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
allisonhorst.github.io/palmer...
Recommended Readings
peerj.com/articles/9522/
(Source of figure from thumbnail: DOI: 10.7717/peerj.9522/fig-1)
bookdown.org/steve_midway/DAR...
peerj.com/articles/4794/#
Code for this video:
github.com/chloefouilloux/Ran...
Пікірлер: 13
Absolutely amazing! thanks so much! Another request, what to do with data that has **a lot** of zeros? Please keep posting videos like this; you are indeed amazing at explaining what is going on and what we should be looking for! Thanks!
super helpfuil! Would love a video showing how to compare models (and esp mixed effect models) for best fit!
Very informative video with a refreshing amount of humour (which is rare to find in the world of statistics). Never had so much fun watchin this kind of videos, congrats haha! I would love to see more videos on GLMMs cause its cool but also complicated af
Can you do a video explaining what family of curve to choose? Here you have picked gaussian each time. I am struggling with this step in my own glmm's. Thanks for all your help so far, this was an interesting example!
Great explanation, you have a talent for explaining complex things well !
antarctic research student here! Super helpful and interesting video!!
The thumbnail tho!!!! LOL why did you have to make me feel old?! LOL!
I recognise that mixed effect model schematic on the thumbnail from Silk, Harrison & Hodgson 2020 PeerJ
@chloefouilloux
5 ай бұрын
Yes! Beautifully written by some great researchers ;) I linked the paper in my GitHub, but added it now to the video description as well!
Very specific but should you have expertise in setting up MaxEnt with SWD files to run several projections out of the same model...videos would be highly appreciated 🙃
Hi, thanks for the explanation of the GLMM analysis. I have a question: Do you applied DHARMA::simulateResiduals after created the last model? Thanks :D
No no no... I say to have at least 30 in each group.... If the data is sparse, it can cause problems...
@chloefouilloux
5 ай бұрын
I mean that is definitely better! But man oh man, try recapturing bank voles in the middle of an enormous forest over a summer. They are tiny little jerks!!!