Good evening and thank you in advance for your help!
For my masters project, I am attempting to run a nominal logistic regression to show that as the basal diameter of an invasive plant species increases, its likelihood to resprout also increases following treatment of an herbicide. The data certainly seems to show this and the prediction profiler (pictured) seems to support this, but the created graph seems to show the opposite (pictured).
A few questions:
1) Why are my data points all over the place? Shouldn't they be represented as either 1 or 0?
2) Why is response on a continuous scale on the y axis? Is this the predicted percentage? My response variable is shown on the right.
3) Why do the two appear to be opposite? Is there anything I can do?
I tried changing value ordering, but received the same results (opposite curve).
Good evening to you, too.
Please reference Chapter 11 "Logistic Regression Models" of the Fitting Linear Models book under the Help menu. JMP books in the Help menu are very helpful, and should assist with your confusion regarding responses and interpretation of graphs.
The fitted probabilities of a nominal regression model always sum to 1. Both graphs suggest that when the Basal Measure is low, the Resprout probability is lower; When the Basal Measure is high, the Resprout probability is higher.
Isn't that what you're trying to prove? The graphs (both of 'em) are consistent with that hypothesis.
Please follow Kevin's excellent advice and read the JMP documentation for the Logistic platform. In the meantime, I will answer your specific questions.
In case you are interested, we cover all forms of logistic regression and other methods suitable for categorical responses in our JMP Software: Analyzing Discrete Response course.