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