In this JMP Academic Webinar, we cover Generalized Linear Mixed Models in five parts. This is the fifth part of the series, covering a proportion example with a binomial distribution.
GLMM Part 1: Intro, Experiment, and lots about Mixed Models (24:09)
Welcome, and reminder of LM, GLM, MM, and GLMM: 0:00
Agenda: 2:32
Review of random effects and mixed models: 3:37
Key difference between a fixed effect and a random effect 7:06
Summary of Experiment: 9:22
Showing the personalities in Fit Model 12:12
Using SLS and Mixed personalities and seeing the same model but some different output options14:02
Exploring the interaction and the overlay plot for the mixed model 17:54
Understanding LSMeans 19:19
GLMM Part 2: More about GLMMs (7:04)
Count data (a Poisson distribution) 0:00
Details about and examples of GLMMs 2:35
Model + Distribution + Link 4:40
Details about REPL estimation
GLMM Part 3: Download and install the Add-In and find more examples! (4:18)
Webpage to download add-in and find more examples 0:00
Downloading and installing the add-in 1:54
Credit to the Add-In author, Meichen Dong 3:32
GLMM Part 4: Count example with Poisson distribution and LOTS of Graphing tips (27:40)
Setting up the Poisson Mixed Model 0:00
Back-transforming Estimates and CIs 2:52
Graph Builder for the Interaction Plot (with lots of JMP tips!!) 10:05
Saving Figures and Output and Data 17:17
Overdispersion 18:20
Back-transforming pairwise comparisons 25:38
Where to find more examples and ask questions 27:08
GLMM Part 5: Proportion example with Binomial Distribution (9:17)
Introducing the Binomial Scenario 0:00
Fitting the binomial GLMM 1:39
Back-transforming Estimates and CIs 4:00
Interaction Plot 6:46
Where to find more examples and ask questions 8:39