Workshop exercises on regression, GLMs, mixed-effects models, and GLMMs in R
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data Add 2 new lessons: chopsticks and delta-gamma Feb 2, 2018
.gitignore Fix knitting Feb 2, 2018
00-install.R Copy edits + group centering Nov 18, 2017
00-schedule.Rmd Fix knitting Feb 2, 2018
00-vocabulary.Rmd Add to vocabulary Oct 27, 2016
01-regression.Rmd Last minute tweaks Nov 26, 2016
02-ggplot.Rmd Renumber challenges, change aes Oct 29, 2016
03-dplyr.Rmd Tidy up Oct 29, 2016
04-tidyr.Rmd Re-order lessons Oct 25, 2016
05-random-intercepts.Rmd Copyditing Feb 3, 2018
06-partial-pooling-simulation.Rmd Rename exercise Oct 29, 2016
06-random-slopes.Rmd Copyditing Feb 3, 2018
07-grouping-structures.Rmd Fix typo Feb 3, 2018
08-temporal-autocorrelation.Rmd Move the prediction confidence intervals Oct 28, 2016
09-spatial-autocorrelation.Rmd Fixes for render Feb 2, 2018
10-variance-structures.Rmd Unwrap Mar 3, 2017
11-distributions.Rmd poisson -> Poisson Oct 28, 2016
12-glms.Rmd Fix knitting Feb 2, 2018
13-overdispersed-glms.Rmd Fix knitting Feb 2, 2018
14-glm-practice-titanic.Rmd Fix knitting Feb 2, 2018
15-lmm-practice-gapminder.Rmd Copy editing Jan 26, 2018
15.5-chopsticks.Rmd Copy editing Feb 3, 2018
16-binomial-glmm.Rmd Fixes for render Feb 2, 2018
17-poisson-negbin-glmm.Rmd Fixes for render Feb 2, 2018
18-zero-inflation.Rmd Zeros Feb 3, 2018
18.5-delta-gamma.Rmd fill -> colour Feb 5, 2018 Copy edits + group centering Nov 18, 2017 Strip trailing whitespace Dec 6, 2016 Fix link Feb 2, 2018
99-confidence-intervals.Rmd Copy edits + group centering Nov 18, 2017
99-make-checking-data.Rmd Clean up Oct 30, 2016
99-make.R Fix path issue Feb 3, 2018
99-outline-smith.xlsx Update schedule Jan 15, 2017
99-outline.xlsx Tweaks Jan 15, 2017
99-predict_pop_ci.R Move the prediction confidence intervals Oct 28, 2016 Add instructions Oct 30, 2016 Add two-part description Aug 1, 2017
glmm-course.Rproj Initial commit Sep 13, 2016

Generalized Linear Mixed-Effects Modeling in R

This two-day workshop will focus on generalized linear mixed-effects models (GLMMs; hierarchical/multilevel models) using the R programming language. We will concentrate on practical elements of GLMMs such as choosing a modeling approach, the process of building up and understanding a model, model checking, and plotting and interpreting model output. We will focus mainly on linear mixed-effects models, but we will also cover generalized linear mixed-effect models, variance and correlation structures, and zero-inflated models.

By the end of the two-day workshop, you will be able to develop models using your own data and troubleshoot the main problems that arise in the process. You will also become familiar with a number of R packages that can fit GLMMs (e.g. lme4, nlme, glmmTMB) and R packages to help manipulate and plot your data and models (e.g. dplyr, ggplot2, broom).

Prior to taking this workshop, you should be reasonably comfortable with R and linear regression, and ideally have some experience with GLMs (e.g. logistic regression). Some background with dplyr and ggplot2 would be helpful.

Downloading these notes/exercises

Click "Clone or download", "Download ZIP".

Generating the exercises

Open the file glmm-course.Rproj by double-clicking on it. Run the following:


Then look in the folder exercises. Lines with # exercise will be left blank in this version.