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I'm using jmp for my analysis, and I have data from what is ultimately a mixed model. The problem I have is that JMP doesn't have the ability to run GLMM. You can, however, run a mixed model with a standard least squares personality.

My question is: what am I missing out on by not having the GLMM functionality? What is the difference between what I've done (SLSMM) and a GLMM?

Clarification: response variable is continuous (%growth)

Nathan Haag
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  • Can you please clarify the nature of your response variable? – usεr11852 Feb 24 '17 at 19:40
  • @usεr11852 The response variable is continuous (%growth) – Nathan Haag Feb 24 '17 at 20:04
  • Is it bounded in $[0,1]$ or are there instances where you can have say $100\%+$ or negative growth? – usεr11852 Feb 24 '17 at 20:48
  • @usεr11852 I have it in percentages and it ranges from about -26% to 80% – Nathan Haag Feb 24 '17 at 20:51
  • Hmm... OK. Yes, you are good with treating as a Gaussian (ie. through as LMM). In general if you had relatively small changes (say up to 25%) you could squint and say that that percentage chance can be approximated by change in natural logarithms. (You do not have small changes in the sample you describe.) See [this threa](http://stats.stackexchange.com/questions/136749) too, I think it is very relevant. – usεr11852 Feb 24 '17 at 21:03

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