Having Fun with Random Effects in Mixed Models (GLMMs)

Hiya!

We're back with coding. This is probably the most statistically challenging concept we've attacked yet, so tie up your shoelaces and let's venture out into the magical world of coding!

*Jump around the video if you can't be bothered to listen to my exquisite story-telling*
00:00 Introduction
00:13 Defining Random Effects
00:35 Random Effect Examples (and what makes a good one!)
01:28 Introduction to the Palmer Penguin Data
02:06 Introduction to glmmTMB
02:37 Setting up the model
03:06 *Model 1*, "Islands" random intercept
04:13 Variance vs. Standard Deviation
04:43 Random Effect Variance vs. Residual Effect Variance
05:34 Looking at level-specific random intercept estimates
06:22 WTF is your (Intercept)???
07:22 *Model 2*, "Species" random intercept
07:53 (Explained again, but better?) Random Effect Variance vs. Residual Effect Variance
09:05 *Model 3*, Nested Random Effects
10:56 *Model 4*, Multiple Predictors biologically "reasonable" model
11:24 Understanding (Intercept) for multiple predictors


**Links!**
Palmer Penguins
https://allisonhorst.github.io/palmerpenguins/

Recommended Readings
https://peerj.com/articles/9522/
(Source of figure from thumbnail: DOI: 10.7717/peerj.9522/fig-1)
https://bookdown.org/steve_midway/DAR/random-effects.html#introduction-3
https://peerj.com/articles/4794/#

Code for this video:
https://github.com/chloefouilloux/Random_Effects/tree/main