I think I understand what you want to do. It seems entirely reasonable, and also not so easy. I will at a minimum try and articulate in more detail what you want, with a specific example.
Step 1.
![David_Burnham_1-1658504478930.png David_Burnham_1-1658504478930.png](https://community.jmp.com/t5/image/serverpage/image-id/44238i9168AE58B7A62FEF/image-size/medium?v=v2&px=400)
I have my model, which for the special case of a single continuous variable, or one continuous and one discrete (as here) is shown in the Regression Plot outline.
In this example its reasonable apparent that variance increases with speed and this can be improved with a Log transformation to the response. This is confirmed by evaluating Box Cox transformations ( λ = - 0 is equivalent to a log transform).
![David_Burnham_2-1658504570303.png David_Burnham_2-1658504570303.png](https://community.jmp.com/t5/image/serverpage/image-id/44239i1F04DB5DE6837B95/image-size/medium?v=v2&px=400)
For convenience of illustration I will take the log transform rather than λ = -0.025.
Step 2
From the Box Cox transformation red triangle I can refit with a transform and specify the λ value.
The new model looks like this:
![David_Burnham_3-1658504795506.png David_Burnham_3-1658504795506.png](https://community.jmp.com/t5/image/serverpage/image-id/44240i1A64A11A2EEAF9B2/image-size/medium?v=v2&px=400)
It is linear with respect to the transformed response. It is clearer to understand the model by looking at the profiler, which shows the y-axis in the un-transformed units:
![David_Burnham_4-1658504864685.png David_Burnham_4-1658504864685.png](https://community.jmp.com/t5/image/serverpage/image-id/44241i820D26245819908D/image-size/medium?v=v2&px=400)
What I believe @Grumpybaldprof is asking for is the profiler curve on the regression plot. I can make this more explicit:
Step 3
This time I return to the Fit Model platform and specify a Log transformation to the response variable.
![David_Burnham_5-1658505043196.png David_Burnham_5-1658505043196.png](https://community.jmp.com/t5/image/serverpage/image-id/44242iABD307D92AFB684E/image-dimensions/223x62?v=v2)
Now I get the following Regression Plot :
![David_Burnham_6-1658505091568.png David_Burnham_6-1658505091568.png](https://community.jmp.com/t5/image/serverpage/image-id/44243i83BA87AF77BF3016/image-dimensions/422x239?v=v2)
This I think is what @Grumpybaldprof is looking for.
However, I'm only able to achieve this by applying the transformation in the Fit Model dialog and I am limited to the following power transformations:
![David_Burnham_7-1658505218265.png David_Burnham_7-1658505218265.png](https://community.jmp.com/t5/image/serverpage/image-id/44244i14D9DC31C26B1E40/image-dimensions/106x105?v=v2)
These correspond to λ values of 0 ,0.5 ,2 , -1 respectively whereas the Box Cox transformation produces a continuum of values.
So what to do:
- Ask JMP to support the box-cox λ parameter when transforming the response, so that the reverse transformation is automatically applied to graphs
- Take a sensible interpretation of the Box-Cox transformation (99% of the time I end up taking a log transformation; I would never use λ=-0.025 because I don't know what it means)
- Use graph builder to recreate the graph
Step 4
Using graph builder - well it's not the easiest thing to do, let alone describe so I will just give a screenshot to show it can be done
![David_Burnham_8-1658505627592.png David_Burnham_8-1658505627592.png](https://community.jmp.com/t5/image/serverpage/image-id/44245i1D1BAF6AC65EDF31/image-dimensions/340x343?v=v2)
-Dave