Linear and generalized linear mixed models

By Benjamin M. Bolker. 2015

288 citations recorded by the source corpus.

Subfields: Genetics, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c3f2f410b3ca8af5a125c8a18eafa7b011b5b387

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Jacob C. Douma
2. J. Weedon
3. Casper W. Berg
4. Martin Mächler
5. Anders Nielsen
6. Hans Julius Skaug
7. Mollie E. Brooks
8. Koen J. van Benthem

In Nature and Landscape Conservation

1. Jacob C. Douma
2. J. Weedon
3. Anders Nielsen
4. Hans Julius Skaug
5. Kasper Kristensen
6. Casper W. Berg
7. Martin Mächler
8. Koen J. van Benthem

In Statistics and Probability

1. Casper W. Berg
2. Anders Nielsen
3. Benjamin M. Bolker
4. Jacob C. Douma
5. J. Weedon
6. Hans Julius Skaug
7. Kasper Kristensen
8. Mollie E. Brooks

Citations in the corpus

• Analysing continuous proportions in ecology and evolution: A practical introduction to beta and Dirichlet regression (2019, methodology): “Mixed effect models can account for non‐independence of observations and can be implemented as extensions to Generalized Linear Models (Bolker, 2015; Zuur et al., 2009) and have been developed for beta distributed variables as well (Brooks et al., 2017).”
• glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling (2017, methodology): “We assume that the reader already has a basic understanding of GLMs (Buckley, 2015), The R Journal Vol. 9/2, December 2017 ISSN 2073-4859 GLMMs (Bolker, 2015), and zero-altered models (Zeileis et al., 2008; Harrison, 2014)).”
• Repeated Measures Correlation (2017, background): “Estimating random or varying effects requires sufficient, but not excessive, variation, and typically five or more levels (Bolker, 2015).”
• Modeling zero-inflated count data with glmmTMB (2017, methodology): “…estimation and the Laplace approximation to integrate over random effects; unlike lme4, glmmTMB does not have the alternative options of doing restricted maximum likelihood (REML) estimation nor using Gauss-Hermite quadrature to integrate over random effects (Bolker et al., 2009; Bolker, 2015).”
• An Introduction to Mixed Models for Experimental Psychology (2019, unknown): “But at this point a brief description of the data is in order: First, 2 Note that there are di ff erent possibilities on how to define fixed and random e ff ects, ways that are not necessarily compatible with each other (Bolker, 2015; Gelman, 2005).”