I encountered one problem when I try to include an interaction factor in one model. JMP doesn't allow me to include the interaction effect without the main effect. It looks like the model must include both main effects if I want to have the interaction effect in the model, for example, JMP accept y=a+b+a*b, but not accept y=a+a*b
JMP insists you maintain hierarchy within the model, therefore you cannot have an interaction effect without the main effect being present. Once the model is produced with both effects present you may find that the main effect is small in comparison and could be excluded but its presence is required whilst building the model.
Therefore build your model maintaining hierarchy and check significance of each effect. If the model itself is insignificant, you could consider stepwise regression to eliminate highly insignificant terms.
Which version of JMP are you using? I tested it quickly in JMP 7 and JMP 8 and if you leave out a main effect that's used in a interaction JMP will notify you that the effect is missing but it does go ahead and fit the model.
Can you tell us exactly what's happening and what message you're receiving?
By including an interaction in the model without the main effect, you are saying that the missing main effect is exactly 0. To the extent that it is greater than 0, you are contaminating (confounding) the interaction with the missing main effect.