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Linear mixed-effects models using R : a step-by-step approach
La 4e de couv. indique : "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....
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| Auteurs principaux : | , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Linear mixed-effects models using R : a step-by-step approach / Andrzej Gałecki, Tomasz Burzykowski |
| Publié : |
New York, Heidelberg :
Springer
, cop. 2013 |
| Description matérielle : | 1 vol. (XXXII-542 p. ) |
| Collection : | Springer texts in statistics |
| Sujets : |
| LEADER | 04357cam a2200481 4500 | ||
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| 200 | 1 | |a Linear mixed-effects models using R |e a step-by-step approach |f Andrzej Gałecki, Tomasz Burzykowski | |
| 210 | |a New York |a Heidelberg |c Springer |d cop. 2013 | ||
| 215 | |a 1 vol. (XXXII-542 p. ) |c illustrations |d 25 cm | ||
| 225 | 2 | |a Springer texts in statistics |x 1431-875X | |
| 320 | |a Notes bibliogr. Bibliogr. p 527-529. Index | ||
| 330 | |a La 4e de couv. indique : "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 linear models 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." | ||
| 359 | 2 | |b Part I : introduction |c 1. Introduction |c 2. Case studies |c 3. Data exploration |b Linear models for independent observations |b Part II : linear independant observations |c 4. Linear models with homogeneous variance |c 5. Fitting linear models with homogeneous variance: the lm() and gls() functions |c 6. ARMD trial: linear model with homogeneous variance |c 7. Linear models with heterogeneous variance |c 8. Fitting linear models with heterogeneous variance: the gls() function |c 9. ARMD trial: linear model with heterogeneous variance |b Part III : linear fixed-effects models for correlated data |c 10. Linear model with fixed effects and correlated errors |c 11. Fitting linear models with fixed effects and correlated errors: the gls() function |c 12. ARMD trial: modeling correlated errors for visual acuity |b Part IV : linear mixed-effects models |c 13. Linear Mixed-Effects Model |c 14. Fitting linear mixed-effects models: the lme() function |c 15. Fitting linear mixed-effects models: the imer() function |c 16. ARMD trial: modeling visual acuity |c 17. PRT trial: modeling muscle fiber specific-force |c 18. SII Project: modeling gains in mathematics achievement-scores |c 19. FCAT study: modeling attainment-target scores. |c 20. Extensions of the R tools for linear mixed-effects models. | |
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