Basic question: difference between standard least squares mixed model vs generalized linear mixed model

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)

• 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 too, I think it is very relevant. – usεr11852 Feb 24 '17 at 21:03