Generalized Linear Mixed Models were introduced in JMP Pro 17, where you  specify two distributions - Binomial and Poisson. 

GLMM  combines two approaches: the linear mixed model and generalized linear model frameworks . GLMM is useful for three types of model structures:

• Randomized complete and incomplete block designs
• Split-plot experiments
• Random coefficient models

See how to:

• Model mixed effects for count data using Poisson Regression and interpret results 
• Model mixed effects for grouped data using Binary Logistic Regression and interpret results 
• Model mixed effects for individual data using Binary Logistic Regression and interpret results 

Questions answered by Jian Cao @jiancao and Byron Wingerd @Byron_JMP  during the live webinar demo:

Q: How do you assess performance of GLMM?

A: Here are the full details.

Q: Would failure to include the random effects necessarily always lead to an increase in possibliity of overdispersion?

A:  The random effects, when included, make it less likely that you will over- or under-estimate.

Q: So since we have random effects why would we not always use the conditional prediction?

A: We usually want the marginal model profiler. The Conditional Model Profiler goes into some more detail on factors without doing a separate analysis.

Q: Is GLMM supported within MSA platform?

A:  The data generated from the MSA builder could be analyzed with GLMM, but it is not automatically integrated with the variability platform or MSA.

Q: Does the order of the Y’s matter? I’m guessing the numerator is the top number.

A: Yes.

Q: Are repeated measures designs specified the same way as in the mixed model platform?

A:  If you mean mixed model platform not GLMM, see https://www.jmp.com/support/help/en/17.1/index.shtml#page/jmp/statistical-details-for-repeated-measu....

Resources

• Documentation and examples of JMP  Pro Generalized Linear Mixed Models (GLMM)
• Developer Tutorial video on GLMM