Linear mixed-effects models using R : a step-by-step approach /
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| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York, NY :
Springer,
c2013
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| Series: | Springer texts in statistics.
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| Subjects: |
Table of Contents:
- Introduction
- Introduction
- Case Studies
- Data Exploration
- Linear Models for Independent Observations
- Linear Models with Homogeneous Variance
- Fitting Linear Models with Homogeneous Variance: The lm() and gls() Functions
- ARMD Trial: Linear Model with Homogeneous Variance
- Linear Models with Heterogeneous Variance
- Fitting Linear Models with Heterogeneous Variance: The gls() Function
- ARMD Trial: Linear Model with Heterogeneous Variance
- Linear Fixed-effects Models for Correlated Data
- Linear Model with Fixed Effects and Correlated Errors
- Fitting Linear Models with Fixed Effects and Correlated Errors: The gls() Function
- ARMD Trial: Modeling Correlated Errors for Visual Acuity
- Linear Mixed-effects Models
- Linear Mixed-Effects Model
- Fitting Linear Mixed-Effects Models: The lme()Function
- Fitting Linear Mixed-Effects Models: The lmer() Function
- ARMD Trial: Modeling Visual Acuity
- PRT Trial: Modeling Muscle Fiber Specific-Force
- SII Project: Modeling Gains in Mathematics Achievement-Scores
- FCAT Study: Modeling Attainment-Target Scores
- Extensions of the RTools for Linear Mixed-Effects Models.