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Practice JMP using these webinar videos and resources. We hold live Mastering JMP Zoom webinars with Q&A most Fridays at 2 pm US Eastern Time. See the list and register. Local-language live Zoom webinars occur in the UK, Western Europe and Asia. See your country jmp.com/mastering site.

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Modeling Mixed Effects for Binary and Count Response Data using Generalized Linear Mixed Models (GLMM)
Created: Jul 14, 2023 05:03 PM  |  Last Modified: Oct 25, 2023 02:47 PM
Ex 3. Poisson GLMM.jmp
Ex2. Mixed effects Logistic - Individual Data.jmp
Ex1. Mixed effects Logistic - Grouped Data.jmp
Mastering GLMM 07.14.2023 SHARE.jrn

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....

 

 

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