I am analysing data by means of multiple regression. My goal is to find out about the relative importance of the independent variables, using hierarchical partitioning (package
R). However, I assume that the interaction of some of the independent variables is important, too. Now I am wondering if it is valid to have interactions when doing hierarchical partitioning? I could not find anything in the literature (Mac Nally (2002) - Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables, Chevan & Sutherland (1991) - Hierarchical Partioning)
My model would look something like this: DV ~ IV1 + IV2 + IV1:IV2 + IV3 + IV4 + IV5
Result of hierarchical partioning with interaction:
I IV1 20.8255247 IV2 4.3218387 IV1:IV2 70.7574155 IV3 1.6795456 IV4 0.8780111 IV5 1.5376644
Result of hierarchical partioning without interaction:
I IV1 74.132474 IV2 14.690646 IV3 6.872467 IV4 1.311382 IV5 2.993031
It appears that interactions always have (much) greater values than their IVs have seperately. So is that a "true" effect and a sign to include the interaction in the model or should this be treated with caution?