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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 : |
- Part I : introduction
- 1. Introduction
- 2. Case studies
- 3. Data exploration
- Linear models for independent observations
- Part II : linear independant observations
- 4. Linear models with homogeneous variance
- 5. Fitting linear models with homogeneous variance: the lm() and gls() functions
- 6. ARMD trial: linear model with homogeneous variance
- 7. Linear models with heterogeneous variance
- 8. Fitting linear models with heterogeneous variance: the gls() function
- 9. ARMD trial: linear model with heterogeneous variance
- Part III : linear fixed-effects models for correlated data
- 10. Linear model with fixed effects and correlated errors
- 11. Fitting linear models with fixed effects and correlated errors: the gls() function
- 12. ARMD trial: modeling correlated errors for visual acuity
- Part IV : linear mixed-effects models
- 13. Linear Mixed-Effects Model
- 14. Fitting linear mixed-effects models: the lme() function
- 15. Fitting linear mixed-effects models: the imer() function
- 16. ARMD trial: modeling visual acuity
- 17. PRT trial: modeling muscle fiber specific-force
- 18. SII Project: modeling gains in mathematics achievement-scores
- 19. FCAT study: modeling attainment-target scores.
- 20. Extensions of the R tools for linear mixed-effects models.