Linear mixed-effects models using R
By A. Galecki, T. Burzykowski. 2013
499 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0ab2c618af03dc533aa677c679c5d2e293fb9f7f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Harald Baayen
- Reinhold Kliegl
- Shravan Vasishth
- Arnd Florack
- Douglas M. Bates
- S. Haasova
- Sonja Kunz
- Roger Levy
In Statistics and Probability
- Harald Baayen
- Reinhold Kliegl
- Shravan Vasishth
- Douglas M. Bates
- Arnd Florack
- S. Haasova
- Sonja Kunz
- Christopher Eager
Citations in the corpus
- Parsimonious Mixed Models (2015, methodology): “In the statistical literature on fitting mixed-effects modeling (see, e.g. Pinheiro and Bates, 2000; Galecki and Burzykowski, 2013; Bates et al., 2014a), the approach taken is one in which variance components are added to the model step by step, typically driven by theoretical considerations.”
- Greetings promote cooperation and reinforce social bonds among spotted hyaenas (2011, methodology): “We built generalized linear mixed models (GLMM) using lme4 (Bates & Maechler 2010) in R v.2.6.2 (The R Foundation for Statistical Computing, Vienna, Austria).”
- Fifty shades of food: The influence of package color saturation on health and taste in consumer judgments (2020, methodology): “To test the influence of color on the healthiness–tastiness relationship, we used linear mixed‐effect model analysis (Gałecki & Burzykowski, 2013; Maxwell & Delaney, 2004).”