https://github.com/m-clark/mixed-models-with-r

Covers the basics of mixed models, mostly using @lme4
https://github.com/m-clark/mixed-models-with-r

brms covariance crossed-random-effects generalized-linear-mixed-models glmmtmb hierarchical-linear-models linear-mixed-models lme4 mixed-models multilevel-models nested-random-effects nlme r random-effects random-intercepts random-slopes variance-components

Last synced: about 1 month ago
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Covers the basics of mixed models, mostly using @lme4

• Host: GitHub
• URL: https://github.com/m-clark/mixed-models-with-r
• Owner: m-clark
• Created: 2017-04-24T13:05:21.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-01-30T21:42:05.000Z (almost 3 years ago)
• Last Synced: 2024-11-22T15:41:24.989Z (about 2 months ago)
• Topics: brms, covariance, crossed-random-effects, generalized-linear-mixed-models, glmmtmb, hierarchical-linear-models, linear-mixed-models, lme4, mixed-models, multilevel-models, nested-random-effects, nlme, r, random-effects, random-intercepts, random-slopes, variance-components
• Language: R
• Homepage: https://m-clark.github.io/mixed-models-with-R/
• Size: 58.8 MB
• Stars: 130
• Watchers: 8
• Forks: 41
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# mixed-models-with-R

This document provides an introduction to mixed models. It uses lme4 as the primary tool, but demonstrates others.  Topics include random intercept and slope models, discussion of crossed vs. nested random effects, some common extensions (e.g. generalized linear mixed models), Bayesian tools, and other models that deal with dependency in the data.

[Link to doc](http://m-clark.github.io/mixed-models-with-R/).