixed-effects Models for Correlated Data Linear Mixed-effects Models