Interpretting Generalized model results on the original scale
Jul 7, 2019 10:13 PM(69 views)
Maybe this is a silly question but I'm new to generalized models. I have performed a beta generalized model on some proportion data that is naturally between 0 and 1. The model outputs parameter estimates that are significantly different between treatment groups as expected. The problem that I am having is that the parameter estimates are on a new scale that is not between 0 and 1. I am assuming this is due to some kind of transformation the program uses to run the model. I want to be able to interpret these results on the original scale. ie: The test mice consumed 95% of the feed treated with product A(Confidence interval [97%, 93%]) While the feed treated with product B only saw 20% consumption (Confidence interval [24%,16%]) Does anyone know an easy way of back transforming the estimates?
There is a lot to unpack there, and my suggestion is to not work through the formulas yourself because there's an easier way in jmp. My go-to method to profile the response in the original units as a function of the input variables is the Prediction Profiler. You can access this under the Red Triangle for your model (next to Maximum Likelihood) then select Profilers > Profiler. This will append the Prediction Profiler to the report, which lets you interactively profile (click through) the response Y as a function of your inputs. What is reported along the left is the mean response given the inputs you select and the 95% confidence interval around that point estimate.