How to build linear or non linear models with constrained responses (sum of outputs = 1) in JMP?
I often need to model continuous response variables that represent proportions across categories, where the responses for each sample must sum to 1 (e.g., P1=10%, P2=25%, P3=35%, P4=30%).
In Python, I use a softmax output layer to enforce the sum-to-one constraint.
How can I build such models in JMP—either linear or neural network—while ensuring the sum of the predicted responses equals 1?Is there ...