Linear models and linear mixed effects models in R with linguistic applications
arXiv:1308.5499
Abstract
This text is a conceptual introduction to mixed effects modeling with linguistic applications, using the R programming environment. The reader is introduced to linear modeling and assumptions, as well as to mixed effects/multilevel modeling, including a discussion of random intercepts, random slopes and likelihood ratio tests. The example used throughout the text focuses on the phonetic analysis of voice pitch data.
42 pages, 17 figures
Cited by in corpus (6)
- Generalised additive mixed models for dynamic analysis in linguistics: a practical introduction
- Measuring uncertainty during respiratory rate estimation using pressure-sensitive mats
- Preserving Command Line Workflow for a Package Management System using ASCII DAG Visualization
- Is China Entering WTO or shijie maoyi zuzhi--a Corpus Study of English Acronyms in Chinese Newspapers
- DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data
- A study on the Lombard Effect in telepresence robotics