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 `hier.part` in `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][1], [Chevan & Sutherland (1991) - Hierarchical Partioning][2])

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?


  [1]: http://link.springer.com/article/10.1023/A:1016250716679
  [2]: https://www.jstor.org/stable/2684366?seq=1#page_scan_tab_contents