https://github.com/pakillo/lm-glm-glmm-intro

A unified framework for data analysis with GLM/GLMM in R
https://github.com/pakillo/lm-glm-glmm-intro

glm glmm lm lme4 multilevel-models r slide statistics

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A unified framework for data analysis with GLM/GLMM in R

• Host: GitHub
• URL: https://github.com/pakillo/lm-glm-glmm-intro
• Owner: Pakillo
• Created: 2015-01-20T18:13:24.000Z (about 10 years ago)
• Default Branch: trees
• Last Pushed: 2025-01-13T23:26:08.000Z (19 days ago)
• Last Synced: 2025-01-28T10:44:49.545Z (5 days ago)
• Topics: glm, glmm, lm, lme4, multilevel-models, r, slide, statistics
• Language: R
• Homepage: http://pakillo.github.io/LM-GLM-GLMM-intro/
• Size: 109 MB
• Stars: 117
• Watchers: 7
• Forks: 29
• Open Issues: 28
• Metadata Files:
  • Readme: README.md

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README

        
## Linear, Generalized, and Mixed/Multilevel models with R

### Course philosophy

Introductory statistics are typically taught as a sequence of disconnected tests and protocols (e.g. t-test, ANOVA, ANCOVA, regression) while, in reality, all these analyses can be seen as special cases of a more general linear model. In this course, we will introduce Generalised Linear Models as a unified, coherent, and easily extendable framework for the analysis of many different types of data, including Normal (Gaussian), binary, and discrete (count) responses, and both categorical (factors) and continuous predictors.




![](images/flowchart.png)




### Slides (PDF)

- [Framework](framework.pdf)

- [Introduction to linear models](lm_intro.pdf)

- [Linear models](lm.pdf)

- [Variables and model selection](model_selection.pdf)

- [Model comparison](model_comparison_trees.pdf)

- [Generalised Linear Models for binary data](glm_binomial.pdf)

- [Generalised Linear Models for count data](glm_count.pdf)

- [Modelling zero-inflated count data](glm_count_zeroinfl.pdf)

- [Mixed effects / Multilevel models](mixed_models.pdf)

- [Generalised Additive Models (GAMs)](GAMs.pdf)

- [An introduction to Bayesian modelling](Bayes_intro.pdf)

- [Causal inference](causal-inference.pdf)

- [Regression to the mean](regression-to-the-mean.pdf)

### Interactive tutorials, R scripts, etc

https://pakillo.github.io/LM-GLM-GLMM-intro/

##### LICENSE

These materials are released with a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/). You can use/adapt them for **non-commercial purposes** as long as you mention the source (this repository) and share the materials with a similar license.

![](images/CClogo.png)

Francisco Rodriguez-Sanchez  

https://frodriguezsanchez.net